Capgo

Replacing brake pads sounds simple until part numbers are worn off, visual differences are tiny, and compatibility depends on brand, caliper, and generation. We set out to turn that messy, manual process into a quick, visual check. The app guides users to frame the pad correctly, runs on-device recognition, and returns an exact model with compatibility notes and where to source it. The goal was to cut decision time from minutes of catalog hunting to a few seconds, reduce returns and mis-picks, and make the experience reliable enough for busy benches and DIY garages alike.

We built the application with Flutter to deliver one consistent codebase and a smooth, responsive UI. Image capture uses a lightweight camera flow with guidance overlays and instant feedback so users can retake a blurry shot before it slows them down. Recognition runs on-device with TensorFlow Lite, which keeps results fast and privacy-friendly, even with spotty connectivity. Firebase provides secure authentication, analytics, and push updates, while MongoDB stores the catalog, compatibility mappings, and model lineage so we can keep up with new parts. We invested in clear error states, confidence indicators, and quick recovery paths so even imperfect photos lead to useful next steps rather than dead ends.

The result is a compact, purpose-built app that makes precision effortless. Users take a photo, confirm the match, and see a model page with specs, supported brake systems, and direct links to reputable purchase options. Shops can move through multiple bikes without flipping through PDFs, while riders can identify parts at home without specialist knowledge. Saved history and favorites help with repeat orders; search and filters cover edge cases when a photo isn’t available. With a clean interface and focused guidance, the app turns an error-prone task into a quick, confident step in any repair workflow.

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