ZeroDefect

ZeroDefect is AI quality inspection for small factories that needs only one good part and a phone camera. Small units in Chennai's industrial estates (Ambattur, Guindy, Sriperumbudur) still check bolts, nuts, bottles and PCBs by eye, so defects slip through and whole batches get rejected. Machine-vision systems cost lakhs of rupees and need thousands of labelled defect photos. How it works: the owner shows ZeroDefect one golden sample. SAM 3.1 finds every part in the frame from a phrase like "metal nut". A zero-training patch-feature detector (frozen ResNet-18, PatchCore-style memory bank built from the golden sample) makes an anomaly heatmap for each part. Muse Spark acts as the QC inspector: it compares the golden sample, the part and the heatmap, and returns a structured verdict (defect type, location in plain words, severity, likely process cause, a spoken call-out and a grounded defect box). That box goes back to SAM 3.1 as a box prompt to get a pixel-accurate defect mask and the defect's size. When the detector and Muse Spark disagree, the part goes to human REVIEW. One tap on "Actually OK" adds a part to the golden set, and a Muse Spark tool-calling agent turns the inspection log into a shift report. Everything falls back to local methods if the network drops. Results with 1 golden image and 0 defect images on MVTec AD: bottle image AUROC 0.993 / pixel AUROC 0.983; metal nut (scratch + colour defects) 0.931 / 0.951. In the full app with 4 extra OK parts for calibration: 63/63 defective bottles caught (1/20 good flagged) and 36/45 defective nuts caught (0/22 good flagged). About 0.15 s per part on a laptop CPU. Built during the hackathon.