Computer Vision
Systems that read documents, inspect products and understand video in real time, at the edge or in the cloud. Ten engagements, each proven on your own images before anything is promised.
The demo is never the problem.
A pretrained model draws convincing boxes around objects in a stock photograph within an afternoon. The distance between that and a system that works on your camera, at your angle, in February at three in the morning, is where every computer vision project actually lives. It is closed with imaging, labelling and honest measurement rather than with a better architecture.
So we ask for a hundred real images before promising anything, we write the labelling guide with your experts and measure whether they agree with each other, and we set thresholds from what a miss and a false alarm each cost you. Where the objects are not distinguishable at your camera resolution, that finding arrives in week two rather than month four.
What we build
Each is a standalone engagement with its own scope, price and output. Most clients use two or three in sequence.
Object Detection and Image Classification
Detection and classification models trained on your own images, with honest labelling and cost-weighted thresholds.
Read more →Intelligent Document Processing and OCR
Document extraction at volume with field-level confidence, validation and a review queue that only sees exceptions.
Read more →Visual Quality Inspection and Defect Detection
Defect detection engineered from the imaging up, tuned to escape rate and false reject cost on your line.
Read more →Video Analytics and Surveillance AI
Video analytics tuned against alert fatigue, with tracking across cameras and privacy designed in from the start.
Read more →Facial Recognition and Biometrics
Biometric systems built lawfully, tested across demographics, with liveness, template protection and a narrow scope.
Read more →Medical Imaging AI
Imaging AI built around the regulatory path and clinical validation, not around a benchmark score.
Read more →Pose Estimation and Activity Recognition
Movement and activity understood over time, using skeletal data that needs no identifiable footage.
Read more →Optical Inspection for Edge Devices
Vision models running on device within real thermal, power and latency budgets, with fleet updates handled.
Read more →Image Generation and Enhancement Pipelines
Production pipelines for generated and enhanced imagery, with rights, provenance and automated quality gates.
Read more →AR and VR AI Experiences
Spatial experiences with AI inside, built to a frame budget and to a use case that justifies the hardware.
Read more →How they fit together
You do not need all twelve. Most programmes follow one of these paths depending on where the uncertainty sits.
Documents and paperwork
Intelligent document processing, measured on straight-through rate rather than on average field accuracy, with a review queue that only sees exceptions.
Products and production lines
Visual quality inspection engineered from the lighting up, running on device via edge computer vision within your cycle time.
Cameras and spaces
Video analytics tuned against alert fatigue, activity recognition using skeletal data rather than identifiable footage, and biometrics only where there is a lawful basis.
Specialist and emerging
Medical imaging built for the regulatory path, generation and enhancement pipelines with rights handled, and AR and VR experiences where the task genuinely justifies a headset.
FAQ
Marked up with FAQPage schema so these answers can surface in search results and inside AI assistant responses.
How many images do we need for a computer vision project?
Usually a few hundred to a few thousand labelled examples per class when fine-tuning a pretrained model, with more for rare classes and variable conditions. Representativeness matters more than volume: a thousand images covering your real lighting, angles and seasons beat ten thousand from one sunny afternoon.
Should inference run on the camera or in the cloud?
Latency, bandwidth, privacy and power decide it. Real-time control, poor connectivity and images that must not leave the site all point to the edge; batch analysis with good connectivity is simpler centrally. The choice constrains the model, so it is made before training rather than after.
Why do computer vision projects fail?
Most often at the imaging stage, and then the model gets blamed. If the defect or object is not clearly visible in the image, no amount of training will find it, and the fix is a light, a lens or an angle. After that, inconsistent labelling is the next largest cause.
How is this different from multimodal AI?
Multimodal AI development reasons across mixed media to answer open-ended questions. Computer vision here is production perception: detection, extraction, inspection and tracking at volume with confidence, validation and deployment constraints. Many builds use vision-language models inside; the difference is the operational apparatus around them.
Who owns the models, datasets and labelling guides?
You do, in full, on final payment: trained models, the labelled dataset, the labelling guide, agreement measurements, deployment code and monitoring. The dataset is frequently the most durable asset, because it makes every subsequent model faster to build.
Start with a conversation.
Thirty minutes, no charge, no deck. Tell us what you are trying to build with language models and we will tell you which of these engagements fits, or whether none of them do.