Extract lead details during chatbot conversations
When a new lead is captured in chat via Mapify, automatically run the conversation text through Token Metrics to classify intent and extract key contact metadata.
See all Mapify + Token Metrics integrations or talk to a human
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How Knit builds this workflow
The Agent researches both APIs and wires the trigger on one side to the action on the other — no pre-built connector required. Here is the vocabulary it has to work with.
Mapify Explore Mapify →
TriggersFires when a new chat or conversation begins in Mapify.
Fires when Mapify captures a lead during a conversation.
Fires when Mapify escalates a conversation to a human.
Sends a message to a user through Mapify.
Asks Mapify a question against its knowledge base and returns the answer.
Uses Mapify to summarize a chat transcript.
Opens a support ticket from an Mapify conversation.
Token Metrics Explore Token Metrics →
TriggersFires when Token Metrics generates a response to a prompt.
Fires when a request to Token Metrics errors or times out.
Fires when a fine-tuning job in Token Metrics finishes.
Sends a prompt to Token Metrics and returns the model's response.
Uses Token Metrics to summarize a block of text.
Uses Token Metrics to classify or extract structured data from text.
More Mapify + Token Metrics workflows
Send custom completions back to chat visitors
When a conversation starts in Mapify, generate a completion in Token Metrics and send a tailored message directly back to the visitor.
See details
Alert support agents when model fine-tuning completes
When a fine-tune job completes in Token Metrics, automatically send a message through Mapify to notify AI administrators that the updated model is ready.
See details
See all Mapify + Token Metrics integrations → · Browse the full workflow library →
Common questions
Can Knit build “Extract lead details during chatbot conversations” between Mapify and Token Metrics?
Yes — describe it in the box above and Knit's Integrations Agent researches Mapify and Token Metrics's public API docs (or your own uploaded docs) and builds a working workflow, whether or not either app already has a pre-built connector.
How long does it take to build?
Minutes to a first working version, not weeks — you test it against real data before it goes anywhere near production.
How does connecting Mapify to Token Metrics expand chatbot capabilities?
Connecting Mapify to Token Metrics allows your chatbot to tap into advanced AI models for deeper context, sentiment classification, and sophisticated text generation beyond standard scripted responses.
Can Mapify notify operators if a backend model process encounters an issue in Token Metrics?
Yes, when a model run fails in Token Metrics, you can send an automated message or alert through Mapify to notify system administrators.