Production quality inspection
Defect detection on high-speed lines with accuracy above human inspection.
01 / COMPUTER-VISION
Artificial Intelligence
Computer VisionArtificial Intelligence — We teach machines to see, count and inspect.
We teach machines to see, count and inspect.
We build computer vision systems for quality control, counting, security, advanced OCR and real-time video analytics. From model training to integration with edge cameras.
>99%
Typical accuracy
30 FPS
Real-time on edge
−85%
False positives vs rule-based systems
§ A
Overview
Modern computer vision combines deep learning, foundation models (SAM, CLIP, DINO) and classic image processing. We tackle problems ranging from quality control on production lines to document recognition, from perimeter security to in-store behavioural analytics.
We work end-to-end: data collection, annotation, training, deployment on edge (Jetson, Coral, smart cameras) or cloud, monitoring of model drift in production.
§ B
What's included
- Problem definition and feasibility on sample images
- Dataset collection and annotation (with dedicated labelling team if needed)
- Training custom models or fine-tuning foundation models
- Inference optimisation (quantisation, pruning, TensorRT)
- Deployment on edge devices or GPU cloud
- Integration with IP cameras, RTSP, MES, ERP
- Active learning: the system learns from new cases it encounters
§ C
Deliverables
What you get at the end — or along the way — of an engagement on Computer Vision.
- D/01Trained model with performance metrics
- D/02Inference API or edge app
- D/03Customer-owned annotated dataset
- D/04Monitoring dashboard
- D/05Technical documentation and re-training procedures
§ D
Use cases
OCR on complex documents
Structured field extraction from invoices, receipts, contracts, even with variable layouts.
People counting & analytics
Headcount, heat maps and flow analysis in retail and public spaces, GDPR-compliant.
Perimeter security
Intrusion detection, abandoned object detection, anomaly behaviour with reduced false alarms.
§ E
Our process
01
Feasibility
Test on 200–500 images to validate the approach before investing in the dataset.
02
Data pipeline
Structured collection, annotation, augmentation, train/val/test split.
03
Model training
Iterations with experiment tracking, hyperparameter tuning, edge-case evaluation.
04
Edge deployment
Compilation, quantisation, integration with the real video flow.
05
MLOps
Drift monitoring, periodic retraining, field version management.
§ F
Technologies
PyTorch · TensorFlowYOLOv8/v11 · DETR · SAM2OpenCV · KorniaONNX · TensorRTNVIDIA Jetson · Google CoralDocker · Triton Inference Server
Indicative stack. We adapt choices to your context, internal skills and existing constraints.
§ G
Frequently asked questions
Q/01How many images do you need?+
Depends on the problem. Simple classification needs 100–500 examples per class; complex detection needs 2–5k. Foundation models drastically reduce the need.
Q/02Does it work in real time?+
Yes. On Jetson Orin we run 30+ FPS in HD; on GPU servers we reach hundreds of concurrent streams.
Q/03Is it GDPR-compliant?+
We work by-design with anonymisation (face/plate blurring) and DPIA. Where possible inference happens on-edge without storing images.
Next step
Let's talk about computer vision.
A 30-minute call to understand your context and whether we can really help. No commitment.