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Transcripts → tasks

Paste a meeting transcript, AI extracts action items, you review and one-click create tasks. Works with Fireflies, Otter, Granola, Zoom AI Companion, or hand-typed notes.

The biggest pain in the r/projectmanagement community survey was the closed loop from meeting notes to a tracked task. Most AI tools generate meeting summaries; few of them push the action items into your sprint board. sprintrr does — without a meeting bot, without an OAuth dance, just paste and confirm.

How it works

  1. Open a project, click Extract from transcript in the Kanban toolbar.
  2. Paste the meeting transcript — anywhere from a 30-line snippet to ~60k characters (a full hour-long meeting).
  3. Pick Quality (Claude Sonnet 4.6) or Speed (Gemini Flash).
  4. The AI returns:
    • A short summary of what the meeting was about.
    • A list of proposed action items, each with title, description, priority, category, suggested due date, and (when named) the assignee hint.
    • A separate list of risks / blockers it flagged from the conversation.
  5. Review each proposal. Edit titles, untick anything wrong, pick the real milestone + assignee, then click Create N tasks — one round-trip, tasks land in the project.

The whole flow is transparent: every proposal carries a sourceQuote showing the verbatim line from the transcript it came from, so you can verify before committing.

Costs

1 generation credit per extraction — the same as a project generation. Creating the tasks afterwards is free (no second credit). On BYOK the whole flow is unlimited and runs on your own AI key.

What works well

  • Otter / Fireflies / Zoom AI Companion transcripts: paste the full transcript with speaker labels. Speaker attribution helps the AI guess assignees ("Sarah: I'll take the design review by Friday." → assignee hint: Sarah, due Friday).
  • Granola exports: copy the "Notes" pane. Granola already pre-summarises to a structured outline, which the extractor handles well.
  • Hand-typed notes: even rough bullets work. "- Sarah → design review, Fri" extracts cleanly.

What works less well

  • Transcripts with no clear speakers + no clear verbs ("we talked about X"). The extractor is tuned for precision over recall — it skips vague intents rather than inventing tasks.
  • Extremely long transcripts (60k+ chars get truncated). Split a half-day workshop into multiple extracts if needed.

MCP usage

For Claude Desktop / Cursor users, the same flow is exposed via MCP:

POST /api/mcp/transcripts/extract
{
  "projectId": "<uuid>",
  "transcript": "[paste here]",
  "mode": "quality"
}

→ { proposals: [...], risks: [...], summary: "...", meta: {...} }

Then to actually create:

POST /api/mcp/transcripts/create-tasks
{
  "projectId": "<uuid>",
  "tasks": [ { "title": "...", "priority": "High", ... }, ... ]
}

Requires the write:tasks scope (default for new MCP keys).

Trust + data

  • The AI only sees what you paste in. Nothing is auto-recorded from any external source.
  • BYOK calls go directly to the provider you chose with your own key — sprintrr never proxies the transcript anywhere it wasn't going.
  • Every extraction is logged in the activity feed with honest provider + model attribution (e.g. via Sprintrr Claude Sonnet 4.6).

Roadmap

  • Risks → first-class objects (M3) — auto-create the AI-flagged risks into a real RAID-lite register, with severity + probability + owner.
  • Optional transcript storage (M2.5) — opt-in to keep the source transcript attached to the project for audit / re-extract.
  • Bot ingestion (M2.5+) — Fireflies webhook, Zoom recording fetch, etc. Today only paste is supported by design (smaller security surface, no third-party dependency).
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