Process media uploads following fine-tune completions
When a fine-tune job completes in Token Metrics, automatically upload relevant audio files into Transkriptor for processing.
See all Transkriptor + 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.
Transkriptor Explore Transkriptor →
TriggersFires when a new meeting is recorded in Transkriptor.
Fires when an AI-generated summary is ready in Transkriptor.
Fires when a meeting transcript is ready in Transkriptor.
Fires when a new action item is captured from a meeting in Transkriptor.
Uploads an audio or video file to Transkriptor for transcription.
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 Transkriptor + Token Metrics workflows
Extract structured insights from meeting transcripts
When a new transcript is ready in Transkriptor, send the raw text to Token Metrics to automatically extract follow-up tasks and custom classifications.
See details
Condense meeting summaries with advanced AI models
When a meeting summary is generated in Transkriptor, pass it to Token Metrics to summarize text into executive bullet points.
See details
See all Transkriptor + Token Metrics integrations → · Browse the full workflow library →
Common questions
Can Knit build “Process media uploads following fine-tune completions” between Transkriptor and Token Metrics?
Yes — describe it in the box above and Knit's Integrations Agent researches Transkriptor 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 can custom AI models process meeting transcripts?
When a transcript becomes ready in Transkriptor, it can automatically pass to Token Metrics to extract specialized structured insights or key findings.
Can raw meeting audio be analyzed automatically?
Yes, when a fine-tune job or custom model run completes in Token Metrics, you can upload meeting media into Transkriptor for enhanced transcript generation.