Scheduling and meetings decision
Fellow
A small team or single developer can build a basic meeting transcription + summarization workflow (uploads, ASR, LLM summaries, UI), but reproducing Fellow’s enterprise-grade compliance, audit controls, broad conferencing integrations, and org-scale features is not realistic without substantial additional effort and investment.
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
- Upload/record meeting audio → transcribe audio → generate structured summary + action items → store transcript & summary → search/playback recordings
What it still won’t have
- Enterprise compliance certifications and audit-ready controls (SOC2/HIPAA/GDPR)
- Advanced admin controls (zero-day retention, transcript redaction, information barriers)
- 50+ out-of-the-box integrations (CRM, Slack, calendar platforms)
- Bot-free live capture across conferencing platforms and org-wide recording policies
- AskFellow cross-meeting agent and advanced org analytics
What remains hard
- Compliance and regulation
Built to serve regulated financial institutions.
- Compliance and regulation
SOC 2 Type II, and GDPR certified, with MNPI-aware workflows and analyst-style summaries that capture decisions and action items.
- Compliance and regulation
Our AI is never trained on your data.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 10 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 AI meeting-notes web app using: React frontend, Node/Express backend, Postgres, S3-compatible storage, and OpenAI (or open-source) ASR + LLM. In scope: (1) upload or attach meeting audio, (2) store audio in S3, (3) transcribe audio via Whisper (local or API), (4) call an LLM to produce a structured meeting summary and extract action items, (5) store transcript, summary, and metadata in Postgres, (6) simple UI to list recordings, play audio, view transcript and summary, (7) basic full-text search over transcripts. Out of scope: live bot joining Zoom/Teams/Google Meet, enterprise compliance certification (SOC2/HIPAA), advanced admin policies, and 3rd-party CRM integrations. Include error handling, retries for ASR/LLM calls, authentication (JWT), and unit/integration tests for API endpoints.
How we checked
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
- Hard moats found in the evidence-3
- Evidence score64
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.
- official productFellow.ai — official product
- official pricingFellow.ai — Pricing
- official docsFellow.ai — Features
- open sourceZackriya-Solutions/meetily
- open sourceNatively-AI-assistant/natively-cluely-ai-assistant
Integrity checks
What held up, and what did not.
