AI assistants and search decision
Kabo AI
A small technical team or single experienced developer can build a useful replacement using OpenFoodFacts and rule-based analysis in ~38 hours; proprietary data and subscription lab services are the main paid gaps.
View on the App Store↗$14.99/mo
$180/yr
Read off the official pricing page.
$100one-off38 h to build
$20/mo3 h/mo upkeep
On cash alone, building overtakes the subscription at 2 seats.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- Scan barcode or photo → lookup product metadata → parse ingredients and flag allergens/additives via rules/ML → compute a health score and show swaps/history.
What it still won’t have
- Proprietary product dataset and labeled analyses used by the app
- Subscription-only lab-informed recall monitoring and curated recommendations
- Polished native iOS UX and App Store distribution
What remains hard
- Proprietary data
100k+ products data added
- Proprietary data
Lab-informed insights and recall monitoring (available with a subscription).
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 2 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 web/PWA food scanner using Next.js + React for frontend, Postgres for storage, Node.js + Express for API, and deploy to Vercel/Heroku. In scope: camera-based barcode scanning (use a JS library like ZXing/quagga), product lookup via the OpenFoodFacts API with local caching, ingredient-parsing rules engine (configurable list of additives/allergens), a simple scoring algorithm, per-user history, basic login (email/password), error handling, and unit/integration tests. Out of scope: native iOS App Store packaging, lab testing/recall monitoring, paid subscription billing and marketing UX. Provide automated tests, CI, and monitoring alerts for API failures.
How we checked
How the score was reached
- Build verdict base78
- 2 cited sources+1
- Price verified on pricing page+3
- Hard moats found in the evidence-3
- Evidence score79
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 · 2
Every page the run actually retrieved.
- official productKabo AI - Healthy Food Scanner (App Store)
- official productReveal it - Product Scanner (App Store)
Integrity checks
What held up, and what did not.
