AI assistants and search decision

Tana

A focused meeting notetaker that records, transcribes, and generates summaries/action items is realistic for a small team to build and run; reproducing Tana’s full product (native in-call agents, broad MCP integrations, enterprise compliance, and orchestration) is larger and better served by the hosted product.

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Subscription$20/month ✓ verified
Initial build30 hours
Monthly upkeep6 hours + $70
Evidence3/3 runs agree

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need — the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship.

What a replacement has to do

  • Record meeting audio, transcribe it, extract/summarize decisions and action items, store items in a simple searchable graph, and surface/send follow-ups.

What it still won’t have

  • Native in-call agent automation that acts during the video call
  • Deep, supported integrations (MCP/API-first ecosystem) and turnkey connectors
  • Enterprise security, compliance, and dedicated support (SOC2/HIPAA claims)
  • Multi-agent orchestration, skills, and the full knowledge-graph UX

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 4 seats.

Paid seatsseats

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

Time you would spend

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal agentic meeting notetaker as a web app using: React frontend, Node.js/Express backend, PostgreSQL, and Docker. Implement: (1) browser audio recording + upload endpoint; (2) async transcription via a speech-to-text API (e.g., Whisper/AssemblyAI) with timestamps; (3) LLM-based post-processing step to produce meeting summary, decisions, and action items (use OpenAI/GPT or other LLM API); (4) Postgres schema for meetings, participants, items, and a simple REST search endpoint; (5) outbound follow-up sender (SMTP) and optional Google Calendar task creation. Out of scope: building a full video conferencing client, multi-tenant enterprise SSO/SAML, SOC2/HIPAA attestation, and a marketplace of integrations. Include error handling, request retries, input validation, and automated tests for transcription processing and item extraction. Provide Docker compose for local dev and deployment notes for a single small VPS.
How we checked5 sources · 3/3 runs agreed · evidence score 67

How the score was reached

  • Partly verdict base52
  • An open-source build was found+5
  • 5 cited sources+3
  • Price verified on pricing page+3
  • 3/3 assessment runs agreed+4
  • Evidence score67

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.

How scoring works →

Cited sources · 5

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 1 moat recorded