Scheduling and meetings decision
Wudpecker
A capable engineer can replicate the core meeting-capture, transcription, diarization and summarization workflow using open-source components and the referenced prior-art; the vendor's polished UI, integrations, EU-managed hosting and operational polish are the main things you'd lose.
Visit website↗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
- Join meetings, capture audio, transcribe and diarize speakers, generate structured notes/summaries, store recordings and send notes to integrations (Slack/Notion/HubSpot).
What it still won’t have
- Polished cross-platform UI and mobile/desktop apps
- Refined product integrations and ongoing beta feature rollouts
- EU-hosted managed infrastructure and vendor security operations
- Operational reliability, scaling, and monitoring the vendor provides
- Ongoing model tuning and proprietary prompt engineering
What remains hard
- Product polish and ongoing maintenance
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 14 seats.
Money you would actually spend
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
Build a minimal self-hosted AI meeting assistant using: Node.js + Express backend, PostgreSQL for metadata, S3-compatible object store for recordings, Redis for queues, a React web UI. Scope in-scope: OAuth calendar integration (Google Calendar), join Zoom/Meet/Teams by accepting meeting links (webhook/invite flow), record/upload audio, run open-source ASR (Whisper/ faster alternative) with speaker diarization, run an open-source summarization model to produce structured notes and action items, store transcripts and recordings, and export notes to Slack and Notion via their APIs. Out of scope: native desktop/mobile apps, multi-tenant billing, advanced analytics, and production-grade autoscaling. Require: error handling for failed transcriptions, retries for integration webhooks, access control per user, automated tests for transcription pipeline and integration endpoints, and basic CI to deploy to a single VM. Provide deployment scripts (Docker Compose) and monitoring alerts (logs + health endpoint).
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 4 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 · 4
Every page the run actually retrieved.
- official productWudpecker — product
- official pricingWudpecker — pricing
- open sourceVexa-ai/vexa
- open sourceZackriya-Solutions/meetily
Integrity checks
What held up, and what did not.
