Build a Meeting Notes Agent

Automatically transcribe calls, extract action items, and route follow-ups to the right person without any manual note-taking.

Every post-meeting note document is a manual tax on the person who ran the meeting. Someone has to write a summary, decide what counts as an action item, remember who owns it, and then track whether it actually happens. Consultants and ops leads at small firms do this after every call, often from memory 20 minutes later. The hard part is not the transcription. Whisper and AssemblyAI can handle that. The hard part is structured extraction: turning a 45-minute conversation into a clean list of owners, deadlines, and decisions that maps onto your actual CRM and project tool. That extraction step is where most cobbled-together pipelines break down, because it requires a language model that understands your firm's vocabulary and deal stages, not just generic meeting patterns.

The stack

Each pick is a real entry on the index. Click any one for the full detail page.

1

openai-whisper

Whisper runs locally or via API and handles the raw audio-to-text step with high accuracy across accents and domain vocabulary. For a meeting notes agent, you need transcription that can be triggered automatically on a recording file without a per-seat SaaS subscription. Whisper gives you that without locking every team member into a paid account.
2

claude-claude-sonnet

The extraction step requires a model that can read a full transcript and reliably output structured JSON: action items with owner names, due dates, and decision summaries. Claude Sonnet handles long transcripts in a single context window and follows structured output instructions consistently, which matters when the downstream step is writing to a CRM or project tool automatically.
3

notion-mcp

Notion MCP lets the agent write the structured summary directly into a meeting notes database without a separate API integration layer. For teams already living in Notion, this means the output lands exactly where people look for context, not in a separate tool they have to remember to check.
4

google-calendar-mcp

Calendar access lets the agent know which meeting just ended, who attended, and what the stated agenda was before it even reads the transcript. That pre-call context dramatically improves extraction accuracy because the model can match vague pronoun references to actual participant names and align action items to the right project.
5

P Apps Orchestration

n8n

by Various

Why this: The pipeline has four sequential steps: recording lands in storage, Whisper runs, Claude extracts, output routes to Notion and CRM. n8n handles that sequencing with error handling and retry logic without requiring custom code for each connector. For a meeting notes agent that runs after every call automatically, you need that orchestration layer to be reliable and auditable.
Full entry
6

supabase

Action items need a queryable home separate from the narrative notes. Storing them in Supabase lets you build a weekly digest query, flag overdue items, or feed them into a CRM follow-up sequence. Notion is good for reading; Supabase is better when you need to run a query across 30 meetings to see which items have gone stale.

Why we picked this stack

Get this running with Enterprise DNA.

Enterprise DNA's Omni Command Centre connects these tools at the workflow layer rather than the integration layer. Secrets for your Whisper endpoint, Notion workspace token, and Supabase credentials live in a single secrets store managed inside your Command Centre, so credentials rotate in one place rather than across five separate tool settings pages. Action items extracted by the agent feed directly into OPM work items with owner assignments, which means your weekly project review in Omni Project Manager already reflects what was agreed on the call. The inbox layer surfaces any action item that bounced or was assigned to a contact not yet in your CRM, so nothing falls through after a discovery call or client check-in.

Run this stack on Enterprise DNA

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