Classify incoming support tickets automatically with AI
When a new ticket is created in Infraspeak, automatically analyze its text and extract key metadata using Deepgram to streamline triage.
See all Infraspeak + Deepgram 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.
Infraspeak Explore Infraspeak →
TriggersFires when a new support ticket or chat conversation starts in Infraspeak.
Fires when a ticket's status changes in Infraspeak.
Fires when a ticket in Infraspeak is assigned to an agent.
Fires when a ticket in Infraspeak breaches its response SLA.
Creates a new ticket in Infraspeak.
Updates a ticket's status in Infraspeak.
Assigns a ticket in Infraspeak to an agent.
Posts a reply or internal note on a ticket or conversation in Infraspeak.
Deepgram Explore Deepgram →
TriggersFires when Deepgram returns a completion, prediction or other result.
Fires when a request to Deepgram errors or times out.
Fires when a fine-tuning job in Deepgram finishes.
Sends a prompt to Deepgram and returns the model's response.
Sends text, an image or other data to Deepgram and returns the model's output.
Uses Deepgram to classify or extract structured data from text or images.
More Infraspeak + Deepgram workflows
Create engineering tickets when AI model runs fail
If an AI model run fails in Deepgram, automatically create an engineering ticket in Infraspeak containing the failure diagnostics.
See details
Add AI resolution summaries to ticket notes
When a completion is generated in Deepgram, append the AI-crafted resolution summary as an internal comment on the target ticket in Infraspeak.
See details
See all Infraspeak + Deepgram integrations → · Browse the full workflow library →
Common questions
Can Knit build “Classify incoming support tickets automatically with AI” between Infraspeak and Deepgram?
Yes — describe it in the box above and Knit's Integrations Agent researches Infraspeak and Deepgram'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.
What's the most common AI Models + Ticketing automation?
Having Deepgram draft a suggested reply or summary for a new ticket in Infraspeak, or auto-suggest a priority and category based on the ticket's content.
Does a human still review the AI's suggestion before it's sent?
That's up to how you configure it — the Agent can draft a reply for a human to approve, or, for lower-stakes categorization, act directly on Infraspeak.