Project and task management decision
Motion
A small team can build a narrowed-down docs-to-tasks scheduler and AI chat/search, but recreating Motion's full product (especially its meeting-notetaker accuracy backed by proprietary meeting-video training and broad integrations) is impractical without their proprietary data and larger engineering effort.
Visit website↗$19/mo
$228/yr
Per seat. Read off the official pricing page.
$100one-off80 h to build
$200/mo8 h/mo upkeep
On cash alone, building overtakes the subscription at 11 seats.
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. All Motion alternatives, with the arithmetic →
What a replacement has to do
- Extract action items from docs/notes, create tasks with assignments and deadlines, schedule/timeblock tasks on a calendar, surface/search docs and tasks via an AI chat interface
What it still won’t have
- Proprietary meeting-video-trained models and accuracy gains
- Polished, cross-platform UI and desktop/mobile apps
- Built-in enterprise integrations, dashboards, and priority support
- Scale, reliability, and continuous optimization of scheduling algorithms
What remains hard
- Proprietary data
Trained on 10K+ hours of proprietary meeting video data, Motion's Notetaker is more accurate than human notes 80% of the time — and just as good as the rest.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 11 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-driven docs→tasks scheduler using Next.js + Postgres + Python worker (FastAPI) + Redis. Scope in-scope: 1) accept pasted/uploaded docs and run an LLM extractor to identify action items, assignees (by email), deadlines, and descriptions; 2) persist tasks and simple dependencies in Postgres; 3) connect to Google Calendar via OAuth and timeblock tasks into free slots using a simple greedy optimizer that respects durations and due dates; 4) provide a web UI to review extracted tasks, edit fields, and trigger scheduling; 5) a chat endpoint that searches docs and returns task links. Out of scope: full project/Gantt UI, meeting audio recording/transcription, mobile/desktop apps, team capacity planning, and advanced optimization. Include input validation, retry/backoff for external APIs, basic logging, and unit + integration tests for extractor, scheduler, and OAuth flows.
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
- 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 · 4
Every page the run actually retrieved.
- official docsMotion — AI Docs Assistant feature page
- official pricingMotion Pricing
- open sourcehcengineering/platform
- open sourcemakeplane/plane
Integrity checks
What held up, and what did not.
