Computer Vision
Custom Computer Vision systems: object detection, OCR and document AI, classification and segmentation, real-time video analytics, automated quality control in manufacturing, facial recognition and biometrics, ANPR (license plate reading), people counting and retail flow analysis, visual inspection on industrial lines. Stack: OpenCV, TensorFlow, PyTorch, YOLO, MediaPipe, plus cloud services (Google Cloud Vision, AWS Rekognition, Azure Computer Vision) and edge inference (NVIDIA Jetson, Coral) when latency and privacy require it. From POC on your client datasets to production rollout with MLOps pipelines, drift monitoring and continuous re-training.
What we deliver
- Multi-class object detection and tracking with YOLO and custom models fine-tuned on proprietary datasets
- OCR and Document AI for structured data extraction from invoices, delivery notes, and non-standard forms
- Semantic and instance segmentation for automated visual quality control on production lines
- Real-time video analytics: people counting, flow heat maps, dwell time analysis for retail environments
- ANPR (Automatic Number Plate Recognition) for access control, logistics, and parking management
- Edge inference on NVIDIA Jetson and Google Coral for low-latency, air-gapped or privacy-sensitive deployments
- MLOps pipelines with drift monitoring, model degradation alerting, and automated re-training workflows
- Cloud Vision API integration (Google, AWS, Azure) for horizontal scaling on variable-volume workloads
When you need it
Manufacturer with defect rates manual inspection can't control
A production facility runs high-speed lines where human inspectors miss subtle visual defects. Scrap rates are climbing and traceability is weak. They need a 24/7 automated system that catches anomalies consistently and logs every result.
Retailer that needs in-store behavior data
A retail chain wants to understand which areas drive conversion, where customers dwell, and where they drop off — data that point-of-sale systems don't provide. They need anonymized visual analytics that hold up under GDPR scrutiny.
Logistics operator automating vehicle access
An industrial site or logistics hub needs to manage truck and vehicle access without manual intervention. Manual plate reading creates bottlenecks and errors. They need a reliable ANPR system that handles variable lighting and dirty plates.
Back-office drowning in unstructured paper documents
A professional firm or administrative office processes hundreds of heterogeneous documents daily — handwritten forms, mixed-format invoices, non-standard delivery notes. Manual data entry is slow and error-prone. They need an OCR engine that writes structured data directly into their ERP.
Frequently asked questions
How much training data do we actually need?
It depends on task complexity. For detecting a repeating visual defect, 300–500 annotated images are often enough for a working first model. Higher-variability scenarios — multiple lighting conditions, angles, or classes — typically need 2,000–5,000 examples per class. We always start with a dataset audit before committing to a volume estimate.
Can the model run without an internet connection?
Yes. For air-gapped environments or strict privacy requirements, we deploy inference at the edge: NVIDIA Jetson for heavier compute loads, Google Coral for ultra-low latency. The model runs locally, no data leaves the facility.
What's the realistic timeline from POC to production?
A POC on customer-supplied data typically takes 3–6 weeks. Moving to production — with MLOps pipelines, monitoring, and integration into existing systems — adds 4–10 weeks depending on infrastructure complexity. The most common total timeline for an industrial project is 3–4 months.
How do you handle model accuracy degradation over time?
We set up a monitoring layer that tracks input distribution and confidence metrics in production. When values drift outside thresholds agreed at project start, an alert fires. We then assess whether additional training data resolves the issue or whether operating conditions have changed structurally enough to require model redesign.
Is facial recognition viable under GDPR?
Biometric data is a special category under GDPR Art. 9 and requires explicit legal basis, a mandatory DPIA, and technical minimization measures. We only work on use cases where those conditions are fully documented upfront. For retail, we typically recommend aggregated anonymized analytics instead — same business insight, no biometric processing.
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