AI assistants and search decision
Wood Ai
A single capable developer can reproduce the core identification and chat features using existing open-source plant-ID projects and standard cloud services; no durable moats are evident so keeping paying is optional.
View on the App Store↗$9.99/mo
$120/yr
Read off the official pricing page.
$100one-off88 h to build
$90/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 10 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 Wood Ai alternatives, with the arithmetic →
What a replacement has to do
- Take photo → run image ID model → return species + care/wood data → optional follow-up chat
What it still won’t have
- Large, curated commercial species database and rare/exotic species coverage
- App Store polish, marketing, and existing user base
- Built-in multi-language translations and localization
- Ongoing accuracy tuning and labeled training-data improvements
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 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 cross-platform (React Native + Expo) mobile app that identifies plants, trees, and wood from photos. Stack: React Native (Expo), TypeScript, Supabase (Postgres) for metadata and history, and OpenAI (or another LLM) + an image-identification API/model for inference. Core features in scope: camera/photo picker, image preprocessing and upload, call an image-ID model API and store returned species and confidence, lookup and display species/wood metadata (care tips, Janka hardness, common uses) from Postgres, simple chat UI that sends the selected scan context to an LLM and displays responses, local scan history with basic search, and iOS in-app subscription via StoreKit. Out of scope: training new image classification models from scratch, multi-language localization beyond English, and enterprise analytics. Include error handling for network and inference failures, unit tests for data-layer functions, and end-to-end smoke tests for the capture→identify→chat flow.
How we checked
How the score was reached
- Build verdict base78
- An open-source build was found+5
- 3 cited sources+3
- Price verified on pricing page+3
- Evidence score89
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 · 3
Every page the run actually retrieved.
- official productWood Ai: Tree & Plant Scanner — App Store
- open sourceGardenPulse — GitHub
- open sourcenature-id — GitHub
Integrity checks
What held up, and what did not.
