Vision AI that catches defects nobody taught it to find.
Onqyra is an edge-native vision foundation model for the factory floor. Show it a dozen good parts and it flags anomalies it was never trained on — then traces every defect class back to the process that caused it.
- Waiting for parts…
No correlated pattern yet. Watching Line 3 for process drift.
- A dozen good samples
- That's all it takes to teach a new part number. No labelled defect library, no rules engineer.
- Zero-shot anomalies
- Flags scuffs, voids, contamination and drift it has never seen — because it learned what good looks like.
- Closed-loop root cause
- Correlates each defect class with line, shift, tool and batch, and pushes the signal to process control.
Built for the high-volume, high-defect-cost lines everyone else ignores.
Teach. Detect. Trace.
Three moves that replace a rules engineer, a labelled defect library and a fatigued second-shift inspector.
Teach it with a handful of good parts.
Point a camera at the line, capture a dozen known-good samples, and Onqyra builds a self-supervised model of what normal looks like for that part number. No defect library. No rules engineer. New SKU on Monday, inspecting by lunch.
- Few-shot learning from good samples only
- One model per part, versioned and rollback-safe
- Re-teach in minutes when the product changes
Detect anomalies it was never trained on.
Because the model learned normal, anything that deviates lights up — a scuff, a void, a foreign object, a shade drift. It localises the defect on the part, scores it, and clusters new defect types automatically so your team can name them later.
- Zero-shot anomaly detection with pixel-level localisation
- Auto-clustered defect classes, human-named in one click
- Runs at line speed on the edge appliance
- 0.97Missing tabletnamed by QA
- 0.91Foil scuffnamed by QA
- 0.88Unclassified cluster #7never seen before
- 0.84Seal voidnamed by QA
Trace every defect class back to the process.
Each anomaly is stamped with line, shift, tool, batch and upstream sensor state. Onqyra correlates defect classes with process events and pushes root-cause signals to your PLC, MES or SCADA — so the line fixes itself instead of just rejecting parts.
- Correlation across line, shift, tool change, supplier lot
- Signals over OPC-UA, MQTT, Modbus or REST
- Scrap and warranty impact measured in weeks, not quarters
- Line 3 · after 06:40 tool change3.4×
- Supplier lot B-118 foil2.1×
- Night shift · sealer temp > 182 °C1.6×
- Line 1 · baseline1.0×
Foil scuff correlates with Line 3 after tool change. Recommend sealer roller inspection before next changeover.
From “defect detected” to “here’s why.”
Rejecting a bad part is table stakes. Onqyra stamps every anomaly with the state of the line and finds the process event that explains it — then closes the loop.
- Process-awareLine, shift, tool change, batch, supplier lot and upstream sensor readings ride along with every frame.
- Correlation, not just countingDefect classes are tested against process events continuously. When one lifts, you know within the hour.
- Closed-loop signalsRoot-cause findings publish to PLC, MES and SCADA over OPC-UA, MQTT, Modbus or REST — so the line can react.
The second shift never gets tired. Neither should your inspection.
Human inspection fatigues. Rules-based machine vision fossilises. Onqyra learns.
| Human inspection | Rules-based machine vision | Onqyra | |
|---|---|---|---|
| Setup for a new part number | Train an inspector, hope they retain it | A rules engineer, per SKU, per change | A dozen good samples. Minutes. |
| Catches defect types nobody defined | Sometimes — if they're looking | Only what was explicitly programmed | Zero-shot anomaly detection |
| Consistency across a shift | ~70–80% catch rate, fades within hours | Consistent, until the product changes | Every part, every shift, same eyes |
| Survives a product change | Retraining and a learning curve | Breaks. Re-engineer the rules. | Re-teach from new good samples |
| Explains the root cause | Anecdotal, on a good day | Pass/fail only | Correlated to line, tool, shift, lot |
| Cost model for a mid-sized plant | Headcount per line per shift | Capex plus integrator days | From $200 per camera per month |
Runs on the line. Stays in the plant.
Inference happens on an edge appliance next to the camera, not in someone else's cloud. Your frames, your models, your network — with the connectivity a real plant floor demands.
Built for the lines automotive-grade vision never bothered with.
From $200 per camera per month.
Priced like software, not like a capex project. Scrap and warranty reduction show up within weeks — which is why pilots convert.
Per camera
Up to $800 depending on resolution and frame rate.
- Few-shot teaching, unlimited part numbers
- Zero-shot anomaly detection & localisation
- Inspection log, exports and alerts
- Edge appliance included on pilot
- PLC verdict output over OPC-UA
Per line
Most pilotsAll cameras on a line, root cause and process integration.
- Everything in Per camera
- Root-cause correlation across line, shift, tool, lot
- Closed-loop signals to MES, SCADA, historian
- Fleet model management & staged rollouts
- Named engineer during pilot
Plant & enterprise
Air-gapped deployment, validation support and SLAs.
- Everything in Per line
- Air-gapped and validated environments
- Audit-grade verdict and frame-hash logging
- Cross-site defect intelligence
- Uptime and response SLAs
Pilot pricing and volume discounts available. Camera pricing scales with resolution and throughput; we'll quote after a 20-minute line review.
Questions from the plant floor.
The ones quality managers and process engineers ask first.
A handful of good parts — typically a dozen — captured on your own line with your own lighting. Onqyra learns a self-supervised representation of “normal” for that part and flags deviations. You never need a labelled defect library, and you can add named defect classes later as they appear.
Put an inspector on every line that never blinks.
Pilots start with one camera and a dozen good parts. Most plants see scrap move within the first weeks.