ANOMALY HUNTING: What's Strange About This Market?
Anomaly 1: The "Too Good to Scale" Trap
Every visual inspection startup reaches ~$5M ARR and stalls. Why? They become custom ML consultancies, not software companies. Each customer requires bespoke training data, custom models, and ongoing tuning.
Anomaly 2: Cross-Industry Learning Doesn't Exist
Cognex has inspected billions of products. Where's the transfer learning? A model trained to detect scratches on automotive parts should help detect scratches on consumer electronics. But no one has built the shared learning layer.
Anomaly 3: No Marketplace for Inspection Models
There's a marketplace for everything in AI — except pre-trained visual inspection models. A food manufacturer shouldn't need to train a "detect mold" model from scratch.
Anomaly 4: Humans Still Do First-Pass Triage
Even Cognex customers use humans to triage "uncertain" detections. The AI handles obvious cases; humans handle edge cases. No one has optimized this human-AI collaboration.
Gap Analysis:
- Gap 1: No self-serve deployment (all require professional services)
- Gap 2: No pay-per-inspection pricing (all require hardware purchase)
- Gap 3: No cross-industry model sharing (every customer starts from zero)
- Gap 4: No smartphone/tablet-first solution (all require industrial hardware)
- Gap 5: No real-time quality dashboards for operators (all require QA specialists)
