SUMMARIZATION

Per-Transcript Feature-Request Extractor to Linear

When a new call transcript lands in Drive, extracts explicit feature requests and pain points, then files or de-dupes them as tagged issues in Linear.

CategorySummarization
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew transcript file added to Drive folderGoogle DriveGoogle Drive
  • ActionRead the transcript textGoogle DriveGoogle Drive
  • ActionExtract structured feature requestsOpenAI
  • LogicMatch each request to existing Linear issueLinearLinear
  • ActionCreate issue or append customer mentionLinearLinear
  • OutputEmit filed-vs-merged summary

What it does

Watches for each new transcript as it arrives and pulls out the concrete product asks buried in the conversation. For every distinct request it either opens a Linear issue or, if a matching one already exists, adds the new customer mention as a comment so demand stacks on a single ticket instead of fragmenting.

When to use it

Use it when you want feature signal captured the moment a call is transcribed, not batched weekly. Good for teams that run product discovery through Linear and want every customer ask traceable back to the call that produced it.

How it works

  1. 1A new file appearing in the Drive transcripts folder triggers the run.
  2. 2The transcript text is read.
  3. 3OpenAI extracts a structured list of feature requests, each with a one-line title, the verbatim quote, and the customer name.
  4. 4A logic step searches Linear for an existing issue matching each request title.
  5. 5If found, the new mention is appended as a comment; if not, a new issue is created with the customer-requests label.
  6. 6The flow emits a short summary of what was filed versus merged.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect Google DriveDocs, sheets, slides, files.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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