Products that see what humans miss.
For manufacturing, healthcare, retail, and logistics teams. We build production computer vision systems — not proof-of-concepts — with the active learning pipelines, edge deployment tooling, and compliance-aware architecture to keep them accurate as the world changes.
We build systems that read images — counting stock, checking quality, reading documents and plates, spotting defects. Accuracy depends entirely on your images, so we test on your real ones before quoting anything.
Updated August 2026
It is never the model.
What actually decides whether a vision system works.
Computer vision accuracy at demo time and accuracy in production are two different numbers.
It's easy to get 95% accuracy on a clean benchmark dataset. It's hard to maintain it when the production data distribution shifts — the lighting changes, a new product variant appears, or a supplier changes packaging. We build the active learning infrastructure to keep models accurate over time, not just at launch.
We build on YOLO, Segment Anything, PaddleOCR, and custom architectures depending on the task. We also build the data labeling pipeline, annotation tooling, model versioning, and the deployment infrastructure — because a model is only as good as the system around it.
For regulated industries (healthcare, fintech KYC, food safety), we build the compliance architecture alongside the model: audit logs for every inference, explainability layers for model decisions, and version-controlled model registries that can be audited and rolled back.
What we build.
Defect detection & quality inspection
Real-time defect detection on production lines — scratches, misalignments, missing components, packaging defects — at the throughput and false positive rate your ops team can act on.
Document & identity verification (KYC)
Passport, ID card, and driving license reading with liveness detection, fraud signal scoring, and compliance-aware audit trails for regulated financial services.
Medical image analysis
Segmentation, detection, and classification for radiology (X-ray, CT, MRI), pathology (H&E slides), and dermatology — with FDA/CE pathway documentation support.
Retail analytics & planogram compliance
Shelf stock monitoring, planogram compliance checking, customer flow analytics, and checkout automation using standard CCTV infrastructure.
Logistics & warehouse automation
Package detection, barcode and QR reading, pallet inspection, and dock-door automation — integrated with your WMS and conveyor systems.
Active learning pipelines
Continuous model improvement systems: hard example mining, uncertainty sampling, annotation queue management, and automated retraining — so accuracy compounds instead of drifting.
What decides whether this works.
Almost never the model. Nearly always the data and the conditions.
| Factor | Good outcome | Poor outcome |
|---|---|---|
| Lighting | Consistent, controlled | Varies through the day |
| Camera position | Fixed | Handheld, different angles |
| Example images | Hundreds per case, labelled | A dozen, unlabelled |
| Rare cases | Represented in the data | The ones that matter, missing |
| What counts as a defect | Agreed and written down | Two inspectors disagree |
| Cost of being wrong | Flag for a human | Acts automatically |
How we start.
We test on your real photographs before quoting. Demo accuracy on clean data means nothing.
The system surfaces likely problems; a person confirms. Full automation comes later, if at all.
If the images cannot support it, we say so rather than take the project and underdeliver.
How we build it.
Data audit
We review your existing image/video data, annotation quality, class distribution, and edge cases. We identify gaps and design the data collection strategy for cases the model will encounter in production.
Benchmark & architecture
We select the model architecture based on your latency, hardware, and accuracy requirements. We establish baseline benchmarks and define the production targets before training.
Training & evaluation
We train with your production data, build evaluation sets that match production distribution (not just the easy cases), and iterate until targets are met.
Deployment
We deploy to edge (Jetson, Raspberry Pi, custom hardware) or cloud (AWS, GCP) depending on latency and connectivity requirements. We optimize with TensorRT, ONNX, or OpenVINO for the target hardware.
Active learning
We build the feedback loop: hard example capture, human annotation queue, automated retraining triggers, and model registry. Accuracy compounds — not drifts — as you operate.
Tools we use.
Frequently asked.
5 questions answered. Still have one? Reach out.
For transfer learning with a strong base model, 500–2,000 labeled examples can produce production-ready models for narrow tasks. For complex multi-class detection, you typically need 5,000–20,000+ examples. We help you determine the right target and can accelerate labeling with semi-supervised annotation.