A finished research run on an example topic, shown in full — the findings, the sources behind them and the product versions are all the run's own output, except that a product name may have been changed where it matched an existing brand.
A phone-camera app that scans indoor gym walls to auto-detect holds and generate route maps, plus a session tracker for logging climbs.
01
What to build
MVP
An iOS/Android app using an on-device CNN/YOLO model to detect holds from a photo, overlay a route grid, and let users log attempts against mapped routes.
Why it can be better
Unlike KAYA and TopLogger, which depend on routesetters manually entering data, this auto-detects holds from a photo, closing the gap flagged as no automatic wall scanning.
Revenue model
Freemium app with a monthly subscription (~$6-9) unlocking unlimited wall scans and route history, paid by individual climbers.
Target user
Regular gym climbers who want faster route logging without manual tagging.
02
Context
The opportunity this answers
Build a phone-camera app that scans any climbing wall to auto-map holds and routes, giving climbers and home-wall builders personalized route and training suggestions.
Why now
Fresh CV datasets and hold-classification models built in the past year finally make automated wall scanning technically feasible, but they remain scattered single-star hobbyist repositories with no packaged product, while chain gyms keep expanding and search demand holds steady at 8,100-18,100 monthly, giving a real, growing user base ready for a tool that turns this raw technology into something usable before a larger incumbent bundles it first.
