How to Streamline Customer Support Integrations

Introduction

Seamless CRM and ticketing system integrations are critical for modern customer support software. However, developing and maintaining these integrations in-house is time-consuming and resource-intensive.

In this article, we explore how Knit’s Unified API simplifies customer support integrations, enabling teams to connect with multiple platforms—HubSpot, Zendesk, Intercom, Freshdesk, and more—through a single API.

Why Efficient Integrations Matter for Customer Support

Customer support platforms depend on real-time data exchange with CRMs and ticketing systems. Without seamless integrations:

• Support agents struggle with disconnected systems, slowing response times.
• Customers experience delays, leading to poor service experiences.
• Engineering teams spend valuable resources on custom API integrations instead of product innovation.

A unified API solution eliminates these issues, accelerating integration processes and reducing ongoing maintenance burdens.

Challenges of Building Customer Support Integrations In-House

Developing custom integrations comes with key challenges:

• Long Development Timelines – Every CRM or ticketing tool has unique API requirements, leading to weeks of work per integration.
• Authentication Complexities – OAuth-based authentication requires security measures that add to engineering overhead.
• Data Structure Variations – Different platforms organize data differently, making normalization difficult.
• Ongoing Maintenance – APIs frequently update, requiring continuous monitoring and fixes.
• Scalability Issues – Scaling across multiple platforms means repeating the integration process for each new tool.

Use Case: Automating Video Ticketing for Customer Support

For example a company offering video-assisted customer support where users can record and send videos along with support tickets. Their integration requirements include:

1. Creating a Video Ticket – Associating video files with support requests.
2. Fetching Ticket Data – Automatically retrieving ticket and customer details from Zendesk, Intercom, or HubSpot.
3. Attaching Video Links to Support Conversations – Embedding video URLs into CRM ticket histories.
4. Syncing Customer Data – Keeping user information updated across integrated platforms.

With Knit’s Unified API, these steps become significantly simpler.

How Knit’s Unified API Simplifies Customer Support Integrations

By leveraging Knit’s single API interface, companies can automate workflows and reduce development time. Here’s how:

1. User Records a Video → System captures the ticket/conversation ID.
2. Retrieve Ticket Details → Fetch customer and ticket data via Knit’s API.
3. Attach the Video Link → Use Knit’s API to append the video link as a comment on the ticket.
4. Sync Customer Data → Auto-update customer records across multiple platforms.

Knit’s Ticketing API Suite for Developers

Knit provides pre-built ticketing APIs to simplify integration with customer support systems:

• Tickets API – Retrieve, update, and manage support tickets.
• Comments API – Add and fetch ticket comments.
• Attachments API – Manage attachments, including videos.
• Contacts API – Sync customer profiles.
• Users API – Manage customer identities.
• Groups API – Assign tickets to support teams.
• Tags API – Categorize and track support tickets.
• Ticket Types API – Define different ticket structures.
• Accounts API – Manage customer accounts.

Best Practices for a Smooth Integration Experience

For a successful integration, follow these best practices:

• Utilize Knit’s Unified API – Avoid writing separate API logic for each platform.
• Leverage Pre-built Authentication Components – Simplify OAuth flows using Knit’s built-in UI.
• Implement Webhooks for Real-time Syncing – Automate updates instead of relying on manual API polling.
• Handle API Rate Limits Smartly – Use batch processing and pagination to optimize API usage.

Technical Considerations for Scalability

• Pass-through Queries – If Knit doesn’t support a specific endpoint, developers can pass through direct API calls.
• Optimized API Usage – Cache ticket and customer data to reduce frequent API calls.
• Custom Field Support – Knit allows easy mapping of CRM-specific data fields.

How to Get Started with Knit

1. Sign Up on Knit’s Developer Portal.
2. Integrate the Universal API to connect multiple CRMs and ticketing platforms.
3. Use Pre-built Authentication components for user authorization.
4. Deploy Webhooks for automated updates.
5. Monitor & Optimize integration performance.

Streamline your customer support integrations with Knit and focus on delivering a world-class support experience!

📞 Need expert advice? Book a consultation with our team. Find time here

‍Introduction

AI has revolutionized how recruitment platforms and talent acquisition teams operate. Businesses manage vast amounts of applicant data, multiple job postings, and streamlined candidate pipelines—all while ensuring seamless integration with Applicant Tracking Systems (ATS). For HR and recruiting SaaS / AI Agent providers, offering native ATS integrations is a crucial differentiator to attract and retain customers.

This guide explores the key considerations for integrating your SaaS solution with leading ATS platforms like Ashby, Lever, and Greenhouse. Learn about core use cases, common integration challenges, and best practices for building a scalable and efficient ATS integration strategy.

Why ATS Integration Matters

Integrating with ATS platforms offers significant benefits:

• Rising Customer Expectations – Businesses expect their recruiting software to integrate with preferred ATS platforms effortlessly.
• Enhanced User Experience – Automating data sync between systems eliminates redundant data entry and reduces errors.
• Scalability & Efficiency – High-volume recruiting teams need automation to manage candidates with minimal manual effort.
• Competitive Edge – A seamless ATS integration can be a decisive factor for prospects choosing your platform over competitors.

Challenges in ATS Integration

Despite its advantages, ATS integration comes with challenges:

• Diverse ATS APIs – Each ATS platform has different APIs, documentation formats, and authentication protocols, increasing complexity.
• Limited Development Resources – Building and maintaining multiple integrations requires ongoing engineering investment.
• Rate Limits & Performance Issues – ATS APIs enforce strict rate limits, requiring robust error handling and retry mechanisms.
• Data Mapping & Field Alignment – Candidate and job record fields vary across ATS platforms, making standardization a challenge.
• Security & Compliance – Handling sensitive candidate data demands strict adherence to security protocols (e.g., GDPR, CCPA).

Key Use Cases for ATS Integration

A well-integrated recruiting platform supports -

1. Job Posting Sync – Displaying open job requisitions within your platform.
2. Candidate Data Push – Sending candidate profiles, resumes, and application details to the ATS.
3. Application Status Sync – Keeping candidate statuses, notes, and contact information updated across platforms.

Automation enables recruiting teams to manage hiring processes seamlessly from a single dashboard.

ATS Integration Workflow

A structured integration follows these key steps:

1. User Authentication – Users log in and authorize ATS access via OAuth or API key.
2. Fetch Job Listings – Your system syncs job openings from the ATS using APIs or webhooks.
3. Display Job Listings – Users view job openings and associate candidates within your platform.
4. Push Candidate Profiles – Selected candidates’ details (name, email, resume, etc.) are sent to the ATS.
5. Bi-Directional Sync – Status updates and new candidate information sync in real time.

Best Practices for ATS Integration

• Start with Core Features – Focus on job sync and candidate push before expanding to advanced features.
• Centralize Field Mapping – Use a structured data layer to manage field consistency across ATS platforms.
• Implement Robust Error Handling – Manage API rate limits with exponential backoff and scheduled retries.
• Utilize Sandbox Environments – Test integrations in a controlled setting before production rollout.
• Monitor & Log API Calls – Maintain real-time tracking of successful syncs, failures, and API health.

Technical Considerations

• Authentication & Token Management – Store API tokens securely and refresh OAuth credentials as required.
• Webhooks vs. Polling – Choose between real-time webhook triggers or scheduled API polling based on ATS capabilities.
• Scalability & Rate Limits – Implement request throttling and background job queues to avoid hitting API limits.
• Data Security – Encrypt candidate data in transit and at rest while maintaining compliance with privacy regulations.

ATS Integration Architecture Overview

┌────────────────────┐       ┌────────────────────┐
│ Recruiting SaaS    │       │ ATS Platform       │
│ - Candidate Mgmt   │       │ - Job Listings     │
│ - UI for Jobs      │       │ - Application Data │
└────────┬───────────┘       └─────────┬──────────┘
        │ 1. Fetch Jobs/Sync Apps     │
        │ 2. Display Jobs in UI       │
        ▼ 3. Push Candidate Data      │
┌─────────────────────┐       ┌─────────────────────┐
│ Integration Layer   │ ----->│ ATS API (OAuth/Auth)│
│ (Unified API / Knit)│       └─────────────────────┘
└─────────────────────┘


Actionable Next Steps

• Assess Integration Needs – Identify key ATS platforms your customers use.
• Define Critical Data Fields – Map out job and candidate fields required for smooth integration.
• Pilot an MVP Integration – Start with a single ATS (e.g., Ashby) before expanding.
• Monitor API Performance – Set up error alerts and tracking for integration stability.

Conclusion

Seamless ATS integrations are essential for modern HR and recruiting SaaS platforms. By ensuring secure authentication, robust field mapping, and real-time data sync, you can enhance user experience while reducing administrative workload.

If you're looking to simplify integrations, platforms like Knit offer pre-built connectivity to multiple ATS systems, allowing you to focus on core product innovation rather than managing complex API integrations.

Ready to optimize your ATS integrations? Talk to a solutions expert today

Introduction

In today's fast-evolving business landscape, companies are streamlining employee financial offerings, particularly in payroll-linked payments and leasing solutions. These include auto-leasing programs, payroll-based financing, and other benefits designed to enhance employee financial well-being.

By integrating directly with an organization’s Human Resources Information System (HRIS) and payroll systems, solution providers can offer a seamless experience that benefits both employers (B2B) and employees (B2C). This guide explores the importance of payroll integration, challenges businesses face, and best practices for implementing scalable solutions, with insights drawn from the B2B auto-leasing sector.

Why Payroll Integrations Matter for Leasing and Financial Benefits

Payroll-linked leasing and financing offer key advantages for companies and employees:

• Seamless Employee Benefits – Employees gain access to tax savings, automated lease payments, and simplified financial management.
• Enhanced Compliance – Automated approval workflows ensure compliance with internal policies and external regulations.
• Reduced Administrative Burden – Automatic data synchronization eliminates manual processes for HR and finance teams.
• Improved Employee Experience – A frictionless process, such as automatic payroll deductions for lease payments, enhances job satisfaction and retention.

Common Challenges in Payroll Integration

Despite its advantages, integrating payroll-based solutions presents several challenges:

• Diverse HR/Payroll Systems – Companies use various HR platforms (e.g., Workday, Successfactors, Bamboo HR or in some cases custom/ bespoke solutions), making integration complex and costly.
• Data Security & Compliance – Employers must ensure sensitive payroll and employee data are securely managed to meet regulatory requirements.
• Legacy Infrastructure – Many enterprises rely on outdated, on-prem HR systems, complicating real-time data exchange.
• Approval Workflow Complexity – Ensuring HR, finance, and management approvals in a unified dashboard requires structured automation.

‍

Key Use Cases for Payroll Integration

Integrating payroll systems into leasing platforms enables:

• Employee Verification – Confirm employment status, salary, and tenure directly from HR databases.
• Automated Approvals – Centralized dashboards allow HR and finance teams to approve or reject leasing requests efficiently.
• Payroll-Linked Deductions – Automate lease or financing payments directly from employee payroll to prevent missed payments.
• Offboarding Triggers – Notify leasing providers of employee exits to handle settlements or lease transfers seamlessly.

‍

End-to-End Payroll Integration Workflow

A structured payroll integration process typically follows these steps:

1. Employee Requests Leasing Option – Employees select a lease program via a self-service portal.
2. HR System Verification – The system validates employment status, salary, and tenure in real-time.
3. Employer Approval – HR or finance teams review employee data and approve or reject requests.
4. Payroll Setup – Approved leases are linked to payroll for automated deductions.
5. Automated Monthly Deductions – Lease payments are deducted from payroll, ensuring financial consistency.
6. Offboarding & Final Settlements – If an employee exits, the system triggers any required final payments.

‍

Best Practices for Implementing Payroll Integration

To ensure a smooth and efficient integration, follow these best practices:

• Use a Unified API Layer – Instead of integrating separately with each HR system, employ a single API to streamline updates and approvals.
• Optimize Data Syncing – Transfer only necessary data (e.g., employee ID, salary) to minimize security risks and data load.
• Secure Financial Logic – Keep payroll deductions, financial calculations, and approval workflows within a secure, scalable microservice.
• Plan for Edge Cases – Adapt for employees with variable pay structures or unique deduction rules to maintain flexibility.

Key Technical Considerations

A robust payroll integration system must address:

• Data Security & Compliance – Ensure compliance with GDPR, SOC 2, ISO 27001, or local data protection regulations.
• Real-time vs. Batch Updates – Choose between real-time synchronization or scheduled batch processing based on data volume.
• Cloud vs. On-Prem Deployments – Consider hybrid approaches for enterprises running legacy on-prem HR systems.
• Authentication & Authorization – Implement secure authentication (e.g., SSO, OAuth2) for employer and employee access control.

Recommended Payroll Integration Architecture

A high-level architecture for payroll integration includes:

┌────────────────┐   ┌─────────────────┐
│ HR System      │   │ Payroll         │
│(Cloud/On-Prem) │ → │(Deduction Logic)│
└───────────────┘    └─────────────────┘
       │ (API/Connector)
       ▼
┌──────────────────────────────────────────┐
│ Unified API Layer                        │
│ (Manages employee data & payroll flow)   │
└──────────────────────────────────────────┘
       │ (Secure API Integration)
       ▼
┌───────────────────────────────────────────┐
│ Leasing/Finance Application Layer         │
│ (Approvals, User Portal, Compliance)      │
└───────────────────────────────────────────┘


A single API integration that connects various HR systems enables scalability and flexibility. Solutions like Knit offer pre-built integrations with 40+ HRMS and payroll systems, reducing complexity and development costs.

Actionable Next Steps

To implement payroll-integrated leasing successfully, follow these steps:

• Assess HR System Compatibility – Identify whether your target clients use cloud-based or on-prem HRMS.
• Define Data Synchronization Strategy – Determine if your solution requires real-time updates or periodic batch processing.
• Pilot with a Mid-Sized Client – Test a proof-of-concept integration with a client using a common HR system.
• Leverage Pre-Built API Solutions – Consider platforms like Knit for simplified connectivity to multiple HR and payroll systems.

Conclusion

Payroll-integrated leasing solutions provide significant advantages for employers and employees but require well-planned, secure integrations. By leveraging a unified API layer, automating approval workflows, and payroll deductions data, businesses can streamline operations while enhancing employee financial wellness.

For companies looking to reduce overhead and accelerate implementation, adopting a pre-built API solution can simplify payroll integration while allowing them to focus on their core leasing offerings. Now is the time to map out your integration strategy, define your data requirements, and build a scalable solution that transforms the employee leasing experience.

Ready to implement a seamless payroll-integrated leasing solution? Take the next step today by exploring unified API platforms and optimizing your HR-tech stack for maximum efficiency. To talk to our solutions experts at Knit you can reach out to us here

‍

In the world of APIs, it's not enough to implement security measures and then sit back, hoping everything stays safe. The digital landscape is dynamic, and threats are ever-evolving. 

Why do you need to monitor your APIs regularly

Real-time monitoring provides an extra layer of protection by actively watching API traffic for any anomalies or suspicious patterns.

For instance - 

• It can spot a sudden surge in requests from a single IP address, which could be a sign of a distributed denial-of-service (DDoS) attack. 
• It can also detect multiple failed login attempts in quick succession, indicating a potential brute-force attack. 

In both cases, real-time monitoring can trigger alerts or automated responses, helping you take immediate action to safeguard your API and data.

API Logging

Now, on similar lines, imagine having a detailed diary of every interaction and event within your home, from visitors to when and how they entered. Logging mechanisms in API security serve a similar purpose - they provide a detailed record of API activities, serving as a digital trail of events.

Logging is not just about compliance; it's about visibility and accountability. By implementing logging, you create a historical archive of who accessed your API, what they did, and when they did it. This not only helps you trace back and investigate incidents but also aids in understanding usage patterns and identifying potential vulnerabilities.

To ensure robust API security, your logging mechanisms should capture a wide range of information, including request and response data, user identities, IP addresses, timestamps, and error messages. This data can be invaluable for forensic analysis and incident response. 

API monitoring

Combining logging with real-time monitoring amplifies your security posture. When unusual or suspicious activities are detected in real-time, the corresponding log entries provide context and a historical perspective, making it easier to determine the extent and impact of a security breach.

Based on factors like performance monitoring, security, scalability, ease of use, and budget constraints, you can choose a suitable API monitoring and logging tool for your application.

Access Logs and Issues in one page

This is exactly what Knit does. Along with allowing you access to data from 50+ APIs with a single unified API, it also completely takes care of API logging and monitoring. 

It offers a detailed Logs and Issues page that gives you a one page historical overview of all your webhooks and integrated accounts. It includes a number of API calls and provides necessary filters to choose your criterion. This helps you to always stay on top of user data and effectively manage your APIs.

Ready to build?

Get your API keys to try these API monitoring best practices for real

If you are looking to unlock 40+ HRIS and ATS integrations with a single API key, check out Knit API. If not, keep reading

‍

Note: This is our master guide on API Pagination where we solve common developer queries in detail with common examples and code snippets. Feel free to visit the smaller guides linked later in this article on topics such as page size, error handling, pagination stability, caching strategies and more.

‍

In the modern application development and data integration world, APIs (Application Programming Interfaces) serve as the backbone for connecting various systems and enabling seamless data exchange. 

However, when working with APIs that return large datasets, efficient data retrieval becomes crucial for optimal performance and a smooth user experience. This is where API pagination comes into play.

In this article, we will discuss the best practices for implementing API pagination, ensuring that developers can handle large datasets effectively and deliver data in a manageable and efficient manner. (We have linked bite sized how-to guides on all API pagination FAQs you can think of in this article. Keep reading!)

But before we jump into the best practices, let’s go over what is API pagination and the standard pagination techniques used in the present day.

What is API Pagination

API pagination refers to a technique used in API design and development to retrieve large data sets in a structured and manageable manner. When an API endpoint returns a large amount of data, pagination allows the data to be divided into smaller, more manageable chunks or pages. 

Each page contains a limited number of records or entries. The API consumer or client can then request subsequent pages to retrieve additional data until the entire dataset has been retrieved.
Pagination typically involves the use of parameters, such as offset and limit or cursor-based tokens, to control the size and position of the data subset to be retrieved. 

These parameters determine the starting point and the number of records to include on each page.

Advantages of API Pagination

By implementing API pagination, developers as well as consumers can have the following advantages - 

1. Improved Performance

Retrieving and processing smaller chunks of data reduces the response time and improves the overall efficiency of API calls. It minimizes the load on servers, network bandwidth, and client-side applications.

2. Reduced Resource Usage 

Since pagination retrieves data in smaller subsets, it reduces the amount of memory, processing power, and bandwidth required on both the server and the client side. This efficient resource utilization can lead to cost savings and improved scalability.

3. Enhanced User Experience

Paginated APIs provide a better user experience by delivering data in manageable portions. Users can navigate through the data incrementally, accessing specific pages or requesting more data as needed. This approach enables smoother interactions, faster rendering of results, and easier navigation through large datasets.

4. Efficient Data Transfer

With pagination, only the necessary data is transferred over the network, reducing the amount of data transferred and improving network efficiency.

5. Scalability and Flexibility

Pagination allows APIs to handle large datasets without overwhelming system resources. It provides a scalable solution for working with ever-growing data volumes and enables efficient data retrieval across different use cases and devices.

6. Error Handling

With pagination, error handling becomes more manageable. If an error occurs during data retrieval, only the affected page needs to be reloaded or processed, rather than reloading the entire dataset. This helps isolate and address errors more effectively, ensuring smoother error recovery and system stability.

‍

Common examples of paginated APIs 

Some of the most common, practical examples of API pagination are: 

• Platforms like Twitter, Facebook, and Instagram often employ paginated APIs to retrieve posts, comments, or user profiles. 
• Online marketplaces such as Amazon, eBay, and Etsy utilize paginated APIs to retrieve product listings, search results, or user reviews.
• Banking or payment service providers often provide paginated APIs for retrieving transaction history, account statements, or customer data.
• Job search platforms like Indeed or LinkedIn Jobs offer paginated APIs for retrieving job listings based on various criteria such as location, industry, or keywords.

‍

API pagination techniques

There are several common API pagination techniques that developers employ to implement efficient data retrieval. Here are a few useful ones you must know:

1. Offset and limit pagination
2. Cursor-based pagination
3. Page-based pagination
4. Time-based pagination
5. Keyset pagination

Read: Common API Pagination Techniques to learn more about each technique

Best practices for API pagination

When implementing API pagination in Python, there are several best practices to follow. For example,  

1. Use a common naming convention for pagination parameters

Adopt a consistent naming convention for pagination parameters, such as "offset" and "limit" or "page" and "size." This makes it easier for API consumers to understand and use your pagination system.

2. Always include pagination metadata in API responses

Provide metadata in the API responses to convey additional information about the pagination. 

This can include the total number of records, the current page, the number of pages, and links to the next and previous pages. This metadata helps API consumers navigate through the paginated data more effectively.

For example, here’s how the response of a paginated API should look like -

        
{
 "data": [
   {
     "id": 1,
     "title": "Post 1",
     "content": "Lorem ipsum dolor sit amet.",
     "category": "Technology"
   },
   {
     "id": 2,
     "title": "Post 2",
     "content": "Praesent fermentum orci in ipsum.",
     "category": "Sports"
   },
   {
     "id": 3,
     "title": "Post 3",
     "content": "Vestibulum ante ipsum primis in faucibus.",
     "category": "Fashion"
   }
 ],
 "pagination": {
   "total_records": 100,
   "current_page": 1,
   "total_pages": 10,
   "next_page": 2,
   "prev_page": null
 }
}
        
    

3. Determine an appropriate page size

Select an optimal page size that balances the amount of data returned per page. 

A smaller page size reduces the response payload and improves performance, while a larger page size reduces the number of requests required.

Determining an appropriate page size for a paginated API involves considering various factors, such as the nature of the data, performance considerations, and user experience. 

Here are some guidelines to help you determine the optimal page size.

Read: How to determine the appropriate page size for a paginated API 

4. Implement sorting and filtering options

Provide sorting and filtering parameters to allow API consumers to specify the order and subset of data they require. This enhances flexibility and enables users to retrieve targeted results efficiently. Here's an example of how you can implement sorting and filtering options in a paginated API using Python:

        
# Dummy data
products = [
    {"id": 1, "name": "Product A", "price": 10.0, "category": "Electronics"},
    {"id": 2, "name": "Product B", "price": 20.0, "category": "Clothing"},
    {"id": 3, "name": "Product C", "price": 15.0, "category": "Electronics"},
    {"id": 4, "name": "Product D", "price": 5.0, "category": "Clothing"},
    # Add more products as needed
]


@app.route('/products', methods=['GET'])
def get_products():
    # Pagination parameters
    page = int(request.args.get('page', 1))
    per_page = int(request.args.get('per_page', 10))


    # Sorting options
    sort_by = request.args.get('sort_by', 'id')
    sort_order = request.args.get('sort_order', 'asc')


    # Filtering options
    category = request.args.get('category')
    min_price = float(request.args.get('min_price', 0))
    max_price = float(request.args.get('max_price', float('inf')))


    # Apply filters
    filtered_products = filter(lambda p: p['price'] >= min_price and p['price'] <= max_price, products)
    if category:
        filtered_products = filter(lambda p: p['category'] == category, filtered_products)


    # Apply sorting
    sorted_products = sorted(filtered_products, key=lambda p: p[sort_by], reverse=sort_order.lower() == 'desc')


    # Paginate the results
    start_index = (page - 1) * per_page
    end_index = start_index + per_page
    paginated_products = sorted_products[start_index:end_index]


    return jsonify(paginated_products)

        
    

5. Preserve pagination stability

Ensure that the pagination remains stable and consistent between requests. Newly added or deleted records should not affect the order or positioning of existing records during pagination. This ensures that users can navigate through the data without encountering unexpected changes.

Read: 5 ways to preserve API pagination stability

6. Handle edge cases and error conditions

Account for edge cases such as reaching the end of the dataset, handling invalid or out-of-range page requests, and gracefully handling errors. 

Provide informative error messages and proper HTTP status codes to guide API consumers in handling pagination-related issues.

Read: 7 ways to handle common errors and invalid requests in API pagination

7. Consider caching strategies

Implement caching mechanisms to store paginated data or metadata that does not frequently change. 

Caching can help improve performance by reducing the load on the server and reducing the response time for subsequent requests.

Here are some caching strategies you can consider: 

1. Page level caching

Cache the entire paginated response for each page. This means caching the data along with the pagination metadata. This strategy is suitable when the data is relatively static and doesn't change frequently.

2. Result set caching

Cache the result set of a specific query or combination of query parameters. This is useful when the same query parameters are frequently used, and the result set remains relatively stable for a certain period. You can cache the result set and serve it directly for subsequent requests with the same parameters.

3. Time-based caching

Set an expiration time for the cache based on the expected freshness of the data. For example, cache the paginated response for a certain duration, such as 5 minutes or 1 hour. Subsequent requests within the cache duration can be served directly from the cache without hitting the server.

4. Conditional caching

Use conditional caching mechanisms like HTTP ETag or Last-Modified headers. The server can respond with a 304 Not Modified status if the client's cached version is still valid. This reduces bandwidth consumption and improves response time when the data has not changed.

5. Reverse proxy caching

Implement a reverse proxy server like Nginx or Varnish in front of your API server to handle caching. 

Reverse proxies can cache the API responses and serve them directly without forwarding the request to the backend API server. 

This offloads the caching responsibility from the application server and improves performance.

Simplify API pagination 

In conclusion, implementing effective API pagination is essential for providing efficient and user-friendly access to large datasets. But it isn’t easy, especially when you are dealing with a large number of API integrations.

Using a unified API solution like Knit ensures that your API pagination requirements is handled without you requiring to do anything anything other than embedding Knit’s UI component on your end. 

Once you have integrated with Knit for a specific software category such as HRIS, ATS or CRM, it automatically connects you with all the APIs within that category and ensures that you are ready to sync data with your desired app. 

In this process, Knit also fully takes care of API authorization, authentication, pagination, rate limiting and day-to-day maintenance of the integrations so that you can focus on what’s truly important to you i.e. building your core product.

By incorporating these best practices into the design and implementation of paginated APIs, Knit creates highly performant, scalable, and user-friendly interfaces for accessing large datasets. This further helps you to empower your end users to efficiently navigate and retrieve the data they need, ultimately enhancing the overall API experience.

Sign up for free trial today or talk to our sales team

If you are looking to unlock 40+ HRIS and ATS integrations with a single API key, check out Knit API. If not, keep reading

‍

Note: This is a part of our series on API Pagination where we solve common developer queries in detail with common examples and code snippets. Please read the full guide here where we discuss page size, error handling, pagination stability, caching strategies and more.

‍

Ensure that the pagination remains stable and consistent between requests. Newly added or deleted records should not affect the order or positioning of existing records during pagination. This ensures that users can navigate through the data without encountering unexpected changes.

5 ways for pagination stability

To ensure that API pagination remains stable and consistent between requests, follow these guidelines:

1. Use a stable sorting mechanism

If you're implementing sorting in your pagination, ensure that the sorting mechanism remains stable. 

This means that when multiple records have the same value for the sorting field, their relative order should not change between requests. 

For example, if you sort by the "date" field, make sure that records with the same date always appear in the same order.

2. Avoid changing data order

Avoid making any changes to the order or positioning of records during pagination, unless explicitly requested by the API consumer. 

If new records are added or existing records are modified, they should not disrupt the pagination order or cause existing records to shift unexpectedly.

3. Use unique and immutable identifiers

It's good practice to use unique and immutable identifiers for the records being paginated. T

This ensures that even if the data changes, the identifiers remain constant, allowing consistent pagination. It can be a primary key or a unique identifier associated with each record.

4. Handle record deletions gracefully

If a record is deleted between paginated requests, it should not affect the pagination order or cause missing records. 

Ensure that the deletion of a record does not leave a gap in the pagination sequence.

For example, if record X is deleted, subsequent requests should not suddenly skip to record Y without any explanation.

5. Use deterministic pagination techniques

Employ pagination techniques that offer deterministic results. Techniques like cursor-based pagination or keyset pagination, where the pagination is based on specific attributes like timestamps or unique identifiers, provide stability and consistency between requests.

Also Read: 5 caching strategies to improve API pagination performance

5 Best Nango Alternatives for Streamlined API Integration

Are you in the market for Nango alternatives that can power your API integration solutions? In this article, we’ll explore five top platforms—Knit, Merge.dev, Apideck, Paragon, and Tray Embedded—and dive into their standout features, pros, and cons. Discover why Knit has become the go-to option for B2B SaaS integrations, helping companies simplify and secure their customer-facing data flows.

‍

TL;DR

‍
Nango is an open-source embedded integration platform that helps B2B SaaS companies quickly connect various applications via a single interface. Its streamlined setup and developer-friendly approach can accelerate time-to-market for customer-facing integrations. However, coverage is somewhat limited compared to broader unified API platforms—particularly those offering deeper category focus and event-driven architectures.

Nango also relies heavily on open source communities for adding new connectors which makes connector scaling less predictable fo complex or niche use cases.

Pros (Why Choose Nango):

• Straightforward Setup: Shortens integration development cycles with a simplified approach.
• Developer-Centric: Offers documentation and workflows that cater to engineering teams.
• Embedded Integration Model: Helps you provide native integrations directly within your product.

Cons (Challenges & Limitations):

• Limited Coverage Beyond Core Apps: May not support the full depth of specialized or industry-specific APIs.
• Standardized Data Models: With Nango you have to create your own standard data models which requires some learning curve and isn't as straightforward as prebuilt unified APIs like Knit or Merge
• Opaque Pricing: While Nango has a free to build and low initial pricing there is very limited support provided initially and if you need support you may have to take their enterprise plans

Now let’s look at a few Nango alternatives you can consider for scaling your B2B SaaS integrations, each with its own unique blend of coverage, security, and customization capabilities.

1. Knit

Overview
Knit is a unified API platform specifically tailored for B2B SaaS integrations. By consolidating multiple applications—ranging from CRM to HRIS, Recruitment, Communication, and Accounting—via a single API, Knit helps businesses reduce the complexity of API integration solutions while improving efficiency.

Key Features

• Bi-Directional Sync: Offers both reading and writing capabilities for continuous data flow.
• Secure - Event-Driven Architecture: Real-time, webhook-based updates ensure no end-user data is stored, boosting privacy and compliance.
• Developer-Friendly: Streamlined setup and comprehensive documentation shorten development cycles.

Pros

• Simplified Integration Process: Minimizes the need for multiple APIs, saving development time and maintenance costs.
• Enhanced Security: Event-driven design eliminates data-storage risks, reinforcing privacy measures.
• New integrations Support : Knit enables you to build your own APIs in minutes or builds new integrations in a couple of days to ensure you can scale with confidence

2. Merge.dev

Overview
Merge.dev delivers unified APIs for crucial categories like HR, payroll, accounting, CRM, and ticketing systems—making it a direct contender among top Nango alternatives.

Key Features

• Extensive Pre-Built Integrations: Quickly connect to a wide range of platforms.
• Unified Data Model: Ensures consistent and simplified data handling across multiple services.

Pros

• Time-Saving: Unified APIs cut down deployment time for new integrations.
• Simplified Maintenance: Standardized data models make updates easier to manage.

Cons

• Limited Customization: The one-size-fits-all data model may not accommodate every specialized requirement.
• Data Constraints: Large-scale data needs may exceed the platform’s current capacity.
• Pricing : Merge's platform fee  might be steep for mid sized businesses

3. Apideck

Overview
Apideck offers a suite of API integration solutions that give developers access to multiple services through a single integration layer. It’s well-suited for categories like HRIS and ATS.

Key Features

• Unified API Layer: Simplifies data exchange and management.
• Integration Marketplace: Quickly browse available integrations for faster adoption.

Pros

• Broad Coverage: A diverse range of APIs ensures flexibility in integration options.
• User-Friendly: Caters to both developers and non-developers, reducing the learning curve.

Cons

• Limited Depth in Categories: May lack the robust granularity needed for certain specialized use cases.

‍

4. Paragon

‍

Overview
Paragon is an embedded integration platform geared toward building and managing customer-facing integrations for SaaS businesses. It stands out with its visual workflow builder, enabling lower-code solutions.

Key Features

• Low-Code Workflow Builder: Drag-and-drop functionality speeds up integration creation.
• Pre-Built Connectors: Quickly access popular services without extensive coding.

Pros

• Accessibility: Allows team members of varying technical backgrounds to design workflows.
• Scalability: Flexible infrastructure accommodates growing businesses.

Cons

• May Not Support Complex Integrations: Highly specialized needs might require additional coding outside the low-code environment.

5. Tray Embedded

‍

Overview
Tray Embedded is another formidable competitor in the B2B SaaS integrations space. It leverages a visual workflow builder to enable embedded, native integrations that clients can use directly within their SaaS platforms.

Key Features

• Visual Workflow Editor: Allows for intuitive, drag-and-drop integration design.
• Extensive Connector Library: Facilitates quick setup across numerous third-party services.

Pros

• Flexibility: The visual editor and extensive connectors make it easy to tailor integrations to unique business requirements.
• Speed: Pre-built connectors and templates significantly reduce setup time.

Cons

• Complexity for Advanced Use Cases: Handling highly custom scenarios may require development beyond the platform’s built-in capabilities.

Conclusion: Why Knit Is a Leading Nango Alternative

When searching for Nango alternatives that offer a streamlined, secure, and B2B SaaS-focused integration experience, Knit stands out. Its unified API approach and event-driven architecture protect end-user data while accelerating the development process. For businesses seeking API integration solutions that minimize complexity, boost security, and enhance scalability, Knit is a compelling choice.

Interested in trying Knit? - Contact us for a personalized demo and see how Knit can simplify your B2B SaaS integrations

HRIS or Human Resources Information Systems have become commonplace for organizations to simplify the way they manage and use employee information. For most organizations, information stored and updated in the HRIS becomes the backbone for provisioning other applications and systems in use. HRIS enables companies to seamlessly onboard employees, set them up for success and even manage their payroll and other functions to create an exemplary employee experience.

However, integration of HRIS APIs with other applications under use is essential to facilitate workflow automation. Essentially, HRIS API integration can help businesses connect diverse applications with the HRIS to ensure seamless flow of information between the connected applications. HRIS API integrations can either be internal or customer-facing. In internal HRIS integrations, businesses connect their HRIS with other applications they use, like ATS, Payroll, etc. to automate the flow of information between the same. On the other hand, with customer-facing HRIS integrations, businesses can connect their application or product with the end customer’s HR applications for data exchange. 

This article seeks to serve as a comprehensive repository on HRIS API integration, covering the benefits, best practices, challenges and how to address them, use cases, data models, troubleshooting and security risks, among others. 

Benefits of HRIS API integration

Here are some of the top reasons why businesses need HRIS API integration, highlighting the benefits they bring along:

• Higher employee productivity: HRIS API integration ensures that all data exchange between HRIS and other applications is automated and doesn’t require any human intervention. This considerably reduces the time and effort spent on manually updating all platforms with HR related data. This ensures that employees are able to focus more on value add tasks, leading to increased productivity and an improved employee experience.

• Reduced errors: Manual data entry is prone to errors. For instance, if during payroll updation, the compensation of an employee is entered incorrectly and differently from HRIS data, the employee will receive incorrect compensation, leading to regulatory/ financial discrepancies and employee displeasure. 

• End customer satisfaction: This is specifically for customer-facing HRIS integrations. By facilitating integration with your end customer’s HRIS applications and your product, you can foster automated data sync between the applications, eliminating the need for the customer to manually give you access to the data needed. This considerably augments customer experience and satisfaction.

• Expanded customer base: The ability to offer integrations with associated applications like payroll, attendance, etc. is something that most HR professionals seek. Therefore, when an application offers integrations with a wide range of HRIS, the total addressable market or TAM, significantly increases, augmenting the overall reach and potential customers. 

HRIS API Data Models Explained

The different HRIS tools you use are bound to come with different data models or fields which will capture data for exchange between applications. It is important for HR professionals and those building and managing these integrations to understand these data models, especially to ensure normalization and transformation of data when it moves from one application to another. 

Employees/ Employee Profiles

This includes details of all employees whether full time or contractual, including first and last name, contact details, date of birth, email ID, etc. At the same time, it covers other details on demographics and employment history including status, start date, marital status, gender, etc. In case of a former employee, this field also captures termination date. 

Employee Contact Details

This includes personal details of the employee, including personal phone number, address, etc. which can be used to contact employees beyond work contact information. 

Employee Profile Picture

Employee profile picture object or data model captures the profile picture of the employees that can be used across employee records and purposes. 

Employment Type

The next data model in discussion focuses on the type or the nature of employment. An organization can hire full time employees, contractual workers, gig workers, volunteers, etc. This distinction in employment type helps differentiate between payroll specifications, taxation rules, benefits, etc. 

Location

Location object or data model refers to the geographical area for the employee. Here, both the work location as well as the residential or native/ home location of the employee is captured. This field captures address, country, zip code, etc. 

Leave Request

Leave request data model focuses on capturing all the time off or leave of absence entries made by the employee. It includes detailing the nature of leave, time period, status, reason, etc.

Leave Balance

Each employee, based on their nature of employment, is entitled to certain time off in a year. The leave balance object helps organizations keep a track of the remaining balance of leave of absence left with the employee. With this, organizations can ensure accurate payroll, benefits and compensation. 

Attendance 

This data model captures the attendance of employees, including fields like time in, time out, number of working hours, shift timing, status, break time, etc. 

Organizational Structure

Each organization has a hierarchical structure or layers which depict an employee’s position in the whole scheme of things. The organizational structure object helps understand an employee’s designation, department, manager (s), direct reportees, etc. 

Bank Details

This data model focuses on capturing the bank details of the employee, along with other financial details like a linked account for transfer of salary and other benefits that the employee is entitled to. In addition, it captures routing information like Swift Code, IFSC Code, Branch Code, etc. 

Dependents

Dependents object focuses on the family members of an employee or individuals who the employee has confirmed as dependents for purposes of insurance, family details, etc. This also includes details of employees’ dependents including their date of birth, relation to the employee, among others. 

KYC

This includes the background verification and other details about an employee with some identification proof and KYC (know your customer) documents. This is essential for companies to ensure their employees are well meaning citizens of the country meeting all compliances to work in that location. It captures details like Aadhar Number, PAN Number or unique identification number for the KYC document. 

Compensation

This data model captures all details related to compensation for an employee, including total compensation/ cost to company, compensation split, salary in hand, etc. It also includes details on fixed compensation, variable pay as well as stock options. Compensation object also captures the frequency of salary payment, pay period, etc. 

HRIS API Integration Best Practices for Developers

To help you leverage the benefits of HRIS API integrations, here are a few best practices that developers and teams that are managing integrations can adopt:

Prioritize which HRIS integrations are needed for efficient resource allocation

This is extremely important if you are building integrations in-house or wish to connect with HRIS APIs in a 1:1 model. Building each HRIS integration or connecting with each HR application in-house can take four weeks on an average, with an associated cost of ~$10K. Therefore, it is essential to prioritize which HRIS integrations are pivotal for the short term versus which ones can be pushed to a later period. If developers focus all their energy in building all HRIS integrations at once, it may lead to delays in other product features. 

Understand the HRIS API before integrating with it

Developers should spend sufficient time in researching and understanding each individual HRIS API they are integrating with, especially in a 1:1 case. For instance, REST vs SOAP APIs have different protocols and thus, must be navigated in different ways. Similarly, the API data model, URL and the way the HRIS API receives and sends data will be distinct across each application. Developers must understand the different URLs and API endpoints for staging and live environments, identify how the HRIS API reports errors and how to respond to them, the supported data formats (JSON/ XML), etc.  

Stay up to date with API versioning

As HRIS vendors add new features, functionalities and update the applications, the APIs keep changing. Thus, as a best practice, developers must support API versioning to ensure that any changes can be updated without impacting the integration workflow and compatibility. To ensure conducive API versioning, developers must regularly update to the latest version of the API to prevent any disruption when the old version is removed. Furthermore, developers should eliminate the reliance on or usage of deprecated features, endpoints or parameters and facilitate the use of fallbacks or system alter notifications for unprecedented changes. 

Set appropriate rate limits and review them regularly

When building and managing integrations in-house, developers must be conscious and cautious about rate limiting. Overstepping the rate limit can prevent API access, leading to integration workflow disruption. To facilitate this, developers should collaboratively work with the API provider to set realistic rate limits based on the actual usage. At the same time, it is important to constantly review rate limits against the usage and preemptively upgrade the same in case of anticipated exhaustion. Also, developers should consider scenarios and brainstorm with those who use the integration processes the maximum to identify ways to optimize API usage.

Document HR integration process for each HRIS

Documenting the integration process for each HRIS is extremely important. It ensures there is a clear record of everything about that integration in case a developer leaves the organization, fostering integration continuity and seamless error handling. Furthermore, it enhances the long-term maintainability of the HRIS API integration. A comprehensive document generally captures the needs and objectives of the integration, authentication methods, rate limits, API types and protocols, testing environments, safety net in case the API is discontinued, common troubleshooting errors and handling procedures, etc. At the same time this documentation should be stored in a centralized repository which is easily accessible. 

Test HRIS integrations across different scenarios

HRIS integration is only complete once it is tested across different settings and they continue to deliver consistent performance. Testing is also an ongoing process, because everytime there is an update in the API of the third-party application, testing is needed, and so is the case whenever there is an update in one’s own application. To facilitate robust testing, automation is the key. Additionally, developers can set up test pipelines and focus on monitoring and logging of issues. It is also important to check for backward compatibility, evaluate error handling implementation and boundary values and keep the tests updated. 

Guides to popular HRIS APIs

Each HRIS API in the market will have distinct documentation highlighting its endpoints, authentication methods, etc. To make HRIS API integration for developers simpler, we have created a repository of different HR application directories, detailing how to navigate integrations with them:

• Workday API Integration Guide
• ADP API Integration Guide
• greytHR API Guide
• Zoho People API Guide
• Ceridian Dayforce API Directory
• Oracle HCM API Directory
• Gusto API Directory
• Zenefits API Directory
• Freshteam API Directory
• Keka API Directory
• Greenhouse API Directory
• Darwinbox API Directory
• SuccessFactors API Directory
• Namely API Directory

Common HRIS API Integration Challenges 

While there are several benefits of HRIS API integration, the process is fraught with obstacles and challenges, including:

Diversity of HRIS API providers

Today, there are 1000s of HR applications in the market which organizations use. This leads to a huge diversity of HRIS API providers. Within the HRIS category, the API endpoints, type of API (REST vs SOAP), data models, syntax, authentication measures and standards, etc. can vary significantly. This poses a significant challenge for developers who have to individually study and understand each HRIS API before integration. At the same time, the diversity also contributes to making the integration process time consuming and resource intensive.

Lack of public APIs and robust documentation

The next challenge comes from the fact that not all HRIS APIs are publicly available. This means that these gated APIs require organizations to get into partnership agreements with them in order to access API key, documentation and other resources. Furthermore, the process of partnering is not always straightforward either. It ranges from background and security checks to lengthy negotiations, and at times come at a premium cost associated. At the same time, even when APIs are public, their documentation is often poor, incomplete and difficult to understand, adding another layer of complexity to building and maintaining HRIS API integrations. 

Difficulty in testing across environments

As mentioned in one of the sections above, testing is an integral part of HRIS API integration. However, it poses a significant challenge for many developers. On the one hand, not every API provider offers testing environments to build against, pushing developers to use real customer data. On the other hand, even if the testing environment is available, running integrations against the same, requires thorough understanding and a steep learning curve for SaaS product developers. Overall, testing becomes a major roadblock, slowing down the process of building and maintaining integrations. 

Maintaining data quality and standardization

When it comes to HRIS API integration, there are several data related challenges that developers face across the way. To begin with, different HR providers are likely to share the same information in different formats, fields and names. Furthermore, data may also not come in a simple format, forcing developers to collect and calculate the data to decipher some values out of it. Data quality adds another layer of challenges. SInce standardizing and transforming data into a unified format is difficult, ensuring its accuracy, timeliness, and consistency is a big obstacle for developers.  

Scaling HRIS integrations

Scaling HRIS API integrations can be a daunting task, especially when integrations have to be built 1:1, in-house. Since building each integration requires developers to understand the API documentation, decipher data complexities, create custom codes and manage authentication, the process is difficult to scale. While building a couple of integrations for internal use might be feasible, scaling customer-facing integrations leads to a high level of inefficient resource use and developer fatigue. 

Post integration maintenance

Keeping up with third-party APIs and integration maintenance is another challenge that developers face. To begin with as the API versions update and change, HRIS API integration must reflect those changes to ensure usability and compatibility. However API documentation seldom reflects these changes, making it a cumbersome task for developers to keep pace with the changes. And, the inability to update API versioning can lead to broken integrations, endpoints and consistency issues. Furthermore, monitoring and logging, necessary to monitor the health of integrations can be a big challenge, with an additional resource allocation towards checking logs and addressing errors promptly. Managing rate limiting and throttling are some of the other post integration maintenance challenges that developers tend to face. 

Building Your First HRIS Integration with Knit: Step-by-Step Guide

Building Your First E-Signature Integration with Knit

Knit provides a unified HRIS API that streamlines the integration of HRIS solutions. Instead of connecting directly with multiple HRIS APIs, Knit allows you to connect with top providers like Workday, Successfactors, BambooHr, and many others through a single integration.

Learn more about the benefits of using a unified API.

Getting started with Knit is simple. In just 5 steps, you can embed multiple HRIS integrations into your APP.

Steps Overview:

1. Create a Knit Account: Sign up for Knit to get started with their unified API. You will be taken through a getting started flow.
2. Select Category: Select HRIS from the list of available option on the Knit dashboard
3. Register Webhook: Since one of the use cases of HRIS integrations is to sync data at frequent intervals, Knit supports scheduled data syncs for this category. Knit operates on a push based sync model, i.e. it reads data from the source system and pushes it to you over a webhook, so you don’t have to maintain a polling infrastructure at your end. In this step, Knit expects you to tell us the webhook over which it needs to push the source data.
4. Set up Knit UI to start integrating with APPs: In this step you get your API key and integrate with the HRIS APP of your choice from the frontend.
5. Fetch data and make API calls: That’s it! It’s time to start syncing data and making API calls and take advantage of Knit unified APIs and its data models. 

For detailed integration steps with the unified HRIS APIt, visit:

• Getting started with Knit

Security Considerations for HRIS API Integrations

Security happens to be one of the main tenets of HRIS API integration, determining its success and effectiveness. As HRIS API integration facilitates transmission, exchange and storage of sensitive employee data and related information, security is of utmost importance. 

‍

HRIS API endpoints are highly vulnerable to unauthorized access attempts. The lack of robust security protocols, these vulnerabilities can be exploited and attackers can gain access to sensitive HR information. On the one hand, this can lead to data breaches and public exposure of confidential employee data. On the other hand, it can disrupt the existing systems and create havoc. Here are the top security considerations and best practices to keep in mind for HRIS API integration. 

Broken authentication tokens and unauthorized access

Authentication is the first step to ensure HRIS API security. It seeks to verify or validate the identity of a user who is trying to gain access to an API, and ensures that the one requesting the access is who they claim to be. The top authentication protocols include:

‍

• OAuth: It is commonly used to grant third-party applications limited access to user data from other services without exposing user credentials with the third party. It uses access tokens, which are temporary and short lived. 
• Bearer tokens: They are stateless, time-bound access tokens which are simple for one time use, but need to be protected as anyone with access to them can access the API. 
• API keys: Facilitating server-to-server communication, these long-lived secret keys are ideal for trusted parties or internal use. 
• JSON Web Tokens: A token-based authentication method with a self-contained nature, facilitates scalable and secure access. 
• Basic Auth: Involves sending a username and password in the API request header in the form of Base64-encoded credentials.

‍

Most authentication methods rely on API tokens. However, when they are not securely generated, stored, or transmitted, they become vulnerable to attacks. Broken authentication can grant access to attackers, which can cause session hijacking, giving the attackers complete control over the API session. Hence, securing API tokens and authentication protocols is imperative. Practices like limiting the lifespan of your tokens/API keys, via time-based or event-based expiration as well as securing credentials in secret vault services can. 

Data exposure during transmission

As mentioned, HRIS API integration involves transmission and exchange of sensitive and confidential employee information. However, if the data is not encrypted during transmission it is vulnerable to attacker interception. This can happen when APIs use insecure protocols (HTTP instead of HTTPS), data is transmitted as plain text without encryption, there is insufficient data masking and validation. 

‍

To facilitate secure data transmission, it is important to use HTTPS, which uses Transport Layer Security (TLS) or its predecessor, Secure Sockets Layer (SSL), to encrypt data and can only be decrypted when it reaches the intended recipient. 

Input validation failure

Input validation failures can increase the incidence of injection attacks in HRIS API integrations. These attacks, primarily SQL injection and cross-site scripting (XSS), manipulate input data or untrusted data is injected into the database queries. This enables attackers to execute unauthorized database operations, potentially accessing or modifying sensitive information.

‍

Practices like input validation, output encoding, and the principle of least privilege, can help safeguard against injection vulnerabilities. Similarly, for database queries, using parameterized statements instead of injecting user inputs directly into SQL queries, can help mitigate the threat. 

Denial of service attacks and rate limiting

HRIS APIs are extremely vulnerable to denial of service (DoS) attacks where attackers flood your systems with excessive requests which it is not able to process, leading to disruption and temporarily restricts its functionality. Human errors, misconfigurations or even compromised third party applications can lead to this particular security challenge. 

‍

Rate limiting and throttling are effective measures that help prevent the incidence of DoS attacks, protecting APIs against excessive or abusive use and facilitating equitable request distribution between customers. While rate limiting restricts the number of requests or API calls that can be made in a specified time period, throttling slows down the processing of requests, instead of restricting them. Together, these act as robust measures to prevent excessive use attacks by perpetrators, and even protects against brute-force attacks. 

Third party security risks and ongoing threats

Third party security concerns i.e. how secure or vulnerable the third-party applications which you are integrating with, have a direct impact on the security posture of your HRIS API integration. Furthermore, threats and vulnerabilities come in without any prompt, making them unwanted guests. 

‍

To address the security concerns of third-party applications, it is important to thoroughly review the credibility and security posture of the software you integrate with. Furthermore, be cautious of the level of access you grant, sticking to the minimum requirement. It is equally important to monitor security updates and patch management along with a prepared contingency plan to mitigate the risk of security breaches and downtime in case the third-party application suffers a breach. 

‍

Furthermore, API monitoring and logging are critical security considerations for HRIS API integration. While monitoring involves continuous tracking of API traffic, logging entails maintaining detailed historical records of all API interactions. Together they are invaluable for troubleshooting, debugging, fostering trigger alerts in case security thresholds have been breached. In addition, regular security audits and penetration testing are extremely important. While security audits ensure the review of an API's design, architecture, and implementation to identify security weaknesses, misconfigurations, and best practice violations, penetration testing simulates cyberattacks to identify vulnerabilities, weaknesses, and potential entry points that malicious actors could exploit. These practices help mitigate ongoing security threats and facilitate API trustworthiness. 

Security with Knit’s HRIS API

When dealing with a large number of HRIS API integrations, security considerations and challenges increase exponentially. In such a situation, a unified API like Knit can help address all concerns effectively. Knit’s HRIS API ensures safe and high quality data access by:

‍

• Complying with industry best practices and security standards with SOC2, GDPR and ISO27001 certifications. 
• Monitoring Knit's infrastructure continuously with the finest intrusion detection systems. 
• Being the only unified API in the market that does not store any of your end user’s data in its servers.
• Encrypting all data doubly, when in transit and when at rest.
• Facilitating an additional layer of application security for encrypting PII and user credentials.
• Using a detailed Logs, Issues, Integrated Accounts and Syncs page to monitor and manage all integrations and keep track of every API request, call or data sync. 

HRIS API Use Cases: Real-World Examples

Here’s a quick snapshot of how HRIS integration can be used across different scenarios.

HRIS integration for ATS tools

ATS or applicant tracking system can leverage HRIS integration to ensure that all important and relevant details about new employees, including name, contact information, demographic and educational backgrounds, etc. are automatically updated into the customer’s preferred HRIS tool without the need to manually entering data, which can lead to inaccuracies and is operationally taxing. ATS tools leverage the write HRIS API and provide data to the HR tools in use.   

‍

Examples: Greenhouse Software, Workable, BambooHR, Lever, Zoho

HRIS integration for payroll software

Payroll software plays an integral role in any company’s HR processes. It focuses on ensuring that everything related to payroll and compensation for employees is accurate and up to date. HRIS integration with payroll software enables the latter to get automated and real time access to employee data including time off, work schedule, shifts undertaken, payments made on behalf of the company, etc. 

‍

At the same time, it gets access to employee data on bank details, tax slabs, etc. Together, this enables the payroll software to deliver accurate payslips to its customers, regarding the latter’s employees. With automated integration, data sync can be prone to errors, which can lead to faulty compensation disbursal and many compliance challenges. HRIS integration, when done right, can alert the payroll software with any new addition to the employee database in real time to ensure setting up of their payroll immediately. At the same time, once payslips are made and salaries are disbursed, payroll software can leverage HRIS integration to write back this data into the HR software for records. 

‍

‍Examples: Gusto, RUN Powered by ADP, Paylocity, Rippling

HRIS integration for employee onboarding/ offboarding software

Employee onboarding software uses HRIS integration to ensure a smooth onboarding process, free of administrative challenges. Onboarding tools leverage the read HRIS APIs to get access to all the data for new employees to set up their accounts across different platforms, set up payroll, get access to bank details, benefits, etc.

‍

With HRIS integrations, employee onboarding software can provide their clients with automated onboarding support without the need to manually retrieve data for each new joiner to set up their systems and accounts. Furthermore, HRIS integration also ensures that when an employee leaves an organization, the update is automatically communicated to the onboarding software to push deprovisioning of the systems, and services. This also ensures that access to any tools, files, or any other confidential access is terminated. Manually deprovisioning access can lead to some manual errors, and even cause delays in exit formalities. 

‍

Examples: Deel, Savvy, Sappling

Ease of communication and announcements

With the right HRIS integration, HR teams can integrate all relevant data and send out communication and key announcements in a centralized manner. HRIS integrations ensure that the announcements reach all employees on the correct contact information without the need for HR teams to individually communicate the needful. 

HRIS integration for LMS tools

LMS tools leverage both the read and write HRIS APIs. On the one hand, they read or get access to all relevant employee data including roles, organizational structure, skills demand, competencies, etc. from the HRIS tool being used. Based on this data, they curate personalized learning and training modules for employees for effective upskilling. Once the training is administered, the LMS tools again leverage HRIS integrations to write data back into the HRIS platform with the status of the training, including whether or not the employee has completed the same, how did they perform, updating new certifications, etc. Such integration ensures that all learning modules align well with employee data and profiles, as well as all training are captured to enhance the employee’s portfolio. 

‍

Example: TalentLMS, 360Learning, Docebo, Google Classroom

HRIS integration for workforce management and scheduling tools 

Similar to LMS, workforce management and scheduling tools utilize both read and write HRIS APIs. The consolidated data and employee profile, detailing their competencies and training undertaken can help workforce management tools suggest the best delegation of work for companies, leading to resource optimization. On the other hand, scheduling tools can feed data automatically with HRIS integration into HR tools about the number of hours employees have worked, their time off, free bandwidth for allocation, shift schedules etc. HRIS integration can help easily sync employee work schedules and roster data to get a clear picture of each employee’s schedule and contribution. 

‍

Examples: QuickBooks Time, When I Work

HRIS integration for benefits administration tools

HRIS integration for benefits administration tools ensures that employees are provided with the benefits accurately, customized to their contribution and set parameters in the organization. Benefits administration tools can automatically connect with the employee data and records of their customers to understand the benefits they are eligible for based on the organizational structure, employment type, etc. They can read employee data to determine the benefits that employees are entitled to. Furthermore, based on employee data, they feed relevant information back into the HR software, which can further be leveraged by payroll software used by the customers to ensure accurate payslip creation. 

‍

‍Examples: TriNet Zenefits, Rippling, PeopleKeep, Ceridian Dayforce

HRIS integration for workforce planning tools

Workforce planning tools essentially help companies identify the gap in their talent pipeline to create strategic recruitment plans. They help understand the current capabilities to determine future hiring needs. HRIS integration with such tools can help automatically sync the current employee data, with a focus on organizational structure, key competencies, training offered, etc. Such insights can help workforce planning tools accurately manage talent demands for any organization. At the same time, real time sync with data from HR tools ensures that workforce planning can be updated in real time. 

‍

HRIS API Integration Error Handling

There are several reasons why HRIS API integrations fail, highlighting that there can be a variety of errors. Invariably, teams need to be equipped to efficiently handle any integration errors, ensuring error resolution in a timely manner, with minimal downtime. Here are a few points to facilitate effective HRIS API integration error handling. 

Understand the types of errors

Start with understanding the types of errors or response codes that come in return of an API call. Some of the common error codes include:

‍

• 404 Not Found: The requested resource doesn’t exist or isn’t available
• 429 Too Many Requests: API call request rate limit has been reached or exceeded
• 401 Unauthorized: Lack of authorization or privileges to access the particular resource
• 500 Internal Server Error: Issue found at the server’s end

‍

While these are some, there are other error codes which are common in nature and, thus, proactive resolution should be available. 

Configure the monitoring system to incorporate all error details

All errors are generally captured in the monitoring system the business uses for tracking issues. For effective HRIS API error handling, it is imperative that the monitoring system be configured in such a way that it not only captures the error code but also any other relevant details that may be displayed along with it. These can include a longer descriptive message detailing the error, a timestamp, suggestion to address the error, etc. Capturing these can help developers with troubleshooting the challenge and resolve the issues faster. 

Use exponential back-offs to increase API call intervals

This error handling technique is specifically beneficial for rate limit errors or whenever you exceed your request quota. Exponential backoffs allow users to retry specific API calls at an increasing interval to retrieve any missed information. The request may be retrieved in the subsequent window. This is helpful as it gives the system time to recover and reduces the number of failed requests due to rate limits and even saves the costs associated with these unnecessary API calls. 

Test, document and review error handling process

It is very important to test the error handling processes by running sandbox experiments and simulated environment testing. Ideally, all potential errors should be tested for, to ensure maximum efficiency. However, in case of time and resource constraints, the common errors mentioned above, including HTTP status code errors, like 404 Not Found, 401 Unauthorized, and 503 Service Unavailable, must be tested for. 

In addition to robust testing, every step of the error handling process must be documented. Documentation ensures that even in case of engineering turnover, your HRIS API integrations are not left to be poorly maintained with new teams unable to handle errors or taking longer than needed. At the same time, having comprehensive error handling documentation can make any knowledge transfer to new developers faster. Ensure that the documentation not only lists the common errors, but also details each step to address the issues with case studies and provides a contingency plan for immediate business continuity. 

‍

Furthermore, reviewing and refining the error handling process is imperative. As APIs undergo changes, it is normal for initial error handling processes to fail and not perform as expected. Therefore, error handling processes must be consistently reviewed and upgraded to ensure relevance and performance. 

API error handling with Knit

Knit’s HRIS API simplifies the error handling process to a great extent. As a unified API, it helps businesses automatically detect and resolve HRIS API integration issues or provide the customer-facing teams with quick resolutions. Businesses do not have to allocate resources and time to identify issues and then figure out remedial steps. For instance, Knit’s retry and delay mechanisms take care of any API errors arising due to rate limits. 

TL:DR

It is evident that HRIS API integration is no longer a good to have, but an imperative for businesses to manage all employee related operations. Be it integrating HRIS and other applications internally or offering customer facing integrations, there are several benefits that HRIS API integration brings along, ranging from reduced human error to greater productivity, customer satisfaction, etc. When it comes to offering customer-facing integrations, ATS, payroll, employee onboarding/ offboarding, LMS tools are a few among the many providers that see value with real world use cases. 

‍

However, HRIS API integration is fraught with challenges due to the diversity of HR providers and the different protocols, syntax, authentication models, etc. they use. Scalining integrations, testing across different environments, security considerations, data normalization, all create multidimensional challenges for businesses. Invariably, businesses are now going the unified API way to build and manage their HRIS API integration. Knit’s unified HRIS API ensures:

‍

• One unified API to connect with all HRIS tools you need
• Single unified data model for seamless data normalization and exchange
• Compliance to the highest security standards like SOC2, GDPR, ISO27001, HIPAA
• Option to authenticate the way you want, including, OAuth, API key or a username-password based authentication
• 100% webhooks architecture that send out notification whenever updated data is available
• Guaranteed scalability and delivery of HR data irrespective of data load
• High level of security as Knit doesn’t store a copy of your data
• Option to read and write data from any app from any HRIS category
• Option to limit data sync and API calls to only what you need
• Double encryption when in transit and when at rest with addition PII layer
• Detailed Logs, Issues, Integrated Accounts and Syncs page to easily monitor HRIS integrations
• Custom fields to manage any non-standard HRIS data

‍

Knit’s HRIS API ensures a high ROI for companies with a single type of authentication, pagination, rate limiting, and automated issue detection making the HRIS API integration process simple.

‍

Top 5 Alternatives to tryfinch

‍

TL:DR:

Finch is a leading unified API player, particularly popular for its connectors in the employment systems space, enabling SaaS companies to build 1: many integrations with applications specific to employment operations. This translates to the ease for customers to easily leverage Finch’s unified connector to integrate with multiple applications in HRIS and payroll categories in one go. Invariably, owing to Finch, companies find connecting with their preferred employment applications (HRIS and payroll) seamless, cost-effective, time-efficient, and overall an optimized process. While Finch has the most exhaustive coverage for employment systems, it's not without its downsides - most prominent being the fact that a majority of the connectors offered are what Finch calls “assisted” integrations. Assisted essentially means a human-in-the-loop integration where a person has admin access to your user's data and is manually downloading and uploading the data as and when needed.

Pros and cons of Finch

Why chose Finch (Pros)

● Ability to scale HRIS and payroll integrations quickly

● In-depth data standardization and write-back capabilities

● Simplified onboarding experience within a few steps

However, some of the challenges include(Cons):

● Most integrations are human-assisted instead of being true API integrations

● Integrations only available for employment systems

● Limited flexibility for frontend auth component

● Requires users to take the onus for integration management

‍

Pricing: Starts at $35/connection per month for read only apis; Write APIs for employees, payroll and deductions are available on their scale plan for which you’d have to get in touch with their sales team.

Now let's look at a few alternatives you can consider alongside finch for scaling your integrations

Finch alternative #1: Knit

‍

Knit is a leading alternative to Finch, providing unified APIs across many integration categories, allowing companies to use a single connector to integrate with multiple applications. Here’s a list of features that make Knit a credible alternative to Finch to help you ship and scale your integration journey with its 1:many integration connector:

‍

Pricing: Starts at $2400 Annually

‍

Here’s when you should choose Knit over Finch:

‍

● Wide horizontal and deep vertical coverage: Knit not only provides a deep vertical coverage within the application categories it supports, like Finch, however, it also supports a wider horizontal coverage of applications, higher than that of Finch. In addition to applications within the employment systems category, Knit also supports a unified API for ATS, CRM, e-Signature, Accounting, Communication and more. This means that users can leverage Knit to connect with a wider ecosystem of SaaS applications.

‍

● Events-driven webhook architecture for data sync: Knit has built a 100% events-driven webhook architecture, which ensures data sync in real time. This cannot be accomplished using data sync approaches that require a polling infrastructure. Knit ensures that as soon as data updates happen, they are dispatched to the organization’s data servers, without the need to pull data periodically. In addition, Knit ensures guaranteed scalability and delivery, irrespective of the data load, offering a 99.99% SLA. Thus, it ensures security, scale and resilience for event driven stream processing, with near real time data delivery.

‍

● Data security: Knit is the only unified API provider in the market today that doesn’t store any copy of the customer data at its end. This has been accomplished by ensuring that all data requests that come are pass through in nature, and are not stored in Knit’s servers. This extends security and privacy to the next level, since no data is stored in Knit’s servers, the data is not vulnerable to unauthorized access to any third party. This makes convincing customers about the security potential of the application easier and faster.

‍

● Custom data models: While Knit provides a unified and standardized model for building and managing integrations, it comes with various customization capabilities as well. First, it supports custom data models. This ensures that users are able to map custom data fields, which may not be supported by unified data models. Users can access and map all data fields and manage them directly from the dashboard without writing a single line of code. These DIY dashboards for non-standard data fields can easily be managed by frontline CX teams and don’t require engineering expertise.  

‍

● Sync when needed: Knit allows users to limit data sync and API calls as per the need. Users can set filters to sync only targeted data which is needed, instead of syncing all updated data, saving network and storage costs. At the same time, they can control the sync frequency to start, pause or stop sync as per the need.

‍

● Ongoing integration management: Knit’s integration dashboard provides comprehensive capabilities. In addition to offering RCA and resolution, Knit plays a proactive role in identifying and fixing integration issues before a customer can report it. Knit ensures complete visibility into the integration activity, including the ability to identify which records were synced, ability to rerun syncs etc.

‍

As an alternative to Finch, Knit ensures:

● No-Human in the loop integrations

● No need for maintaining any additional polling infrastructure

● Real time data sync, irrespective of data load, with guaranteed scalability and delivery

● Complete visibility into integration activity and proactive issue identification and resolution

● No storage of customer data on Knit’s servers

● Custom data models, sync frequency, and auth component for greater flexibility

‍

Finch alternative #2: Merge

Another leading contender in the Finch alternative for API integration is Merge. One of the key reasons customers choose Merge over Finch is the diversity of integration categories it supports.

Pricing: Starts at $7800/ year and goes up to $55K

‍

Why you should consider Merge to ship SaaS integrations:

‍

● Higher number of unified API categories; Merge supports 7 unified API categories, whereas Finch only offers integrations for employment systems

● Supports API-based integrations and doesn’t focus only on assisted integrations (as is the case for Finch), as the latter can compromise customer’s PII data

● Facilitates data sync at a higher frequency as compared to Finch; Merge ensures daily if not hourly syncs, whereas Finch can take as much as 2 weeks for data sync

‍

However, you may want to consider the following gaps before choosing Merge:

‍

● Requires a polling infrastructure that the user needs to manage for data syncs

● Limited flexibility in case of auth component to customize customer frontend to make it similar to the overall application experience

● Webhooks based data sync doesn’t guarantee scale and data delivery

‍

Finch alternative #3: Workato

‍

Workato is considered another alternative to Finch, albeit in the traditional and embedded iPaaS category.

‍

Pricing: Pricing is available on request based on workspace requirement; Demo and free trial available

‍

Why you should consider Workato to ship SaaS integrations:

‍

● Supports 1200+ pre-built connectors, across CRM, HRIS, ticketing and machine learning models, facilitating companies to scale integrations extremely fast and in a resource efficient manner

● Helps build internal integrations, API endpoints and workflow applications, in addition to customer-facing integrations; co-pilot can help build workflow automation better

● Facilitates building interactive workflow automations with Slack, Microsoft Teams, with its customizable platform bot, Workbot

However, there are some points you should consider before going with Workato:

● Lacks an intuitive or robust tool to help identify, diagnose and resolve issues with customer-facing integrations themselves i.e., error tracing and remediation is difficult

● Doesn’t offer sandboxing for building and testing integrations

● Limited ability to handle large, complex enterprise integrations

‍

Finch alternative #4: Paragon

Paragon is another embedded iPaaS that companies have been using to power their integrations as an alternative to Finch.

Pricing: Pricing is available on request based on workspace requirement;

‍

Why you should consider Paragon to ship SaaS integrations:

‍

● Significant reduction in production time and resources required for building integrations, leading to faster time to market

● Fully managed authentication, set under full sets of penetration and testing to secure customers’ data and credentials; managed on-premise deployment to support strictest security requirements

● Provides a fully white-labeled and native-modal UI, in-app integration catalog and headless SDK to support custom UI

However, a few points need to be paid attention to, before making a final choice for Paragon:

● Requires technical knowledge and engineering involvement to custom-code solutions or custom logic to catch and debug errors

● Requires building one integration at a time, and requires engineering to build each integration, reducing the pace of integration, hindering scalability

● Limited UI/UI customization capabilities

‍

Finch alternative #5: Tray.io

‍

Tray.io provides integration and automation capabilities, in addition to being an embedded iPaaS to support API integration.

Pricing: Supports unlimited workflows and usage-based pricing across different tiers starting from 3 workspaces; pricing is based on the plan, usage and add-ons

‍

Why you should consider Tary.io to ship SaaS integrations:

‍

● Supports multiple pre-built integrations and automation templates for different use cases

● Helps build and manage API endpoints and support internal integration use cases in addition to product integrations

● Provides Merlin AI which is an autonomous agent to build automations via chat interface, without the need to write code

However, Tray.io has a few limitations that users need to be aware of:

● Difficult to scale at speed as it requires building one integration at a time and even requires technical expertise

● Data normalization capabilities are rather limited, with additional resources needed for data mapping and transformation

● Limited backend visibility with no access to third-party sandboxes

‍

TL:DR

We have talked about the different providers through which companies can build and ship API integrations, including, unified API, embedded iPaaS, etc. These are all credible alternatives to Finch with diverse strengths, suitable for different use cases. Undoubtedly, the number of integrations supported within employment systems by Finch is quite large, there are other gaps which these alternatives seek to bridge:

‍

● Knit: Providing unified apis for different categories, supporting both read and write use cases. A great alternative which doesn’t require a polling infrastructure for data sync (as it has a 100% webhooks based architecture), and also supports in-depth integration management with the ability to rerun syncs and track when records were synced.

● Merge: Provides a greater coverage for different integration categories and supports data sync at a higher frequency than Finch, but still requires maintaining a polling infrastructure and limited auth customization.

● Workato: Supports a rich catalog of pre-built connectors and can also be used for building and maintaining internal integrations. However, it lacks intuitive error tracing and remediation.

● Paragon: Fully managed authentication and fully white labeled UI, but requires technical knowledge and engineering involvement to write custom codes.

● Tray.io: Supports multiple pre-built integrations and automation templates and even helps in building and managing API endpoints. But, requires building one integration at a time with limited data normalization capabilities.

‍

Thus, consider the following while choosing a Finch alternative for your SaaS integrations:

● Support for both read and write use-cases

● Security both in terms of data storage and access to data to team members

● Pricing framework, i.e., if it supports usage-based, API call-based, user based, etc.

● Features needed and the speed and scope to scale (1:many and number of integrations supported)

Depending on your requirements, you can choose an alternative which offers a greater number of API categories, higher security measurements, data sync (almost in real time) and normalization, but with customization capabilities.

‍

‍

‍

‍

‍

‍

Introduction

SaaS (Software-as-a-Service) applications now account for over 70% of company software usage, and research shows the average organization runs more than 370 SaaS tools today. By 2025, 85% of all business applications will be SaaS-based, underscoring just how fast the market is growing.

However, using a large number of SaaS tools comes with a challenge: How do you make these applications seamlessly talk to each other so you can reduce manual workflows and errors? That’s where SaaS integration steps in.

In this article, we’ll break down everything from the basics of SaaS integration and its benefits to common use cases, best practices, and a look at the future of this essential connectivity.

1. What Is SaaS Integration?

SaaS integration is the process of connecting separate SaaS applications so they can share data, trigger each other’s workflows, and automate repetitive tasks. This connectivity can be:

• Internal (used for your own workflows among various tools like CRM, HRMS, payroll, etc.)
• Customer-facing (offered by a SaaS provider to help its customers seamlessly connect the SaaS product with whatever tools they already use)

At its core, SaaS integration often involves using APIs (Application Programming Interfaces) to ensure data can move between apps in real time. As companies add more and more SaaS tools, integration is no longer a luxury—it's a necessity for efficiency and scalability.

2. Why SaaS Integrations Matter

Below are some of the top reasons companies invest heavily in SaaS integrations:

• Eliminate Data Silos: Integrations unify data across multiple departments, so every team has the context they need—without duplicating effort.
• Increase Efficiency and Accuracy: By automating repetitive tasks and reducing manual data entry, businesses avoid costly errors.
• Enhance Decision Making: Real-time data flow enables better analytics and data-driven decisions.
• Improve Employee Experience: Automated workflows free employees from mundane, error-prone tasks so they can focus on impactful, creative work.
• Drive Customer Delight and Retention (for SaaS providers): Offering out-of-the-box integrations with popular apps positions your product as a one-stop solution—and customers stick around when things “just work.”

3. Popular SaaS Integration Use Cases

Here are a few real-world ways SaaS integrations can transform businesses:

1. Sync HRMS and Payroll
   • Automate employee onboarding data from your HRMS to your payroll system.
   • Eliminate manual re-entry of compensation, leaves, bonuses, etc.
2. Add Employee Data from ATS to Onboarding Systems
   • Once a candidate is hired in the ATS, create a user profile for them in the onboarding software.
   • Ensure they receive all relevant documents, access, and resources on Day 1.
3. Connect Marketing Automation Platforms with CRM
   • Whenever a lead engages with a campaign in HubSpot, reflect the new/updated lead details in Salesforce.
   • Let sales teams see fresh, accurate lead info in real time.
4. Link CRM with Contract Management & File Storage
   • Automatically generate contracts in a contract management system (e.g., DocuSign) when a CRM deal is marked as “won.”
   • Store important client documents in Dropbox, Box, or Google Drive via an automated sync.
5. Sync HRMS and Benefits Administration
   • Reflect salary changes or promotions from HRMS to benefits software, ensuring perks and incentives are accurately applied.

4. Key Challenges in Building SaaS Integrations

Despite the clear advantages, integrating SaaS apps can be complicated. Here are some challenges to watch out for:

• Compatibility Issues & Lack of Standardized APIs
  • Many SaaS apps have inconsistent or poorly documented APIs, making integration a puzzle.
• Security & Privacy Risks
  • Sensitive business or personal data is often exchanged, so robust encryption and authentication are a must.
• Heavy Developer Bandwidth Required
  • Building integrations in-house can overwhelm engineering teams, especially when creating multiple point-to-point connections.
• Ongoing Maintenance
  • Even after your integrations are up and running, changes in third-party APIs or business logic can break workflows, requiring continuous monitoring.

5. Choosing the Right Approach: Build vs Buy

Depending on your goals, your team size, and the complexity of the integrations, you’ll have to decide whether to develop integrations in-house or outsource to third-party solutions.

6. Top Platforms for SaaS Integration

Multiple categories of third-party platforms exist to help you avoid building everything from scratch:

1. iPaaS (Integration Platform as a Service)
   • Examples: Workato, Zapier, Mulesoft
   • Ideal for internal software connectivity and workflow automation. Often includes drag-and-drop, low-code interfaces.
2. Embedded iPaaS
   • Examples: Workato Embedded, Tray Embedded
   • Allows SaaS providers to embed integrations directly into their product, so end users can set up connections quickly.
3. Unified API
   • Examples: Knit, Merge, Finch
   • Offers a “one-to-many” approach, so you integrate once with a unified API and instantly unlock connectivity to many apps within that category.
   • Great for scaling customer-facing integrations rapidly.
4. RPA (Robotic Process Automation)
   • Examples: UiPath, Blue Prism
   • Uses “bots” to mimic manual tasks (like form-filling). Ideal when no suitable API is available, though can be fragile.

7. How to Integrate SaaS Applications (Step-by-Step)

If you’re ready to implement SaaS integrations, here’s a simplified roadmap:

1. Define Goals and Scope
   • Clarify whether integrations are for internal efficiency, customer-facing benefits, or both.
   • List and prioritize which SaaS apps to connect first (based on ROI, user demand, etc.).
2. Choose the Right Tools (or Strategy)
   • Pick between building native integrations, using an iPaaS or embedded iPaaS, or leveraging a unified API provider like Knit.
   • Factor in timeline, developer bandwidth, total cost, and your long-term product roadmap.
3. Design Workflows and Data Mappings
   • Determine exactly how data should flow from one application to the other.
   • Create field mappings (e.g., “CRM Lead Name” → “Marketing Platform Contact Name”).
4. Configure Authentication & Security
   • Use secure OAuth flows (or relevant protocols) to connect the apps.
   • Encrypt data at rest and in transit, and follow compliance regulations (SOC 2, GDPR, etc.).
5. Test Thoroughly
   • Start with a sandbox or staging environment to test for data accuracy and error handling.
   • Check edge cases (large data volumes, missing fields, rate limits).
6. Launch and Monitor
   • Push live gradually to a small set of users or a pilot department.
   • Use logging and alert systems to detect any integration failures early.
7. Iterate and Optimize
   • Solicit feedback from end users.
   • Adjust data flows, add more connectors, or refine based on your evolving requirements.

8. SaaS Integration Best Practices

To ensure your integrations are robust and future-proof, follow these guiding principles:

• Start with a Clear Business Goal
  • Align every integration with a tangible outcome—e.g., reduce 30% of manual data entry time, or expedite customer onboarding by 40%.
• Prioritize Security and Compliance
  • Protect sensitive data via encryption, access controls, and up-to-date compliance (SOC 2, ISO 27001, etc.).
• Document Everything
  • Keep track of workflows, field mappings, and error-handling protocols. This ensures anyone on your team can quickly troubleshoot or iterate.
• Build Scalably
  • Avoid one-off solutions that can’t handle more data or additional endpoints. A single integration might be fine initially, but plan for 10 or 50.
• Test and Monitor Continuously
  • Integrations can break when APIs update or data schemas change. Ongoing logging, alerts, and performance metrics help you catch issues early.

9. The Future of SaaS Integration

1. AI-Powered Integrations
Generative AI will reshape how integrations are built, potentially automating much of the dev work to accelerate go-live times.

2. Verticalized Solutions
Industry-specific integration packs will make it even easier for specialized SaaS providers (e.g., healthcare, finance) to connect relevant tools in their niche.

3. Heightened Security and Privacy
As data regulations tighten worldwide, expect solutions that offer near-zero data storage (to reduce breach risk) and continuous compliance checks.

10. FAQ

Q1: What is the difference between SaaS integration and API integration?
They’re related but not identical. SaaS integration typically connects different cloud-based tools for data-sharing and workflow automation—often via APIs. However, “API integration” can also include on-prem systems or older apps that aren’t strictly SaaS.

Q2: Which SaaS integration platform should I choose for internal workflows?
If the goal is internal automation and quick no-code workflows, an iPaaS solution (like Zapier or Workato) is often enough. Evaluate cost, number of connectors, and ease of use.

Q3: How do I develop a SaaS integration strategy?

1. Define objectives (cost savings, time to market, user experience).
2. Map out which applications need to be connected first.
3. Decide on build vs buy.
4. Implement a pilot integration and measure results.
5. Iterate and scale.

Q4: What are the best SaaS integrations to start with?
Go for high-impact and low-complexity connectors—like CRM + marketing automation or HRMS + payroll. Solving these first yields immediate ROI.

Q5: How do I ensure security in SaaS integrations?
Use encrypted data transfer (HTTPS, TLS), store credentials securely (e.g., OAuth tokens), and partner with vendors that follow strict security and compliance standards (SOC 2 Type II, GDPR, etc.).

11. TL;DR

SaaS integration is the key to eliminating data silos, cutting down manual work, and offering exceptional user experiences. While building integrations in-house can suit a handful of simple workflows, scaling to dozens or hundreds of connectors often calls for third-party solutions—like iPaaS, embedded iPaaS, or unified API platforms.

A single, well-planned integration strategy can elevate your team’s productivity, delight customers, and set you apart in a crowded SaaS market. With careful planning, robust security, and ongoing monitoring, you’ll be ready to ride the next wave of SaaS innovation.

Get Started with Knit’s Unified API

If you need to build and manage customer-facing SaaS integrations at scale, Knit has you covered. With our unified API approach, you can connect to hundreds of popular SaaS tools in just one integration effort—backed by robust monitoring, a pass-through architecture for security, and real-time sync with a 99.99% SLA.

Ready to learn more?
Schedule a Demo with Knit or explore our Documentation to see how you can launch SaaS integrations faster than ever.

‍

In today's AI-driven world, AI agents have become transformative tools, capable of executing tasks with unparalleled speed, precision, and adaptability. From automating mundane processes to providing hyper-personalized customer experiences, these agents are reshaping the way businesses function and how users engage with technology. However, their true potential lies beyond standalone functionalities—they thrive when integrated seamlessly with diverse systems, data sources, and applications.

This integration is not merely about connectivity; it’s about enabling AI agents to access, process, and act on real-time information across complex environments. Whether pulling data from enterprise CRMs, analyzing unstructured documents, or triggering workflows in third-party platforms, integration equips AI agents to become more context-aware, action-oriented, and capable of delivering measurable value.

This article explores how seamless integrations unlock the full potential of AI agents, the best practices to ensure success, and the challenges that organizations must overcome to achieve seamless and impactful integration.

Rise of AI Agents

The rise of Artificial Intelligence (AI) agents marks a transformative shift in how we interact with technology. AI agents are intelligent software entities capable of performing tasks autonomously, mimicking human behavior, and adapting to new scenarios without explicit human intervention. From chatbots resolving customer queries to sophisticated virtual assistants managing complex workflows, these agents are becoming integral across industries.

This rise of use of AI agents has been attributed to factors like:

• Advances in AI and machine learning models, and access to vast datasets which allow AI agents to understand natural language better and execute tasks more intelligently.
• Demand for automating routine tasks, reducing the burden on human resources, and improving efficiency, driving operational efficiency

Understanding How AI Agents Work

AI agents are more than just software programs; they are intelligent systems capable of executing tasks autonomously by mimicking human-like reasoning, learning, and adaptability. Their functionality is built on two foundational pillars: 

1) Contextual Knowledge

For optimal performance, AI agents require deep contextual understanding. This extends beyond familiarity with a product or service to include insights into customer pain points, historical interactions, and updates in knowledge. However, to equip AI agents with this contextual knowledge, it is important to provide them access to a centralized knowledge base or data lake, often scattered across multiple systems, applications, and formats. This ensures they are working with the most relevant and up-to-date information. Furthermore, they need access to all new information, such as product updates, evolving customer requirements, or changes in business processes, ensuring that their outputs remain relevant and accurate.

For instance, an AI agent assisting a sales team must have access to CRM data, historical conversations, pricing details, and product catalogs to provide actionable insights during a customer interaction.

2) Strategic Action

AI agents’ value lies not only in their ability to comprehend but also to act. For instance, AI agents can perform activities such as updating CRM records after a sales call, generating invoices, or creating tasks in project management tools based on user input or triggers. Similarly, AI agents can initiate complex workflows, such as escalating support tickets, scheduling appointments, or launching marketing campaigns. However, this requires seamless connectivity across different applications to facilitate action. 

For example, an AI agent managing customer support could resolve queries by pulling answers from a knowledge base and, if necessary, escalating unresolved issues to a human representative with full context.

The capabilities of AI agents are undeniably remarkable. However, their true potential can only be realized when they seamlessly access contextual knowledge and take informed actions across a wide array of applications. This is where integrations play a pivotal role, serving as the key to bridging gaps and unlocking the full power of AI agents.

Enter Integrations: Powering AI Agents

The effectiveness of an AI agent is directly tied to its ability to access and utilize data stored across diverse platforms. This is where integrations shine, acting as conduits that connect the AI agent to the wealth of information scattered across different systems. These data sources fall into several broad categories, each contributing uniquely to the agent's capabilities:

Types of Agent Data Sources: The Foundation of AI Agent Functionality

1) Structured Data Sources

Platforms like databases, Customer Relationship Management (CRM) systems (e.g., Salesforce, HubSpot), and Enterprise Resource Planning (ERP) tools house structured data—clean, organized, and easily queryable. For example, CRM integrations allow AI agents to retrieve customer contact details, sales pipelines, and interaction histories, which they can use to personalize customer interactions or automate follow-ups.

2) Unstructured Data Sources

The majority of organizational knowledge exists in unstructured formats, such as PDFs, Word documents, emails, and collaborative platforms like Notion or Confluence. Cloud storage systems like Google Drive and Dropbox add another layer of complexity, storing files without predefined schemas. Integrating with these systems allows AI agents to extract key insights from meeting notes, onboarding manuals, or research reports. For instance, an AI assistant integrated with Google Drive could retrieve and summarize a company’s annual performance review stored in a PDF document.

3) Streaming Data Sources

Real-time data streams from IoT devices, analytics tools, or social media platforms offer actionable insights that are constantly updated. AI agents integrated with streaming data sources can monitor metrics, such as energy usage from IoT sensors or engagement rates from Twitter analytics, and make recommendations or trigger actions based on live updates.

4) Third-Party Applications

APIs from third-party services like payment gateways (Stripe, PayPal), logistics platforms (DHL, FedEx), and HR systems (BambooHR, Workday) expand the agent's ability to act across verticals. For example, an AI agent integrated with a payment gateway could automatically reconcile invoices, track payments, and even issue alerts for overdue accounts.

The Role of Data Ingestion 

To process this vast array of data, AI agents rely on data ingestion—the process of collecting, aggregating, and transforming raw data into a usable format. Data ingestion pipelines ensure that the agent has access to a broad and rich understanding of the information landscape, enhancing its ability to make accurate decisions.

However, this capability requires robust integrations with a wide variety of third-party applications. Whether it's CRM systems, analytics tools, or knowledge repositories, each integration provides an additional layer of context that the agent can leverage.

Without these integrations, AI agents would be confined to static or siloed information, limiting their ability to adapt to dynamic environments. For example, an AI-powered customer service bot lacking integration with an order management system might struggle to provide real-time updates on a customer’s order status, resulting in a frustrating user experience.

The Case for Real-Time Integrations

In many applications, the true value of AI agents lies in their ability to respond with real-time or near-real-time accuracy. Integrations with webhooks and streaming APIs enable the agent to access live data updates, ensuring that its responses remain relevant and timely.

Consider a scenario where an AI-powered invoicing assistant is tasked with generating invoices based on software usage. If the agent relies on a delayed data sync, it might fail to account for a client’s excess usage in the final moments before the invoice is generated. This oversight could result in inaccurate billing, financial discrepancies, and strained customer relationships.

Why Agents Need Integrations:

1) Empowering Action Across Applications

Integrations are not merely a way to access data for AI agents; they are critical to enabling these agents to take meaningful actions on behalf of other applications. This capability is what transforms AI agents from passive data collectors into active participants in business processes. 

Integrations play a crucial role in this process by connecting AI agents with different applications, enabling them to interact seamlessly and perform tasks on behalf of the user to trigger responses, updates, or actions in real time. 

For instance, A customer service AI agent integrated with CRM platforms can automatically update customer records, initiate follow-up emails, and even generate reports based on the latest customer interactions. SImilarly, if a popular product is running low, the AI agent for e-commerce platform can automatically reorder from the supplier, update the website’s product page with new availability dates, and notify customers about upcoming restocks. Furthermore, A marketing AI agent integrated with CRM and marketing automation platforms (e.g., Mailchimp, ActiveCampaign) can automate email campaigns based on customer behaviors—such as opening specific emails, clicking on links, or making purchases. 

Integrations allow AI agents to automate processes that span across different systems. For example, an AI agent integrated with a project management tool and a communication platform can automate task assignments based on project milestones, notify team members of updates, and adjust timelines based on real-time data from work management systems.

For developers driving these integrations, it’s essential to build robust APIs and use standardized protocols like OAuth for secure data access across each of the applications in use. They should also focus on real-time synchronization to ensure the AI agent acts on the most current data available. Proper error handling, logging, and monitoring mechanisms are critical to maintaining reliability and performance across integrations. Furthermore, as AI agents often interact with multiple platforms, developers should design integration solutions that can scale. This involves using scalable data storage solutions, optimizing data flow, and regularly testing integration performance under load.

2) Building RAG (Retrieval-Augmented Generation) pipelines

Retrieval-Augmented Generation (RAG) is a transformative approach that enhances the capabilities of AI agents by addressing a fundamental limitation of generative AI models: reliance on static, pre-trained knowledge. RAG fills this gap by providing a way for AI agents to efficiently access, interpret, and utilize information from a variety of data sources. Here’s how iintegrations help in building RAG pipelines for AI agents:

Access to Diverse Data Sources

Traditional APIs are optimized for structured data (like databases, CRMs, and spreadsheets). However, many of the most valuable insights for AI agents come from unstructured data—documents (PDFs), emails, chats, meeting notes, Notion, and more. Unstructured data often contains detailed, nuanced information that is not easily captured in structured formats.

RAG enables AI agents to access and leverage this wealth of unstructured data by integrating it into their decision-making processes. By integrating with these unstructured data sources, AI agents:

• Employ Optical Character Recognition (OCR) for scanned documents and Natural Language Processing (NLP) for text extraction.
• Use NLP techniques like keyword extraction, named entity recognition, sentiment analysis, and topic modeling to parse and structure the information.
• Convert text into vector embeddings using models such as Word2Vec, GloVe, and BERT, which represent words and phrases as numerical vectors capturing semantic relationships between them.
• Use similarity metrics (e.g., cosine similarity) to find relevant patterns and relationships between different pieces of data, allowing the AI agent to understand context even when information is fragmented or loosely connected.

Unified Retrieval Layer

RAG involves not only the retrieval of relevant data from these sources but also the generation of responses based on this data. It allows AI agents to pull in information from different platforms, consolidate it, and generate responses that are contextually relevant. 

For instance, an HR AI agent might need to pull data from employee records, performance reviews, and onboarding documents to answer a question about benefits. RAG enables this agent to access the necessary context and background information from multiple sources, ensuring the response is accurate and comprehensive  through a single retrieval mechanism.

Real-Time Contextual Understanding

RAG empowers AI agents by providing real-time access to updated information from across various platforms with the help of Webhooks. This is critical for applications like customer service, where responses must be based on the latest data. 

For example, if a customer asks about their recent order status, the AI agent can access real-time shipping data from a logistics platform, order history from an e-commerce system, and promotional notes from a marketing database—enabling it to provide a response with the latest information. Without RAG, the agent might only be able to provide a generic answer based on static data, leading to inaccuracies and customer frustration.

Key Benefits of RAG for AI Agents

1. Enhanced Accuracy
   By incorporating real-time information retrieval, RAG reduces the risk of hallucinations—a common issue with LLMs—ensuring responses are accurate and grounded in authoritative data sources.
2. Scalability Across Domains and Use Cases
   With access to external knowledge repositories, AI agents equipped with RAG can seamlessly adapt to various industries, such as healthcare, finance, education, and e-commerce.
3. Improved User Experience
   RAG-powered agents offer detailed, context-aware, and dynamic responses, elevating user satisfaction in applications like customer support, virtual assistants, and education platforms.
4. Cost Efficiency
   By offloading the need to encode every piece of knowledge into the model itself, RAG allows smaller LLMs to perform at near-human accuracy levels, reducing computational costs.
5. Future-Proofing AI Systems
   Continuous learning becomes effortless as new information can be integrated into the retriever without retraining the generator, making RAG an adaptable solution in fast-evolving industries.

Challenges in Implementing RAG (Retrieval-Augmented Generation)

While RAG presents immense opportunities to enhance AI capabilities, its implementation comes with a set of challenges. Addressing these challenges is crucial to building efficient, scalable, and reliable AI systems.

1. Latency and Performance Bottlenecks: Real-time retrieval and generation involve multiple computational steps, including embedding queries, retrieving data, and generating responses. This can introduce delays, especially when handling large-scale queries or deploying RAG on low-powered devices.some text
   • Mitigation Strategies:some text
     • Approximate Nearest Neighbor (ANN) Search: Use ANN techniques in retrievers (e.g., FAISS or ScaNN) to speed up vector searches without sacrificing too much accuracy.
     • Caching Frequent Queries: Cache the most common retrieval results to bypass the retriever for repetitive queries.
     • Parallel Processing: Leverage parallelism in data retrieval and model inference to minimize bottlenecks.
     • Model Optimization: Use quantized or distilled models for faster inference during embedding generation or response synthesis.
2. Data Quality and Bias in Knowledge Bases: The quality and relevance of retrieved data heavily depend on the source knowledge base. If the data is outdated, incomplete, or biased, the generated responses will reflect those shortcomings.some text
   • Mitigation Strategies:some text
     • Regular Data Updates: Ensure the knowledge base is periodically refreshed with the latest and most accurate information.
     • Source Validation: Use reliable, vetted sources to build the knowledge base.
     • Bias Mitigation: Perform audits to identify and correct biases in the retriever’s dataset or the generator’s output.
     • Content Moderation: Implement filters to exclude low-quality or irrelevant data during the retrieval phase.
3. Scalability with Large Datasets: As datasets grow in size and complexity, retrieval becomes computationally expensive. Indexing, storage, and retrieval from large-scale knowledge bases require robust infrastructure.some text
   • Mitigation Strategies:some text
     • Hierarchical Retrieval: Use multi-stage retrievers where a lightweight model filters down the dataset before passing it to a heavier, more precise retriever.
     • Distributed Systems: Deploy distributed retrieval systems using frameworks like Elasticsearch clusters or AWS-managed services.
     • Efficient Indexing: Use optimized indexing techniques (e.g., HNSW) to handle large datasets efficiently.
4. Alignment Between Retrieval and Generation: RAG systems must align retrieved information with user intent to generate coherent and contextually relevant responses. Misalignment can lead to confusing or irrelevant outputs.some text
   • Mitigation Strategies:some text
     • Query Reformulation: Preprocess user queries to align them with the retriever’s capabilities, using NLP techniques like rephrasing or entity extraction.
     • Context-Aware Generation: Incorporate structured prompts that explicitly guide the generator to focus on the retrieved context.
     • Feedback Mechanisms: Enable end-users or moderators to flag poor responses, and use this feedback to fine-tune the retriever and generator.
5. Handling Ambiguity in Queries: Ambiguous user queries can lead to irrelevant or incomplete retrieval, resulting in suboptimal generated responses.some text
   • Mitigation Strategies:some text
     • Clarification Questions: Build mechanisms for the AI to ask follow-up questions when the user query lacks clarity.
     • Multi-Pass Retrieval: Retrieve multiple potentially relevant contexts and use the generator to combine and synthesize them.
     • Weighted Scoring: Assign higher relevance scores to retrieved documents that align more closely with query intent, using additional heuristics or context-based filters.
6. Integration Complexity: Seamlessly integrating retrieval systems with generative models requires significant engineering effort, especially when handling domain-specific requirements or legacy systems.some text
   • Mitigation Strategies:some text
     • Frameworks and Libraries: Use existing RAG frameworks like Haystack or LangChain to reduce development complexity.
     • API Abstraction: Wrap the retriever and generator in unified APIs to simplify the integration process.
     • Microservices Architecture: Deploy retriever and generator components as independent services, allowing for modular development and easier scaling.
7. Using Unreliable Sources: A Key Challenge in RAG Implementation**: The effectiveness of Retrieval-Augmented Generation (RAG) depends heavily on the quality of the knowledge base. If the system relies on unreliable, biased, or non-credible sources, it can lead to the generation of inaccurate, misleading, or harmful outputs. This undermines the reliability of the AI and can damage user trust, especially in high-stakes domains like healthcare, finance, or legal services.some text
   • Mitigation Strategies:some text
     • Source Vetting and Curation: Establish strict criteria for selecting sources, prioritizing those that are credible, authoritative, and up-to-date along with regular audit.
     • Trustworthiness Scoring: Assign trustworthiness scores to sources based on factors like citation frequency, domain expertise, and author credentials.
     • Multi-Source Validation: Cross-reference retrieved information against multiple trusted sources to ensure accuracy and consistency.

Steps to Build a RAG Pipeline

1. Define the Use Casesome text
   • Identify the specific application for your RAG pipeline, such as answering customer support queries, generating reports, or creating knowledge assistants for internal use.
2. Select Data Sources
   • Determine the types of data your pipeline will access, including structured (databases, APIs) and unstructured (documents, emails, knowledge bases).
3. Choose Tools and Technologies
   • Vectorization Tools: Select pre-trained models for creating text embeddings.
   • Databases: Use a vector database to store and retrieve embeddings.
   • Generative Models: Choose a model optimized for your domain and use case.
4. Develop and Deploy Retrieval Models
   • Train retrieval models to handle semantic queries effectively. Focus on accuracy and relevance, balancing precision with speed.
5. Integrate Generative AI
   • Connect the retrieval mechanism to the generative model. Ensure input prompts include the retrieved context for highly relevant outputs.
6. Implement Quality Assurance
   • Regularly test the pipeline with varied inputs to evaluate accuracy, speed, and the relevance of responses.
   • Monitor for potential biases or inaccuracies and adjust models as needed.
7. Optimize and Scale
   • Fine-tune the pipeline based on user feedback and performance metrics.
   • Scale the system to handle larger datasets or higher query volumes as needed.

Real-World Use Cases of integrations for AI Agents

AI-Powered Customer Support for an eCommerce Platform

Integration of an AI-powered customer service agent with CRM systems, ticketing platforms, and other tools can help enhance contextual knowledge and take proactive actions, delivering a superior customer experience. 

‍

For instance, when a customer reaches out with a query—such as a delayed order—the AI agent retrieves their profile from the CRM, including past interactions, order history, and loyalty status, to gain a comprehensive understanding of their background. Simultaneously, it queries the ticketing system to identify any related past or ongoing issues and checks the order management system for real-time updates on the order status. Combining this data, the AI develops a holistic view of the situation and crafts a personalized response. It may empathize with the customer’s frustration, offer an estimated delivery timeline, provide goodwill gestures like loyalty points or discounts, and prioritize the order for expedited delivery.

‍

The AI agent also performs critical backend tasks to maintain consistency across systems. It logs the interaction details in the CRM, updating the customer’s profile with notes on the resolution and any loyalty rewards granted. The ticketing system is updated with a resolution summary, relevant tags, and any necessary escalation details. Simultaneously, the order management system reflects the updated delivery status, and insights from the resolution are fed into the knowledge base to improve responses to similar queries in the future. Furthermore, the AI captures performance metrics, such as resolution times and sentiment analysis, which are pushed into analytics tools for tracking and reporting.

Retail AI Agent with Omni-Channel Integration

In retail, AI agents can integrate with inventory management systems, customer loyalty platforms, and marketing automation tools for enhancing customer experience and operational efficiency. For instance, when a customer purchases a product online, the AI agent quickly retrieves data from the inventory management system to check stock levels. It can then update the order status in real time, ensuring that the customer is informed about the availability and expected delivery date of the product. If the product is out of stock, the AI agent can suggest alternatives that are similar in features, quality, or price, or provide an estimated restocking date to prevent customer frustration and offer a solution that meets their needs. 

‍

Similarly, if a customer frequently purchases similar items, the AI might note this and suggest additional products or promotions related to these interests in future communications. By integrating with marketing automation tools, the AI agent can personalize marketing campaigns, sending targeted emails, SMS messages, or notifications with relevant offers, discounts, or recommendations based on the customer’s previous interactions and buying behaviors. The AI agent also writes back data to customer profiles within the CRM system. It logs details such as purchase history, preferences, and behavioral insights, allowing retailers to gain a deeper understanding of their customers’ shopping patterns and preferences. 

Key challenges with integrations for AI agents

Integrating AI (Artificial Intelligence) and RAG (Recommendations, Actions, and Goals) frameworks into existing systems is crucial for leveraging their full potential, but it introduces significant technical challenges that organizations must navigate. These challenges span across data ingestion, system compatibility, and scalability, often requiring specialized technical solutions and ongoing management to ensure successful implementation.

Data Compatibility and Quality:

• Data Fragmentation: Many organizations operate in data-rich but siloed environments, where critical information is scattered across multiple tools and platforms. For instance, customer data may reside in CRMs, operational data in ERP systems, and communication data in collaboration tools like Slack or Google Drive. These systems often store data in incompatible formats, making it difficult to consolidate into a single, accessible source. This fragmentation obstructs AI's ability to deliver actionable insights by limiting its access to the complete context required for accurate recommendations and decisions. Overcoming this challenge is particularly difficult in organizations with legacy systems or highly customized architectures.
• Data Quality Issues: AI systems rely heavily on data accuracy, completeness, and consistency. Common issues such as duplicate records, missing fields, or outdated entries can severely undermine the performance of AI models. Inconsistent data formatting, such as differences in date structures, naming conventions, or measurement units across systems, can lead to misinterpretation of information by AI agents. Low-quality data not only reduces the effectiveness of AI but also erodes stakeholder confidence in the system's outputs, creating a cycle of distrust and underutilization.

Complexity of Integration:

• System Compatibility: Integrating AI frameworks with existing platforms is often hindered by discrepancies in system architecture, API protocols, and data exchange standards. Enterprise systems such as CRMs, ERPs, and proprietary databases are frequently designed without interoperability in mind. These compatibility issues necessitate custom integration solutions, which can be time-consuming and resource-intensive. Additionally, the lack of standardization across APIs complicates the development process, increasing the risk of integration failures or inconsistent data flow.
• Real-Time Integration: Real-time functionality is critical for AI systems that generate recommendations or perform actions dynamically. However, achieving this is particularly challenging when dealing with high-frequency data streams, such as those from IoT devices, e-commerce platforms, or customer-facing applications. Low-latency requirements demand advanced data synchronization capabilities to ensure that updates are processed and reflected instantaneously across all systems. Infrastructure limitations, such as insufficient bandwidth or outdated hardware, further exacerbate this challenge, leading to performance degradation or delayed responses.

Scalability Issues:

• High Volume or Large Data Ingestion: AI integrations often require processing enormous volumes of data generated from diverse sources. These include transactional data from e-commerce platforms, behavioral data from user interactions, and operational data from business systems. Managing these data flows requires robust infrastructure capable of handling high throughput while maintaining data accuracy and integrity. The dynamic nature of data sources, with fluctuating volumes during peak usage periods, further complicates scalability, as systems must be designed to handle both expected and unexpected surges.
• Third-Party Limitations and Data Loss: Many third-party systems impose rate limits on API calls, which can restrict the volume of data an AI system can access or process within a given timeframe. These limitations often lead to incomplete data ingestion or delays in synchronization, impacting the overall reliability of AI outputs. Additional risks, such as temporary outages or service disruptions from third-party providers, can result in critical data being lost or delayed, creating downstream effects on AI performance.

Building AI Actions for Automation:

• API Research and Management: AI integrations require seamless interaction with third-party applications through APIs, which involves extensive research into their specifications, capabilities, and constraints. Organizations must navigate a wide variety of authentication protocols, such as OAuth 2.0 or API key-based systems, which can vary significantly in complexity and implementation requirements. Furthermore, APIs are subject to frequent updates or deprecations, which may lead to breaking changes that disrupt existing integrations and necessitate ongoing monitoring and adaptation.
• Cost of Engineering Hours: Developing and maintaining AI integrations demands significant investment in engineering resources. This includes designing custom solutions, monitoring system performance, and troubleshooting issues arising from API changes or infrastructure bottlenecks. The long-term costs of managing these integrations can escalate as the complexity of the system grows, placing a strain on both technical teams and budgets. This challenge is especially pronounced in smaller organizations with limited technical expertise or resources to dedicate to such efforts.

Monitoring and Observability Gaps

• Lack of Unified Dashboards: Organizations often use disparate monitoring tools that focus on specific components, such as data pipelines, model health, or API integrations. However, these tools rarely offer a comprehensive view of the overall system performance. This fragmented approach creates blind spots, making it challenging to identify interdependencies or trace the root causes of failures and inefficiencies. The absence of a single pane of glass for monitoring hinders decision-making and proactive troubleshooting.
• Failure Detection: AI systems and their integrations are susceptible to several issues, such as dropped API calls, broken data pipelines, and data inconsistencies. These problems, if undetected, can escalate into critical disruptions. Without robust failure detection mechanisms—like anomaly detection, alerting systems, and automated diagnostics—such issues can remain unnoticed until they significantly impact operations, leading to downtime, loss of trust, or financial setbacks.

Versioning and Compatibility Drift

• API Deprecations: Third-party providers frequently update or discontinue APIs, creating potential compatibility issues for existing integrations. For example, a CRM platform might revise its API authentication protocols, making current integration setups obsolete unless they are swiftly updated. Failure to monitor and adapt to such changes can lead to disrupted workflows, data loss, or security vulnerabilities.
• Model Updates: AI models require periodic retraining and updates to improve performance, adapt to new data, or address emerging challenges. However, these updates can unintentionally introduce changes in outputs, workflows, or integration points. If not thoroughly tested and managed, such changes can disrupt established business processes, leading to inconsistencies or operational delays. Effective version control and compatibility testing are critical to mitigate these risks.

How to Add Integrations to AI Agents?

Adding integrations to AI agents involves providing these agents with the ability to seamlessly connect with external systems, APIs, or services, allowing them to access, exchange, and act on data. Here are the top ways to achieve the same:

Custom Development Approach

Custom development involves creating tailored integrations from scratch to connect the AI agent with various external systems. This method requires in-depth knowledge of APIs, data models, and custom logic. The process involves developing specific integrations to meet unique business requirements, ensuring complete control over data flows, transformations, and error handling. This approach is suitable for complex use cases where pre-built solutions may not suffice.

Pros:

• Highly Tailored Solutions: Custom development allows for precise control over the integration process, enabling specific adjustments to meet unique business requirements.
• Full Control: Organizations can implement specific data validation rules, security protocols, and transformations that best suit their needs.
• Complex Use Cases: Custom development is ideal for complex integrations involving multiple systems or detailed workflows that existing platforms cannot support.

Cons:

• Resource-Intensive: Building and maintaining custom integrations requires specialized skills in software development, APIs, and data integration.
• Time Consuming: Development can take weeks to months, depending on the complexity of the integration.
• Maintenance: Ongoing maintenance is required to adapt the integration to changes in APIs, business needs, or system upgrades.

Embedded iPaaS Approach

Embedded iPaaS (Integration Platform as a Service) solutions offer pre-built integration platforms that include no-code or low-code tools. These platforms allow organizations to quickly and easily set up integrations between the AI agent and various external systems without needing deep technical expertise. The integration process is simplified by using a graphical interface to configure workflows and data mappings, reducing development time and resource requirements.

Pros:

• Quick Deployment: Rapid implementation thanks to the use of visual interfaces and pre-built connectors, enabling organizations to integrate systems quickly.
• Scalability: Easy to adjust and scale as business requirements evolve, ensuring flexibility over time.
• Reduced Costs: Lower upfront costs and less need for specialized development teams compared to custom development.

Cons:

• Limited Customization: Some iPaaS solutions may not offer enough customization for complex or highly specific integration needs.
• Platform Dependency: Integration capabilities are restricted by the APIs and features provided by the chosen iPaaS platform.
• Recurring Fees: Subscription costs can accumulate over time, making this approach more expensive for long-term use.

Unified API Solutions (e.g., Knit) 

Unified API solutions provide a single API endpoint that connects to multiple SaaS products and external systems, simplifying the integration process. This method abstracts the complexity of dealing with multiple APIs by consolidating them into a unified interface. It allows the AI agent to access a wide range of services, such as CRM systems, marketing platforms, and data analytics tools, through a seamless and standardized integration process.

Pros:

• Speed: Quick deployment due to pre-built connectors and automated setup processes.
• 100% API Coverage: Access to a wide range of integrations with minimal setup, reducing the complexity of managing multiple API connections.
• Ease of Use: Simplifies integration management through a single API, reducing overhead and maintenance needs.

How Knit AI Can Power Integrations for AI Agents

Knit offers a game-changing solution for organizations looking to integrate their AI agents with a wide variety of SaaS applications quickly and efficiently. By providing a seamless, AI-driven integration process, Knit empowers businesses to unlock the full potential of their AI agents by connecting them with the necessary tools and data sources.

• Rapid Integration Deployment: Knit AI allows AI agents to deploy dozens of product integrations within minutes. This speed is achieved through a user-friendly interface where users can select the applications they wish to integrate with. If an application isn’t supported yet, Knit AI will add it within just 2 days. This ensures businesses can quickly adapt to new tools and services without waiting for extended development cycles.
• 100% API Coverage: With Knit AI, AI agents can access a wide range of APIs from various platforms and services through a unified API. This means that whether you’re integrating with CRM systems, marketing platforms, or custom-built applications, Knit provides complete API coverage. The AI agent can interact with these systems as if they were part of a single ecosystem, streamlining data access and management.
• Custom Integration Options: Users can specify their needs—whether they want to read or write data, which data fields they need, and whether they require scheduled syncs or real-time API calls. Knit AI then builds connectors tailored to these specifications, allowing for precise control over data flows and system interactions. This customization ensures that the AI agent can perform exactly as required in real-time environments.
• Testing and Validation: Before going live, users can test their integrations using Knit’s available sandboxes. These sandboxes allow for a safe environment to verify that the integration works as expected, handling edge cases and ensuring data integrity. This process minimizes the risk of errors and ensures that the integration performs optimally once it’s live.
• Publish with Confidence: Once tested and validated, the integration can be published with a single click. Knit simplifies the deployment process, enabling businesses to go from development to live integration in minutes. This approach significantly reduces the friction typically associated with traditional integration methods, allowing organizations to focus on leveraging their AI capabilities without technical barriers.

By integrating with Knit, organizations can power their AI agents to interact seamlessly with a wide array of applications. This capability not only enhances productivity and operational efficiency but also allows for the creation of innovative use cases that would be difficult to achieve with manual integration processes. Knit thus transforms how businesses utilize AI agents, making it easier to harness the full power of their data across multiple platforms.

Ready to see how Knit can transform your AI agents? Contact us today for a personalized demo!

‍

Integrations are becoming a mainstream requirement for organizations using many SaaS applications. Invariably, organizations seek robust third-party solutions as alternatives to building and managing all integrations in-house (because it is time—and cost-intensive and diverts engineering bandwidth). Workflow automation, embedded iPaaS, ETL, and unified API are a few options that organizations are increasingly adopting. 

‍

Which integration approach to choose?

As mentioned above, you can ship and scale SaaS integrations in several ways. Here is a quick snapshot of the different approaches and their viability for different scenarios:

If you’d like to learn more about different approaches, you could consider reading a detailed article here

‍

While Merge.dev has become one of the popular solutions in the unified API section, there are alternatives to Merge that support native integration development and management for SaaS applications. Each with their own advantages and drawbacks. 

In this article, we will discuss in detail Merge.dev and other market players who stand as credible alternatives to help companies scale their integration roadmap. A comprehensive comparison detailing the strengths and weaknesses of each alternative will enable businesses to make an informed choice for their integration journey. 

Building and managing SaaS integrations with Merge.dev

Merge.dev is a unified API that helps businesses to build 1: many integrations with SaaS applications. This means that Merge enables companies to build native integrations with multiple applications within the same category (ex., ATS, HRIS) in a single go, using one connector which Merge provides. Invariably, this makes the integration development and management process extremely lucid, saves time and resources, and makes integration scalability robust and effective. Let’s quickly look at the top strengths and weaknesses of Merge.dev as an integration solution for SaaS businesses, and how it compares with other alternatives.

Pricing: Starts at $7800/ year and goes up to $55K+

Merge.dev strengths

Coverage within integration categories

One of the most prominent features in favor of Merge as a preferred integration solution is the number of integrations it supports within different categories. Overall, SaaS businesses can integrate 150+ third-party applications once they connect with Merge’s unified API for different categories. This coverage or the potential integration pool that companies can leverage is significantly high as per current market standards. 

Managed authentication

Second, Merge offers managed authentication to its customers. Most applications today are based on OAuth for authentication and authorization, which require access and refresh tokens. By supporting managed authentication, Merge takes care of the authentication process for each application and keeps track of expiry rules to ensure a safe but hassle-free authentication process.  

Simplified processes

Overall, customers who have used Merge to integrate with third-party applications claim that the entire setup and integration process is quite smooth and simple. At the same time, responsiveness to feedback is high, and even the integration process for end customers is rather seamless. 

Merge.dev weaknesses

Limited integration categories

While the integrations within the unified API categories represent decent coverage for Merge, the total number of categories (6+1 in Beta) is considered to be limited by many organizations. This means that organizations that wish to integrate with applications that don’t fall into those categories have to look for alternatives. Thus, the vertical categories are a limitation that customers find with Merge, and unless this is sufficient critical mass, the addition of a new unified API category may not be justified.  

Auth component

Merge offers limited flexibility when it comes to designing and styling the auth component or branding the end user experience. It uses an iframe for its frontend auth component, which has limited customization capabilities compared to other alternatives in the market. This limits organizations' ability to ensure that the auth component that the end customers interact with looks and feels like their own application.  

Data sync model

When it comes to data sync, Merge uses a pull model, which requires organizations to build and maintain a polling infrastructure for each connected customer. The application is expected to poll the Merge’s copy of the data periodically. For data syncs needed at a higher or ad-hoc frequency, organizations can write sync functions and pull data that has changed since the last sync. While the data load is reduced in this option; however, the requirement for a polling infrastructure remains. 

On the other hand, Merge offers webhooks for data sync in two ways, i.e., sync notifications and changed data webhooks. While in the former, i.e., sync notification, organizations are notified about a potential change in the data but have to fall back on polling infrastructure to sync the changed data. Changed data webhooks exist with Merge. However, the scale and data delivery via these webhooks are not guaranteed. Depending on the data load, potential downtime, or failed processing, changed data webhooks might fail for Merge, persuading organizations to maintain a polling infrastructure. Pulling data and maintaining a polling infrastructure becomes an added responsibility for organizations, which become inclined toward identifying alternative solutions. 

Integration management

Merge’s support for integration management is robust. However, the customer success dashboards are considered technical for some organizations. This means that customer success executives and client-facing personnel have to rely on engineering teams and resources to understand the dashboards. At the same time, no tools can give visibility into integration activity, further increasing the reliance on engineering teams. This invariably slows the integration maintenance process as engineering teams generally prioritize product development and enhancements over integration management. 

TL:DR: Pros and cons of Merge

Why choose Merge

• Offers comprehensive coverage of integrations within chosen categories
• Facilitates hassle-free authentication
• Simplifies the integration process with quick response and seamless customer experience

However, some of the challenges include:

• Limited number of integration categories
• Limited flexibility for frontend auth component
• Requires users to maintain a polling infrastructure
• Lack of visibility into integration activity
• Webhooks based data sync doesn’t guarantee scale and data delivery

There is no doubt that Merge provides a considerably large integration catalog, enabling integration with multiple applications across the defined API categories. However, there are certain other features and parameters that have been pushing businesses to seek alternative solutions to scale their SaaS integration journey. Below is a list of top integration platforms that are being considered as alternatives to Merge.dev:

Merge.dev alternative #1: Knit

One of the top alternatives to Merge has been Knit. As a unified API, Knit supports integration development and management, enabling businesses to integrate with multiple applications within the same category with its 1:many connector. While there are several features which make Knit a preferred unified API provider, below mentioned are the top few which make it a sustainable and scalable Merge alternative. 

Pricing: Starts at $4800 Annually

Data security

Since integrations focus majorly on data exchange between applications, security is of paramount importance. Knit adheres to the industry’s highest standards in terms of its policies, processes and practices, complying with SOC2, GDPR, and ISO27001. In addition, all the data is doubly encrypted, at rest and in transit and all PII and user credentials are encrypted with an additional layer of application security. 

However, Knit's most significant security feature as a Merge alternative is that it doesn’t store a copy of the data. All data requests are pass through in nature, which means that no data is stored in Knit’s servers. This also translates to the fact that no third party can get access to any customer data for any organization via Knit. Since most data that passes through integrations is PII, Knit’s functionality to simply process data in its servers and not store any of it goes a long way in establishing data security and credibility. 

Customizable auth component

Knit has chosen Javascript SDK as its frontend auth component which is built as a true web component for easy customizability. Thus, it offers a lot of flexibility in terms of design and styling. This ensures that the auth component which end customers ultimately interact with has a look and feel similar to the application. Knit provides an easy drop-in frontend and embedded integrations and allows organizations to personalize every component of the auth screen, including text, T&Cs, color, font and logo on the intro screen.

Knit also enables the customization of emails (signature, text and salutations) that are sent to admin in the non-admin integration flow as well as filter the apps/categories that organizations want end customers to see on the auth screen. Finally, all types of authorization capabilities, including OAuth, API key or a username-password based authentication, are supported by Knit. 

Webhooks architecture for data sync

As a Merge alternative, Knit comes with a 100% webhooks architecture. This means that whenever data updates happen, they are dispatched to the organization’s servers in real time and the relevant folks are notified about the sync. In addition to ensuring near real time data sync, Knit has a push based data-sync model which eliminates the need for organizations to maintain a full polling infrastructure. Developers don’t need to pull data updates periodically. 

‍

Furthermore, unlike certain other unified API providers in the market, Knit’s webhook based architecture ensures guaranteed scalability and delivery irrespective of data load. This means that irrespective of the amount of data being synced between the source and destination apps, data sync with Knit will not fail, offering a 99.99 SLA. Thus, Knit’s approach to data sync with a webhook architecture ensures security, scale and resilience for event driven stream processing. Companies get guaranteed data delivery in real time. 

Customizable data sync frequency

While Knit ensures real time data sync and notifications whenever data gets updated, it also provides organizations with the flexibility and option to limit data sync and API calls as per the need. Firstly, Knit enables organizations to work with targeted data by setting filters to retrieve only the data that is needed, instead of syncing all the data which has been updated. By restricting the data being synced to only what is needed, Knit helps organizations save network and storage costs. 

At the same time, organizations can customize the sync frequency and control when syncs happen. They can start, stop or pause syncs directly from the dashboard, having full authority of when to sync and what to sync. 

Vertical and horizontal coverage

Knit also supports a more diverse portfolio of integration categories. For instance, Knit provides unified APIs for communication, e-signature, expense management and assessment integrations, which Merge is yet to bring to the table. Within the unified API categories, Knit supports a large catalog covering 100+ integrations. Furthermore, the integration catalog sees new integrations being added every month along with new unified API categories also being introduced as per market demands. Overall, Knit provides a significant coverage of HRIS, ATS, Accounting, CRM, E-Sign, Assessment, Communication integrations, covering applications across the popularity and market share spectrum. 

Comprehensive integration management support

Another capability that positions Knit as a credible Merge alternative is the comprehensive integration support it provides post development. Knit provides deep RCA and resolution including ability to identify which records were synced, ability to rerun syncs etc. It also proactively identifies and fixes any integration issues itself. With Knit, organizations get access to a detailed Logs, Issues, Integrated Accounts and Syncs page to monitor and manage integrations. Organizations find it extremely easy to keep a track of API calls, data sync requests as well as status of each webhook registered. 

Furthermore, Knit provides integration management dashboards which make it seamless for frontline customer experience (CX) teams to solve customer issues without getting engineering involved. This ensures that engineering teams can focus on new product development/ enhancements, while the CX team can manage the frequency of syncs from the dashboard without any engineering intervention. 

Custom data models

Finally, Knit supports custom data fields, which are not included in the common unified model. It allows users to access and map all data fields and manage them directly from the dashboard without writing a single line of code. These DIY dashboards for non-standard data fields can easily be managed by frontline CX teams and don’t require engineering expertise. At the same time, Knit gives end users control to authorize granular read/write access at the time of integration. 

Thus, Knit is a definite alternative to Merge which ensures:

• Data security by not storing a copy of the data in its servers
• Near real time data sync without a polling infrastructure
• Comprehensive integration management providing greater control to CX teams
• Guaranteed data scalability and delivery irrespective of data load
• Custom data fields 
• Customizable auth component and sync frequency

Merge.dev alternative #2: Finch

Another alternative to Merge is Finch. Another unified API, Finch stands a popular unified API powering employment integrations, particularly for HRIS and Payroll. 

Pricing: Starts at $600 / connected account annually with limited features‍

Here are some of the key reasons to choose Finch over Merge:

• Higher coverage for HRIS and Payroll integrations (200+)
• Standardized data coverage, i.e. standardizes all employment data across top HRIS and Payroll providers, like Quickbooks, ADP, and Paycom
• Allows users to read and write benefits data, including payroll deductions and contributions programmatically

However, there are certain factors that need to be considered before making Finch the final integration choice:

• Focused only on employment systems, i.e. HRIS and Payroll and doesn’t support integrations for other categories
• A lot of applications supported are what Finch calls “assisted” integrations which means a Finch team member or associate would manually sync data on your behalf.
• Stores a copy of the customer data, which not only adds to the storage cost, but also becomes a data privacy and security concern for end customers
• Low data fields support limited data fields available in the source system
• Provides limited integration management support with no provision for RCA and resolution 
• APIs are relatively simplistic, e.g., doesn’t give employee level payroll data

Merge.dev alternative #3: Apideck

Another Merge alternative in the unified API space is Apideck. One of the major differentiators is its focus on going broad instead of going deep in terms of integrations provided, unlike Merge. 

‍Pricing: Starts at $299/mo for each unified API with a quota of 10,000 API calls‍

Some of the top reasons for integrating with Apideck include:

• Rich category portfolio, supporting more API categories than most of the other options available in the market, including file storage, e-commerce, issue tracking, POS, SMS, etc. 
• A built-in integration marketplace, a portal for displaying integration on the users website
• Simplified UI and integration experience, without going much into technical depths

While the number of categories accessible with Apideck increase considerably, there are some concerns on the way:

• Limited depth within each category i.e. number of integrations within each unified API category is narrow
• Limited customization like custom field/ object mapping per customer and lack of specialization 

Merge.dev alternative #4: Kombo

One of the Merge alternatives for the European market is Kombo. As a unified API, it focuses primarily on helping users build and manage HRIS and ATS integrations. 

Pricing: Kombo’s pricing is not publicly available and can be requested‍

Here are some of the key reasons why certain users choose Kombo as an alternative to Merge:

• Better coverage for European applications. Originating from Germany, Kombo supports HR and recruiting related integrations for European companies
• It is a straightforward, easy to use and secure way to integrate with applications
• Has a decent depth in terms of integrations available in the categories it supports

Nonetheless, there are certain constraints which limit Kombo’s popularity outside Europe:

• Limited integration categories or vertical coverage, limited to only HRIS and ATS integrations
• Limited data coverage and security/ compliance requirements needed for companies beyond Europe 

Merge.dev alternative #5: Integration.app

The final name in this last of Merge alternatives for scaling SaaS integrations is Integration.app. As a unified API, it offers an AI powered integration framework to help businesses scale their in-house integration process. 

Pricing: Starting at $500/mo, along with  per-customer, per-integration, per-connection, and other pricing options‍

Here is a quick look why users are preferring Integration.app as a suitable Merge alternative:

• Offers pre-built integrations along with customization capabilities
• Provides comprehensive documentation, enabling developers to come up the curve quickly, without much effort
• Allows users to read and write data in external applications along with API logs to facilitate integration transparency

However, there are certain limitation with Integration.app, including:

• Very steep learning curve

TL:DR

Each of the unified API providers mentioned above are popular alternatives to Merge and have been adopted by several organizations to accelerate the pace of shipping and scaling integrations. While Merge provides great depth in the integration categories it supports, some of the alternatives bring the following strengths to the table:

• Knit: The only other product recognised as a leader on the G2 grid for 2025. Knit offers a secure alternative and 100% webhooks based architecture, eliminating the need for polling infrastructure and supporting customizable data fields, auth components and data syncs.
• Finch: Specializes in HRIS and Payroll integrations, offering standardized data coverage and benefits data management but lacks in integration depth and management support.
• Apideck: Offers a broad portfolio of API categories, an integration marketplace, and a simplified UI, but lacks depth within each category and customization options.
• Kombo: Ideal for European markets with focus on HRIS and ATS integrations, featuring easy integration with decent depth, but limited to HR-related categories.
• Integration.app: Employs AI for integration framework, offers pre-built integrations with customization, and comprehensive documentation, but has a steep learning curve.

Thus, depending on the use case, pricing framework (usage based, API call based, user based, etc.), features needed and scale, organizations can choose from different Merge alternatives. While some offer greater depth within categories, others offer a greater number of API categories, providing a wide choice for users.

‍

Teamtailor is a comprehensive recruitment software designed to streamline the hiring process, making it an indispensable tool for human resources and recruitment professionals. This all-in-one platform offers a suite of features that enhance the recruitment experience for both teams and candidates. By providing tools for job posting, candidate tracking, and communication, Teamtailor ensures a seamless and efficient hiring journey. Its user-friendly interface and robust functionalities make it a preferred choice for organizations looking to optimize their recruitment strategies.

​The Teamtailor API provides developers with robust tools to integrate and automate various recruitment and talent acquisition processes within their applications. Below are the key highlights of the Teamtailor API:

Key Features of the Teamtailor API:

• Data Management: The API provides endpoints to import, edit, and export information from your Teamtailor account, including jobs, candidates, departments, and locations. ​Teamtailor Support
• Authentication: Access is secured via API keys, which can be generated in the Teamtailor account settings under "Integrations" > "API keys." There are three types of keys with varying permissions
  • Public: Access to all public data available on the career site.​
  • Internal: Access to public and internal data, such as unlisted jobs.​
  • Admin: Full access to all account data.​
  Each key can have read, write, or read/write permissions.
• Partner and Job Board Integrations: Teamtailor provides specialized APIs for partners and job boards:​ partner.teamtailor.com
  • Partner API: Allows partners to retrieve webhooks with candidate data or update assessment results.
  • Job Board API: Enables job boards to integrate with Teamtailor using HTTP webhooks or XML feeds.

Getting Started with the Teamtailor API:

1. Generate an API Key:
   • Navigate to "Settings" > "Integrations" > "API keys" in your Teamtailor account.​
   • Click "+ New API Key" and select the appropriate permissions and scopes.​
   • Once created, the API key cannot be edited, only deleted.
2. Explore the API Documentation:
   • Review the official Teamtailor API documentation to understand available endpoints and data structures. ​Teamtailor API
3. Implement API Calls:
   • Use standard HTTP methods (GET, POST, PATCH, DELETE) to interact with the API.​
   • Include the API key in the Authorization header of your requests.​
   • Ensure your application handles responses and errors appropriately.​

‍

Teamtailor API Endpoints

Activities

• GET https://api.teamtailor.com/v1/activities/{id} : The 'Retrieve List of Activities' API allows users to fetch details of a specific activity by its ID.

Answers

• POST https://api.teamtailor.com/v1/answers : This API endpoint allows the creation of a new answer for a candidate to a specific question.
• GET https://api.teamtailor.com/v1/answers/{id} : This API endpoint is used to retrieve lists of audit events for a specific answer identified by its ID.

Audit Events

• GET https://api.teamtailor.com/v1/audit-events : This API retrieves lists of audit events from the Teamtailor platform.
• GET https://api.teamtailor.com/v1/audit-events/{id} : This API retrieves a specific audit event by its ID.

Candidates

• POST https://api.teamtailor.com/v1/candidates : This API endpoint allows you to create a new candidate in the Teamtailor system.
• GET https://api.teamtailor.com/v1/candidates/{candidate_id} : The List Candidates API allows users to retrieve a list of candidates from the Teamtailor platform.
• PATCH https://api.teamtailor.com/v1/candidates/{id} : This API allows you to change the attributes or relationships of a candidate in the Teamtailor system.

Company

• PATCH https://api.teamtailor.com/v1/company : This API endpoint allows you to update the details of a company associated with the current API key.

Custom Field Options

• GET https://api.teamtailor.com/v1/custom-field-options : This API endpoint allows the creation of a new custom field option in the Teamtailor system.
• PATCH https://api.teamtailor.com/v1/custom-field-options/{custom-field-option_id} : This API endpoint allows you to update a custom field option in the Teamtailor system.

Custom Field Selects

• POST https://api.teamtailor.com/v1/custom-field-selects : This API endpoint allows you to create a new custom field select in the Teamtailor system.
• PATCH https://api.teamtailor.com/v1/custom-field-selects/{id} : The 'Update Custom Field Selects' API allows you to update the details of a specific custom field select by its ID.

Custom Field Values

• POST https://api.teamtailor.com/v1/custom-field-values : This API endpoint allows the creation of a new custom field value in the Teamtailor system.
• PATCH https://api.teamtailor.com/v1/custom-field-values/{id} : The 'Update Custom Field Value' API allows you to update the value of a custom field for a specific resource in Teamtailor.

Custom Fields

• GET https://api.teamtailor.com/v1/custom-fields : The List Custom Fields API allows you to retrieve a list of custom fields from the Teamtailor platform.
• GET https://api.teamtailor.com/v1/custom-fields/{id} : This API retrieves a specific custom field by its ID.

Departments

• GET https://api.teamtailor.com/v1/departments : The List Departments API retrieves a list of departments from the Teamtailor platform.
• DELETE https://api.teamtailor.com/v1/departments/{id} : The Delete Department API allows you to delete a department by its ID.

Files

• POST https://api.teamtailor.com/v1/files : This API uploads a file to temporary storage and returns a transient URI that can be used in place of a public URL in some endpoints.

Job Applications

• POST https://api.teamtailor.com/v1/job-applications : This API endpoint allows the creation of a new job application in the Teamtailor system.
• PATCH https://api.teamtailor.com/v1/job-applications/{id} : This API allows you to change a candidate's attributes and relationships for a specific job application.

Job Offers

• GET https://api.teamtailor.com/v1/job-offers : This API endpoint retrieves lists of job offers from the Teamtailor platform.
• GET https://api.teamtailor.com/v1/job-offers/{id} : The Show Job Offer API retrieves details of a specific job offer using its unique ID.

Jobs

• GET https://api.teamtailor.com/v1/jobs : The 'Create a New Job' API allows users to create a new job listing on the Teamtailor platform.
• GET https://api.teamtailor.com/v1/jobs/{id} : The 'Retrieve a Specific Job' API allows users to fetch detailed information about a specific job using its unique ID.

Locations

• GET https://api.teamtailor.com/v1/locations : The List Locations API retrieves a list of locations from the Teamtailor platform.
• PATCH https://api.teamtailor.com/v1/locations/{id} : This API endpoint allows updating a location's details in the Teamtailor system.

Notes

• POST https://api.teamtailor.com/v1/notes : This API endpoint allows the creation of a new note for a candidate in the Teamtailor system.
• GET https://api.teamtailor.com/v1/notes/{id} : This API endpoint retrieves a single note from the Teamtailor system.

Notification Settings

• GET https://api.teamtailor.com/v1/notification-settings : The List Notification Settings API allows clients to retrieve a list of notification settings for a user.
• GET https://api.teamtailor.com/v1/notification-settings/{id} : The Show Notification Setting API retrieves the notification settings for a specific user by ID.

NPS Responses

• GET https://api.teamtailor.com/v1/nps-response/{id} : The Show NPS Response API retrieves the details of a specific NPS (Net Promoter Score) response using its unique identifier.
• GET https://api.teamtailor.com/v1/nps-responses : The List NPS Responses API allows you to retrieve a list of Net Promoter Score (NPS) responses from the Teamtailor platform.

Partner Results

• GET https://api.teamtailor.com/v1/partner-results : The List Partner Results API allows users to retrieve a list of partner results from the Teamtailor platform.
• GET https://api.teamtailor.com/v1/partner-results/<partner result uuid> : This API retrieves lists of answers by fetching partner results using a unique partner result UUID.

Picked Questions

• GET https://api.teamtailor.com/v1/picked-questions : The List Picked Questions API retrieves a list of picked questions from the Teamtailor platform.

Questions

• GET https://api.teamtailor.com/v1/questions : This API endpoint retrieves a list of questions from the Teamtailor platform.

Referrals

• GET https://api.teamtailor.com/v1/referrals : The List Referrals API allows users to retrieve a list of referrals from the Teamtailor platform.
• GET https://api.teamtailor.com/v1/referrals/{id} : The Show Region API retrieves details of a specific referral by its ID.

Regions

• POST https://api.teamtailor.com/v1/regions : This API endpoint allows the creation of a new region in the system.

Reject Reasons

• POST https://api.teamtailor.com/v1/reject-reasons : The Create Reject Reason API allows users to create a new reject reason in the system.
• DELETE https://api.teamtailor.com/v1/reject-reasons/{id} : The Delete Reject Reason API allows you to delete a specific reject reason by its ID.

Requisition Step Verdicts

• GET https://api.teamtailor.com/v1/requisition-step-verdicts/{id} : The 'Show Requisition Step Verdicts' API retrieves the details of a specific requisition step verdict by its ID.

Requisitions

• GET https://api.teamtailor.com/v1/requisitions : The List Requisitions API allows users to retrieve a list of job requisitions from the Teamtailor platform.
• GET https://api.teamtailor.com/v1/requisitions/{id} : The Show Requisition Details API retrieves detailed information about a specific requisition identified by its unique ID.

Roles

• GET https://api.teamtailor.com/v1/roles : The List Roles API allows users to retrieve a list of roles from the Teamtailor platform.
• DELETE https://api.teamtailor.com/v1/roles/{id} : This API endpoint is used to delete a role in the system.

Stage Types

• GET https://api.teamtailor.com/v1/stage-types : The List Stage Types API retrieves a list of stage types from the Teamtailor platform.
• GET https://api.teamtailor.com/v1/stage-types/{id} : The 'Show Stage Type' API retrieves details of a specific stage type identified by its ID.

Stages

• GET https://api.teamtailor.com/v1/stages : This API endpoint retrieves lists of stages from the Teamtailor platform.
• GET https://api.teamtailor.com/v1/stages/{id} : The Get Stage Details API retrieves detailed information about a specific stage in the Teamtailor system.

Team Memberships

• GET https://api.teamtailor.com/v1/team-memberships : The List Team Memberships API allows you to retrieve a list of team memberships from the Teamtailor platform.
• GET https://api.teamtailor.com/v1/team-memberships/{id} : The Show Team Membership API retrieves details of a specific team membership by its ID.

Teams

• GET https://api.teamtailor.com/v1/teams : The List Teams API allows you to retrieve a list of teams from the Teamtailor platform.
• DELETE https://api.teamtailor.com/v1/teams/{id} : This API endpoint is used to delete a team from the system.

Todos

• POST https://api.teamtailor.com/v1/todos : The Create Todo API allows users to create a new todo item in the system.
• PATCH https://api.teamtailor.com/v1/todos/{id} : The Update Todo API allows you to update the details of a specific todo item by its ID.

Triggers

• GET https://api.teamtailor.com/v1/triggers : This API endpoint retrieves lists of triggers from the Teamtailor platform.

Uploads

• GET https://api.teamtailor.com/v1/uploads : This API endpoint retrieves lists of uploads from the Teamtailor platform.

Users

• POST https://api.teamtailor.com/v1/users : This API endpoint allows the creation of a new user in the Teamtailor system.
• DELETE https://api.teamtailor.com/v1/users/{id} : The Delete User API allows an admin to delete a user from the system.

Teamtailot API FAQ's

Leverage Knit for Teamtailor API Integration

For quick and seamless access to Teamtailor API, Knit API offers a convenient Unified API solution. By integrating with Knit just once, you can go live with multiple ATS integrations in one go. Knit takes care of all the authentication, authorization, and ongoing integration maintenance, this approach not only saves time but also ensures a smooth and reliable connection to your Teamtailor API.

Employment Hero is a comprehensive cloud-based human resources software solution designed to cater to the needs of small and medium-sized businesses. As an all-in-one platform, it centralizes a variety of HR functions, including hiring, HR management, payroll, and employee engagement, making it an essential tool for businesses looking to streamline their HR processes. The software is highly customizable, allowing organizations to create an integrated Human Resource Information System (HRIS) and payroll system that aligns with their specific requirements. This flexibility ensures that businesses can efficiently manage their workforce while focusing on growth and productivity.

One of the standout features of Employment Hero is its ability to manage the entire employee lifecycle. From recruitment and onboarding to payroll, time and attendance, and people management, the platform offers a suite of modules designed to simplify HR tasks. Additionally, Employment Hero can integrate seamlessly with other HR and payroll software, facilitating the handling of employee data such as new hires, salary adjustments, and benefit deductions. This integration capability, particularly through the Employment Hero API, is crucial for businesses aiming to consolidate their HR operations into a single, cohesive system, thereby enhancing efficiency and reducing administrative burdens.

Key highlights of Employment Hero APIs

Employment Hero offers a suite of APIs designed to facilitate seamless integration with its HR and payroll platforms. These APIs enable developers to automate processes, manage employee data, and integrate various HR functionalities into their applications. Below is an overview of the available APIs, their functionalities, authentication methods, and rate limits.

Available APIs and Functionalities:

1. HRIS API:
   • Functionality: Provides access to core HR data, including employee records, organizational information, and related HR functionalities.
   • Use Cases: Automating employee onboarding, updating employee details, and retrieving organizational data.
   • Documentation: Employment Hero API - Australia
2. Payroll API:
   • Functionality: Offers endpoints to manage payroll operations, such as processing payroll, managing deductions, and generating payslips.
   • Use Cases: Automating payroll calculations, integrating with accounting systems, and retrieving payroll reports.
   • Documentation: Employment Hero Payroll API Reference - KeyPay
3. Careers Page API:
   • Functionality: Allows integration of Employment Hero's careers page with an organization's website, ensuring job listings are synchronized.
   • Use Cases: Displaying current job openings on a company's website and automating job posting updates.
   • Documentation: API Reference 1 - Australia

Authentication Methods:

• OAuth 2.0:
  • Employment Hero's APIs primarily use OAuth 2.0 for secure authentication. Developers must register their applications through the Employment Hero Developer Portal to obtain client credentials (Client ID and Client Secret). The authentication flow involves obtaining an access token, which is then used to authorize API requests.
  • Steps:
    1. Register your application in the Developer Portal.
    2. Obtain client credentials.
    3. Authorize the application to receive an authorization code.
    4. Exchange the authorization code for an access token.
    5. Use the access token to authenticate API requests.
  • Reference: API Reference - Australia
• API Key Authentication:
  • Some APIs, like the Careers Page API, utilize API key authentication. Users can generate an API access token within the Employment Hero platform, which is then included in the request headers to authenticate API calls.
  • Reference: API Reference 1 - Australia

Rate Limits:

Employment Hero enforces rate limits to ensure fair usage and maintain system performance. While specific rate limits may vary across different APIs, it's essential to implement error handling for potential rate limiting responses. Developers are advised to consult the respective API documentation or contact Employment Hero support for detailed rate limit information.

Additional Resources:

• Developer Portal: Access to API documentation, client registration, and other developer tools.some text
  • Employment Hero API - Australia
• Payroll API Reference: Detailed documentation for payroll-related API endpoints.some text
  • Employment Hero Payroll API Reference - KeyPay

For comprehensive information and to get started with the Employment Hero API, refer to their official API documentation. Employment Hero Developer

‍

Employment Hero API Endpoints

Organisation APIs

• GET     https://api.employmenthero.com/api/v1/organisations : This API     retrieves a list of organisations from Employment Hero. The request must     include an access token in the Authorization header, which is obtained     from the Employment Hero Authorisation Server. The API uses the GET method     and the endpoint is https://api.employmenthero.com/api/v1/organisations.     The response includes details of each organisation such as its unique     identifier, name, creation date, and last update date.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id :     This API endpoint retrieves detailed information about a specific     organisation using its UUID. The request requires an Authorization header     with a bearer token for authentication. The response includes various     details about the organisation such as its name, phone number, country,     logo URL, primary address, end of week, typical work day, payroll admin     emails, subscription plan, superfund name, employee counts, time zone, and     creation date.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/certifications :     This API endpoint retrieves a list of all certifications for a specific     organisation identified by the organisation_id. The request requires an     Authorization header with a bearer token for authentication. The response     includes a data object containing an array of certification objects, each     with an id, name, and status. Pagination details such as items per page,     current page index, total pages, and total items are also provided in the     response.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/custom_fields :     This API endpoint retrieves a list of all custom fields for a specific     organisation. The request requires an 'organisation_id' as a path     parameter and an 'Authorization' header with a bearer token. The response     includes a data object containing an array of custom field objects, each     with details such as id, name, hint, description, type, onboarding status,     requirement status, permissions, and options. The response also provides     pagination details including items per page, current page index, total     pages, and total items.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/policies :     This API endpoint retrieves a list of all policies for a specific     organisation identified by the organisation_id. The request requires an     Authorization header with a bearer token for authentication. The response     includes a data object containing an array of policy objects, each with     details such as id, name, induction status, and creation date. Pagination     details such as items per page, current page index, total pages, and total     items are also provided.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/teams :     The 'Get All Teams' API retrieves an array of all teams associated with a     specified organisation. The request requires an 'organisation_id' as a     path parameter and an 'Authorization' header with a bearer token for     authentication. The response includes a 'data' object containing an array     of team objects, each with an 'id', 'name', and 'status'. Additionally,     pagination details such as 'item_per_page', 'page_index', 'total_pages',     and 'total_items' are provided.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/leave_requests :     The Get Leave Requests API retrieves a list of all leave requests for a     specified organisation. The request requires an organisation_id as a path     parameter and an Authorization header with a bearer token for     authentication. The response includes a data object containing an array of     leave request objects, each with details such as id, start_date, end_date,     total_hours, comment, status, leave_balance_amount, leave_category_name,     reason, and employee_id.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/rostered_shifts :     The 'List Rostered Shifts' API allows users to retrieve all rostered     shifts accessible by the current user within a specified organisation. The     API supports pagination and filtering by various parameters such as date     range, shift statuses, location IDs, member IDs, and more. The request     requires an organisation ID as a path parameter and an authorization     bearer token in the headers. Optional query parameters include from_date,     to_date, statuses, location_ids, member_ids, unassigned_shifts_only, and     exclude_shifts_overlapping_from_date. The response includes a list of     shifts with details such as start and end times, status, location, member     information, and shift swap details.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/unavailabilities :     The List Unavailabilities API retrieves all unavailability records that     match the specified conditions for a given organisation. The API requires     an organisation ID as a path parameter and supports optional query     parameters such as from_date, to_date, location_id, member_id,     item_per_page, and page_index to filter the results. The response includes     a list of unavailability records with details such as member ID,     description, start and end dates, and recurring patterns. The API uses a     bearer token for authorization.

Employee APIs

• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees :     The Get Employees API retrieves an array of all employees managed by a     specified organisation. The API requires an organisation ID as a path     parameter and an authorization bearer token in the headers. The response     includes a data object containing an array of employee objects, each with     detailed information such as ID, email, name, address, job title, and     more. If there are no employees, the array will be empty.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees/:employee_id :     This API endpoint retrieves a specific employee's details from the     Employment Hero platform. It requires the organisation ID and employee ID     as path parameters, and an authorization bearer token in the headers. The     response includes detailed information about the employee, such as their     name, contact details, job title, and associated managers and cost     centres.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees/:employee_id/bank_accounts :     This API endpoint retrieves a list of all bank accounts for a specific     employee within an organisation. The request requires the organisation_id     and employee_id as path parameters, and an Authorization header with a     bearer token for authentication. The response includes a data object     containing an array of bank account records, each with details such as     account name, account number, BSB, amount, and whether it is the primary     account. Pagination details such as items per page, page index, total pages,     and total items are also provided.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees/:employee_id/certifications :     This API endpoint retrieves a list of all certifications assigned to a     specific employee within an organisation. The request requires the     organisation_id and employee_id as path parameters, and an Authorization     header with a bearer token for authentication. The response includes a     data object containing an array of certification items, each with details     such as certification ID, name, type, expiry date, completion date,     status, and any driver problems. The response also includes pagination     details like items per page, current page index, total pages, and total     items.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees/:employee_id/custom_fields :     This API endpoint retrieves a list of all custom fields for a specific     employee within an organisation. It requires the organisation_id and     employee_id as path parameters and an Authorization header with a bearer     token for authentication. The response includes a data object containing     an array of employee custom field objects, each with properties such as     id, value, name, description, and type. The response also includes     pagination details like items per page, current page index, total pages,     and total items.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees/:employee_id/emergency_contacts :     This API endpoint retrieves a list of all emergency contacts for a     specific employee within an organisation. It requires the organisation_id     and employee_id as path parameters, and an Authorization header with a     bearer token for authentication. The response includes a data object     containing an array of emergency contact objects, each with details such     as contact name, contact numbers, relationship, and contact type.     Pagination details such as items per page, page index, total pages, and     total items are also provided.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees/:employee_id/employment_histories :     This API endpoint retrieves the complete employment history for a specific     employee within a given organisation. It requires the organisation_id and     employee_id as path parameters and an Authorization header with a bearer     token for authentication. The response includes an array of employment     history records, each containing details such as the position title, start     and end dates, and employment type. The response also provides pagination     details including items per page, current page index, total pages, and     total items.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees/:employee_id/pay_details :     This API endpoint retrieves a list of all pay details for a specific     employee within a given organisation. The request requires the     organisation_id and employee_id as path parameters, and an Authorization     header with a bearer token for authentication. The response includes a     data object containing an array of pay details, each with properties such     as id, effective_from, classification, industrial_instrument,     pay_rate_template, anniversary_date, salary, salary_type, pay_unit,     pay_category, leave_allowance_template, change_reason, and comments. The     response also includes pagination details like item_per_page, page_index,     total_pages, and total_items.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees/:employee_id/payslips :     This API endpoint retrieves a list of all payslips for a specific employee     within an organisation. It requires the organisation ID and employee ID as     path parameters, and an Authorization header with a bearer token for     authentication. The response includes a data object containing an array of     payslip records, each with details such as employee name, total     deductions, net pay, wages, tax, and other payroll-related information.     The response also includes pagination details like items per page, current     page index, total pages, and total items.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees/:employee_id/superannuation_detail :     This API endpoint retrieves the superannuation detail for a specific     employee within a given organisation. It requires the organisation_id and     employee_id as path parameters, and an Authorization header with a bearer     token. The response includes details such as the fund name, member number,     product code, and other relevant superannuation account information. If     the employee does not have a superannuation detail, a not found error will     be returned.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees/:employee_id/tax_declaration :     The Get Tax Declaration Detail API retrieves the tax declaration details     for a specific employee within an organisation. It requires the     organisation_id and employee_id as path parameters, and an Authorization     header with a bearer token. The response includes details such as the     employee's first and last name, tax file number, residency status, and any     applicable tax debts. If no tax declaration is found, a not found error is     returned.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/employees/:employee_id/timesheet_entries :     The Get Timesheet Entries API retrieves a list of all timesheet entries     for a specific employee within an organisation. The API requires the     organisation ID and employee ID as path parameters. The employee ID can be     a specific UUID or '-' to retrieve timesheets for all employees. Optional     query parameters include start_date and end_date to filter the timesheet     entries by date range. The request must include an Authorization header     with a bearer token. The response contains a data object with an array of timesheet     entries, each entry includes details such as date, start and end times,     status, units, and associated cost centre.
• GET     https://api.employmenthero.com/api/v1/organisations/:organisation_id/teams/:team_id/employees :     This API retrieves all employees associated with a specific team within a     managed organization. It requires the organization ID and team ID as path     parameters and an authorization bearer token in the headers. The response     includes an array of employee objects, each containing detailed     information such as ID, email, name, job title, and more. The response     also provides pagination details like items per page, current page index,     total pages, and total items.

Authorization APIs

• GET     https://oauth.employmenthero.com/oauth2/authorize : The Obtain     Access Token API is used to initiate the OAuth 2.0 authorization process     to obtain an access token for accessing private data over the Employment     Hero API. The API requires the client ID, redirect URI, and response type     as query parameters. The user will be prompted to log in and grant     permissions, after which they will be redirected to the specified redirect     URI with an authorization code. This code can then be used to obtain the     access token. The Employment Hero account used for authorization can only     access data within its roles or permissions.
• POST     https://oauth.employmenthero.com/oauth2/token : The Refresh     Access Token API is used to obtain a new access token using a refresh     token. Access tokens expire after 15 minutes, and this API allows for     continuous usage by providing a new access token and a new refresh token,     invalidating the previous refresh token. The API requires the client_id,     client_secret, grant_type, and refresh_token as query parameters. The     response includes a new access_token, refresh_token, token_type,     expires_in, and scope.

Employment Hero API FAQs

How can I access the Employment Hero API?

• Answer: To access the Employment Hero API, you need to have a Platinum subscription or higher. Once subscribed, you can register your application through the Employment Hero Developer Portal to obtain client credentials, including a Client ID and Client Secret. These credentials are necessary for authenticating your API requests using the OAuth 2.0 protocol.
• Source: API Reference - Australia

What authentication method does the Employment Hero API use?

• Answer: The Employment Hero API utilizes the OAuth 2.0 protocol for secure authentication. After registering your application and obtaining client credentials, you will perform an authorization flow to receive an access token. This token must be included in the Authorization header of your API requests.
• Source: API Reference - Australia

Are there rate limits for the Employment Hero API?

• Answer: Yes, Employment Hero enforces rate limits to ensure fair usage and maintain system performance. While specific rate limits are not publicly detailed, it's recommended to implement error handling for potential rate limiting responses and to contact Employment Hero support for detailed rate limit information.
• Source: Employment Hero API - Australia

Can I retrieve employee data using the Employment Hero API?

• Answer: Yes, the Employment Hero API provides endpoints to retrieve employee data. For example, you can use the /v1/employees endpoint to fetch a list of employees. Ensure that your application has the necessary scopes and permissions to access this data.
• Source: API Reference - Australia

Does the Employment Hero API support webhooks for real-time data updates?

• Answer: As of the latest available information, the Employment Hero API does not natively support webhooks. For real-time data updates, consider implementing periodic polling or integrating with third-party services that provide webhook functionality.
• Source: Employment Hero API - Australia

Get Started with Employment Hero API Integration

Knit API offers a convenient solution for quick and seamless integration with Employment Hero API. Our AI-powered integration platform allows you to build any Employment Hero API Integration use case. By integrating with Knit just once, you can integrate with multiple other CRM, Accounting, HRIS, ATS, and other systems in one go with a unified approach. Knit handles all the authentication, authorization, and ongoing integration maintenance. This approach saves time and ensures a smooth and reliable connection to Employment Hero API.‍

To sign up for free, click here. To check the pricing, see our pricing page.

Oracle Fusion Cloud HCM API Directory

Oracle Fusion Cloud HCM is a cloud-based human resource solution provider which seeks to connect every aspect of the human resources process. It seeks to help enterprises with critical HR functions including, recruiting, training, payroll, compensation, and performance management to drive engagement, productivity, and business value. As a market leader, it allows developers to use Oracle REST APIs to access, view and manage data stored in Oracle Fusion Cloud HCM

Oracle Fusion Cloud HCM API Authorization

Oracle Fusion Cloud HCM API uses authorization to define which users can access the API and relevant information. To get this access, users need to have predefined roles and the necessary security privileges. Oracle’s REST APIs are secured by function and aggregate security privileges, delivered through job roles which are predefined. However, users can also create custom roles to provide access. Authorization and access to Oracle Fusion Cloud HCM API depends on the role of a person and the level of access offered. 

Oracle Fusion Cloud HCM API Objects, Data Models & Endpoints

To get started with Oracle Fusion Cloud HCM API, it is important to understand the end points, data models and objects and make them a part of your vocabulary for seamless access and data management.

Application Management

• POST https://<hostname>.com/odata/v2/upsert : The Update Application Stage API allows users to update the stage of a specific application by providing the application ID and the target stage ID. The request requires an Authorization header with a Bearer token unless accessed through knit. The response includes the status of the update operation, a message indicating success, and the HTTP status code.

Employee Information

• GET https://{{server_url}}/hcmRestApi/resources/{resource_id}1.13.18.05/absences : The 'Get leave requests of an employee' API retrieves the leave requests for a specific employee. It requires an Authorization header for Basic Authentication unless accessed through knit. The API accepts optional query parameters 'offset' and 'limit' to paginate the results. The response includes a list of leave requests with detailed information such as absence type, status, duration, and associated metadata. The response body contains an array of leave request items, each with attributes like absenceTypeId, approvalStatusCd, startDate, endDate, and more, providing comprehensive details about each leave request.
• GET https://{{server_url}}/hcmRestApi/resources/{resource_id}1.13.18.05/benefitEnrollments : This API retrieves the benefit enrollments of a specific employee identified by the personId. It requires an Authorization header for Basic Authentication. The API supports pagination through the offset and limit query parameters. The response includes details such as EnrollmentResultId, PersonId, ProgramId, PlanTypeId, PlanId, OptionId, PersonName, and various dates related to the enrollment coverage. The response also indicates if there are more items to fetch with the hasMore flag.
• GET https://{{server_url}}/hcmRestApi/resources/{resource_id}1.13.18.05/documentRecords : The 'Get documents of an employee' API retrieves document records associated with an employee. It requires a Basic Authorization header unless accessed through knit. The API supports query parameters 'offset' and 'limit' to paginate results. The response includes detailed information about each document, such as document type, person details, and creation metadata. The response body contains an array of document records, each with attributes like 'DocumentsOfRecordId', 'DocumentType', 'PersonId', and more. The API also indicates if more records are available with the 'hasMore' flag.
• GET https://{{server_url}}/hcmRestApi/resources/{resource_id}1.13.18.05/locations : This API retrieves all locations associated with an employee. It requires an Authorization header for Basic Authentication, unless accessed through knit. The API supports query parameters 'offset' and 'limit' to paginate through the results. The response includes a list of location objects with details such as LocationId, SetId, ActiveStatus, and various flags indicating the type of site. Additional information like address details, effective dates, and creation/update timestamps are also provided.
• GET https://{{server_url}}/hcmRestApi/resources/{resource_id}1.13.18.05/salaries : The 'Get compensation information of an employee' API retrieves detailed salary information for a specified employee. The API requires an Authorization header for Basic Authentication, unless accessed through knit. It accepts optional query parameters 'offset' and 'limit' to paginate the results. The response includes a list of salary details such as AssignmentId, SalaryId, SalaryAmount, CurrencyCode, and more, along with metadata like count, hasMore, limit, and offset. The API provides comprehensive salary data including frequency, basis, and range details, as well as action and person-related information.
• GET https://{{server_url}}/hcmRestApi/resources/{resource_id}1.13.18.05/workers : The 'List all employees' API retrieves a list of employees from the specified server URL. It requires an Authorization header with a Bearer token unless accessed through knit. The API supports optional query parameters 'offset' and 'limit' to paginate the results. The response includes a list of employee objects with details such as PersonId, PersonNumber, and metadata like CreatedBy and LastUpdateDate. The response also contains links for navigation and indicates if more employees are available with the 'hasMore' field.
• GET https://{{server_url}}/hcmRestApi/resources/{resource_id}1.13.18.05/workers/{{workersUniqID}}/child/nationalIdentifiers : This API retrieves the identification information of an employee using their unique worker ID. The request requires an Authorization header for Basic Auth, unless accessed through knit. The API accepts optional query parameters 'offset' and 'limit' to paginate the results. The response includes a list of national identifiers with details such as NationalIdentifierId, LegislationCode, NationalIdentifierType, and more. The response also indicates if there are more items to fetch with 'hasMore'.
• GET {{base_url}}/workers : The 'List Details of All Employees' API retrieves detailed information about all employees. It requires an Authorization header with Basic authentication credentials. The API supports an optional query parameter 'expand' to specify which related fields to include in the response, such as addresses, emails, legislative information, phones, names, work relationships, and more. The response includes a success flag, a message containing headers and a body with detailed employee information, including personal details, addresses, emails, legislative info, names, national identifiers, phones, photos, and work relationships. The response also includes pagination details like count, hasMore, limit, and offset.

Check out this detailed guide for all endpoints and data models

Oracle Fusion Cloud HCM API Use Cases

• Seamless end-to-end HR process management including, hiring, onboarding, managing, and engaging workforce aligned with global compliances
• Flexible programs to meet specific benefit requirements and the option to calculate and manage benefit plans for each employee group
• Predictive analytics for workflow planning based on risk of leaving, managing team performance and retaining your best performers.
• Advanced reporting helping teams create, manage, and visualize data from Microsoft Excel within Oracle HCM
• Secure, self-service, mobile-responsive options for employees to manage personal data, PTO, payslips, and more

Top customers

12,000+ companies use Oracle Fusion Cloud HCM as their preferred HR tool, including:

• ArcelorMittal S.A., a Luxembourg-based multinational steel manufacturing corporation
• The Deutsche Bahn AG, the national railway company of Germany
• Fujifilm Holdings Corporation, a Japanese company operating in photography, optics, office and medical electronics, biotechnology, and chemicals
• Hormel Foods Corporation, an American food processing company
• Sofigate, a leading business technology transformation company in the Nordics

Oracle Fusion Cloud HCM API FAQs

To better prepare for your integration journey with Oracle Fusion Cloud HCM API, here is a list of FAQs you should go through:

• How to properly paginate in the API for Oracle Fusion Cloud HCM? Answer
• What to do when Oracle Fusion HCM cannot get data from Rest api /workers? Answer
• How to GET Employee Absences data from HCM Fusion by sending two dates in REST API query parameter? Answer
• How to include multiple query parameters in HCM cloud rest Get call? Answer
• How to get Workers by HireDate in Oracle HCM Cloud API? Answer
• How to pull the latest record when there are multiple records with different dates in Oracle HCM? Answer
• How to use SQL Developer with BIPublisher Oracle Cloud HCM? Answer
• How do I get previous data with respect to effective date in Oracle HCM cloud reporting in a separate column? Answer
• What applications that Integrate with Oracle's PeopleSoft Enterprise Human Capital Management? Answer
• Where are Oracle Fusion Assets REST APIs? Answer

How to integrate with Oracle Fusion Cloud HCM API

To integrate with Oracle Fusion Cloud HCM API, ensure that you review the basics and have an understanding of REST APIs. Then get your Fusion Applications Account Info, including username and password. Configure your client, authorize and authenticate and then send an HTTP request and you’re all set to go. For a more detailed understanding of the best practices and a step-by-step guide to integrate with Oracle Fusion Cloud HCM API, check out this comprehensive guide. 

Get started with Oracle Fusion Cloud HCM API

While integrating with Oracle Fusion Cloud HCM API can help businesses seamlessly view, access and manage all HR data, the process of integration can be tricky. Right from building the integration in-house which requires API knowledge, developer bandwidth and much more to managing the integrations, there are several steps in the way. Naturally, the entire integration lifecycle can turn out to be quite expensive as well. Fortunately, companies today can leverage and integrate with a unified HRIS API like Knit, which allows them to connect with multiple HRIS applications, without the need to integrate with each one individually. Connect for a discovery call today to understand how you can connect with Oracle Fusion Cloud HCM API and several other HRIS applications faster and in a cost-effective manner. 

To get started with Knit for Oracle HCM or any other integrations setup a demo here

‍