StitchCheck

StitchCheck turns any phone into a stitch inspector for small garment units. My family is setting up a 25-machine stitching unit in Kadiri, Andhra Pradesh. A new unit only wins orders by proving consistent quality, but small units check seams by eye and log defects on paper, so there is nothing a buyer can verify. How it works: the checker photographs a seam close-up. SAM 3.1 finds the seam. Muse Spark is the inspector's brain: it calls a stitch classifier fine-tuned on stitch images as a tool, compares the seam with a good reference, confirms or overrides the call and explains it in one line. When they disagree, the checker decides with one tap, or corrects by voice in Telugu (Muse Voice Transcribe). A Muse Spark agent logs every defect by machine, tracks DHU live, raises trend alerts with work orders for the mechanic, plans the AQL 2.5 audit, and writes a buyer report whose every number is filled in by the server from logged data. Muse Spark Open is a second AI mode and the automatic fallback. Honest results: on held-out lab fabrics, StitchCheck catches 92.9% of major defects (Muse Spark alone 53.2%). On 185 real phone photos from Kadiri it flags 85.8% of major defects but also most good seams, so on a phone today it runs as a suggestion tool: the AI proposes, the checker decides, and every confirmed photo becomes training data. Live app: https://jvpnath--stitchcheck-web-web.us-east.modal.run (tap Try demo)