AI/ML
Custom computer vision systems for object detection, inspection automation, and video analytics — trained on your data and deployed at production scale.
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What it is
Computer vision systems use deep learning to interpret visual data by detecting objects, recognising patterns, classifying scenes, tracking motion, and extracting measurements — enabling automated inspection, monitoring, and recognition at machine speed.
What you get
Manual visual inspection is slow, error-prone, and expensive at scale. Computer vision replaces or augments human inspection in manufacturing lines, security systems, medical imaging, retail operations, and agricultural monitoring — with consistent accuracy and round-the-clock availability.
We work with YOLO, EfficientDet, Detectron2, and vision transformer architectures, selecting the model family that balances accuracy with the inference latency your deployment environment requires. Edge deployment on NVIDIA Jetson, cloud GPU inference, or hybrid architectures — we design for your operational constraints.
Data is the defining variable in computer vision. We handle labelling pipeline setup, synthetic data augmentation when real samples are scarce, and active learning strategies to minimise annotation cost while maximising model accuracy over time.
Key capabilities
Each engagement is scoped to your requirements — these are the core capabilities we bring to the table.
Optical character recognition (OCR) for documents and forms
Facial recognition and biometric verification
Medical image analysis (radiology, pathology)
Edge deployment on NVIDIA Jetson and Coral devices
Model retraining pipelines with active learning
Our process
A structured, engineering-led approach that moves from understanding your goals to a production system — with no handoff surprises.
Typical engagement
8–16 WEEKS
We map your goals, constraints, and existing infrastructure. Scope is defined and success criteria agreed before any development begins.
We design the technical approach, select the right tools, and produce a milestone-driven delivery plan with no ambiguity.
Iterative development with regular demos. Code reviews, test coverage, and documentation happen in parallel — not at the end.
Production release with monitoring setup and handover documentation. We stay close during the first weeks post-launch.
Built with
With transfer learning from pretrained models, 200–500 labelled images per class are often sufficient for classification. Detection tasks with many custom classes may require thousands. We assess your dataset during discovery and advise on whether augmentation or synthetic data generation is needed.
Yes. We regularly deploy computer vision models on NVIDIA Jetson devices, Raspberry Pi with Coral accelerators, and mobile devices via CoreML or TFLite. Latency, model size, and accuracy trade-offs are selection criteria we optimise for your specific hardware.
Drift detection, production monitoring, and automated retraining pipelines. We instrument deployed models to flag low-confidence predictions for human review and build labelling queues that feed new examples back into training automatically.
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
