Object detection and tracking
Multi-class detection with persistent IDs across frames, tuned for occlusion and camera movement rather than benchmark scores.
AI & Machine Learning
Computer Vision Systems
Detection, segmentation, tracking, and classification models trained on your imagery and deployed against real cameras and real throughput targets.
Overview
Most vision projects stall between a notebook that works and a system that runs. We build for the second one: models trained on your data, evaluated on the edge cases that matter to your operation, and packaged so they survive contact with production hardware.
We have run this end to end - from labelling raw footage through to a portal that flags violations on a live site. That history means we scope realistically about what accuracy is achievable on your data, and where the model will need a human in the loop.
What you receive
- Trained model weights with a documented evaluation report
- Inference service with a versioned API
- Annotated dataset and labelling guidelines
- Monitoring hooks for drift and confidence distribution
- Runbook covering retraining and rollback
Typical stack
- PyTorch
- Ultralytics
- OpenCV
- ONNX Runtime
- TensorRT
- FastAPI
Capabilities
What this covers in practice
Segmentation
Semantic and instance segmentation where pixel-level boundaries drive a downstream measurement or decision.
Classification and quality inspection
Defect and compliance classification with calibrated confidence, so borderline cases route to review instead of guessing.
Edge and on-premise deployment
Quantisation and runtime optimisation for constrained hardware, including sites with no reliable network.
Related
Also under AI & Machine Learning
Predictive Modelling & Forecasting
Forecasting, pricing, and anomaly detection models built on your operational history, with the validation work that tells you whether to trust them.
Read moreMLOps & Model Deployment
Getting models off laptops: reproducible training, versioned artefacts, automated evaluation gates, and deployment you can roll back.
Read moreWant to talk through a computer vision systems project?
Send us the shape of the problem and we'll come back with a scoped approach, a timeline, and an honest read on what's achievable.