Vapi AssemblyAI

Vapi + AssemblyAI Integrations, Built by an AI Agent

Put Vapi + AssemblyAI to work with AI and enterprise-grade automation

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See the kind of workflows teams build with Vapi and AssemblyAI below — describe the exact one you want above and the Agent builds it on demand.

Vapi + AssemblyAI When a new call recording is generated in Vapi, send the audio to AssemblyAI for transcription. See details

Browse the full workflow library →

Looking to achieve something else with Vapi and AssemblyAI?

Just describe what you need — the Agent researches both APIs and builds the real automation on demand.

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Triggers & actions for Vapi and AssemblyAI

Everything Vapi and AssemblyAI can do inside a workflow — the triggers that start one and the actions that run when it fires.

Vapi Explore Vapi →

Vapi is a developer platform for building, testing, and deploying voice AI agents that make and answer phone calls.

Triggers
New Assistant

Triggers when a new assistant is created.

New Call

Triggers when a new call is created.

New File

Triggers when a new file is uploaded.

Actions
Create Call

Creates an outbound phone call using a Vapi voice assistant.

Create Chat

Create a new chat with Vapi.

Delete Call Data

Delete a call data. This action is permanent and cannot be undone.

Delete Chat

Delete a chat. This action is permanent and cannot be undone.

Delete File

Delete a file. This action is permanent and cannot be undone.

Update Call

Updates the metadata of an existing call using its call ID. Supports changes to the call name.

AssemblyAI Explore AssemblyAI →

AssemblyAI is an artificial intelligence speech recognition platform offering transcription, audio intelligence, and speech-to-text API models.

Actions
Delete Transcript

Remove the data from the transcript and mark it as deleted.

Get Paragraphs

Get the transcript split by paragraphs. The API will attempt to semantically segment your transcript into paragraphs to create more reader-friendly transcripts.

Get Sentences

Get the transcript split by sentences. The API will attempt to semantically segment the transcript into sentences to create more reader-friendly transcripts.

Get Subtitles

Export your transcript in SRT or VTT format to use with a video player for subtitles and closed captions.

Get Transcript

Get the transcript resource. The transcript is ready when the “status” is “completed”.

Prompt Transcript With LLM Gateway

Generates a response from a model given a prompt or a series of messages.

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Customer Stories

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Amazing Product With Exceptional Support from the team. By using Knit we managed to speed up our development by orders of magnitude.
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Huynh H
Developer, Multiplier
A brilliant tool to let you seamlessly integrate with many different systems. It is very easy to integrate with and customer support is next level.
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Jayesh
Co-Founder, Scrut
Seamless integration experience. Well-documented, self-explanatory APIs, and excellent service and support.
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Poorvi
Product Manager, Onsurity
Easy to integrate. Knit's dashboard is intuitive for setting up and testing integrations, and the sandbox is extremely useful during development.
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Aditya
Product Lead, RazorpayXPayroll
Knit shipped a brand-new Timesheet API in a week and handed us a ready-to-use NetSuite sandbox. That velocity is priceless when you're running payroll in 40+ countries.
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Rohit Singh
GM, Payoneer Workforce Management

#1 in Ease of Integrations

4.9 out of 5 stars

4.9 out of 5 stars on G2

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FAQ

Common questions about Vapi + AssemblyAI

Is there a pre-built Vapi + AssemblyAI automation?

Not yet, but Knit's Integrations Agent can build one — describe what you want and it researches Vapi and AssemblyAI's public API docs (or your own uploaded docs) and builds a working automation.

Does this require a pre-built Vapi or AssemblyAI connector?

No. The Agent works whether or not Vapi or AssemblyAI already has a pre-built connector in Knit's catalog — it researches the real API surface either way.

How long does it take to build?

Minutes to build a first working version, not weeks — you test it against real data before it goes anywhere near production.

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