AI assistants and search decision
Avoma
Building a useful subset (record/transcribe/summarize + CRM sync and a basic UI) is realistic for one capable developer in about a week, but reproducing Avoma’s full product — scheduler/lead routing, conversation intelligence, enterprise compliance, and polished multi-conference integrations — is much larger and would require more engineering and ops 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
- Capture meeting audio/video, produce speaker-separated transcription, generate structured AI meeting summary/notes, save transcripts and summaries, push key fields to CRM.
What it still won’t have
- Enterprise features: SSO, DPA/HIPAA workflows, org-level policies and admin controls
- Advanced Conversation Intelligence: AI call scoring, methodology tracking, deal-risk forecasting
- Scheduler & lead-router with round-robin/weighted routing and form-based qualification
- Polished multi-conference integrations, scalability and built-in compliance audits
- “Ask Avoma” cross-account/global conversational search across all conversations
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 6 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 (or Python) + React, Postgres, S3-compatible storage, and deploy on a single small cloud VM (or managed app service). Implement: 1) Zoom/GCal integration to capture meeting media or webhooks; 2) upload/save media to S3 and metadata to Postgres; 3) call a speech-to-text API (whisper/OpenAI or similar) to produce speaker-separated transcripts; 4) call an LLM (OpenAI or similar) to generate structured notes and short summaries with templates; 5) sync extracted CRM fields and a summary to HubSpot via its API; 6) provide a simple authenticated web UI to list meetings, play audio, view transcript, and edit/send summary to CRM. Out of scope: scheduler/lead-routing, advanced call scoring, enterprise SSO/DPAs, multi-tenant admin console. Include basic error handling, retries for API calls, logging, and unit tests for transcription and CRM sync flows.
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
- 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.
- official productAvoma — AI Platform for Note-taking, Scheduling & Coaching
- official pricingAvoma Pricing — Flexible & Affordable Plans
- official productAI-Powered Sales Training: Transform Onboarding & Performance
- open sourceVexa-ai/vexa
- open sourceZackriya-Solutions/meetily
Integrity checks
What held up, and what did not.
