We build computer vision systems that detect, track, read and inspect in real time. They run on your own devices, keep camera footage on site and are monitored once live.
What's included
6 capabilities, delivered by one squad. Use what you need now and add more as you grow.
Object detection, tracking of several objects at once, counting and event triggers for real-time operations.
Read industrial text, meters, labels, forms and ID documents, with a confidence check on every result.
Defect detection, anomaly spotting and visual QA tuned for the variation on your production line.
Models run on the device itself for fast responses and privacy, tuned to your hardware's limits.
Labelling pipelines, dataset checks and active learning, which picks the new images most worth labelling, so the model keeps improving.
Activity records, metrics and alerts that track model confidence and day-to-day reliability.
Our approach
The usual way
Models get worse when the lighting changes, a camera is swapped or the seasons turn.
How we do it
We build datasets that cover your real conditions, add varied training examples and watch for accuracy drift, so the model holds up on site.
The usual way
Models that work in testing but stall on the device once speed, memory or heat limits are hit.
How we do it
We compress, batch and tune each model for your hardware, so it runs steadily on the device.
The usual way
No one tracks confidence, keeps activity records or collects signals for retraining.
How we do it
We log confidence and activity, trigger retraining when needed and send uncertain results to a person for review.
Architecture
Each layer has a clear job, so the system is easier to secure, test and extend.
Dataset versions, label checks and a steady supply of the cases the model gets wrong, so it learns real-world variation.
Camera feeds, time sync, normalisation, regions of interest and calibration for consistent input.
Model compression, hardware acceleration, batching and tuning for steady, fast processing on the device.
Confidence thresholds, region rules and human review before results trigger actions, plus drift alerts.
How we deliver
4 phases, each ending with an output you can review.
Define your camera set-up, edge hardware limits, label categories and how success will be measured in production.
Output: Vision feasibility blueprint
Train and test the model, check and expand the datasets, and collect the hard cases it gets wrong.
Output: Reliable model baseline
Compress the model, tune the runtime, and add confidence checks and human review paths.
Output: Production-ready edge stack
Go live with monitoring, drift alerts and a retraining loop connected to your operations team.
Output: Monitored, measurable accuracy
Your team
Specialists join your squad for this work, alongside a delivery lead who keeps you updated.
Designs the whole system, from cameras and image preparation to the model, its checks and the signals it sends.
Sets the label categories and runs label checks, dataset versions and active learning, so accuracy holds as conditions change.
Compresses and tunes models for your hardware, so they run fast without overheating the device.
Runs monitoring, confidence tracking, drift detection and retraining triggers, with day-to-day dashboards.
Trust and control
Thresholds, region rules and other limits are checked before any action is triggered.
Frames stay on the device, and only signals, metadata and alerts are sent out.
Event logs and confidence tracking show when accuracy slips and retraining is due, and every deployed model is versioned.
Low-confidence results go to a review queue, so a person makes the final call.
You keep full ownership of the code, configuration and documentation we create, with no vendor lock-in.
We pick what fits your product and team, not the other way round.
Results
We built edge AI for a high-volume hospital that analyses CT and MRI scans on site, locates likely tumours and moves urgent cases up the worklist. Throughput rose 4.5x, and each on-site analysis takes 40ms.
We built a computer vision system for a resort group's buffets that tracks tray levels and live demand, then tells kitchens when to cook more or hold back. Food waste fell 15%, and margins rose 11%.
For a large health system, we built a claims platform that matches EHR evidence to payer rules, automates routine authorisations and logs every decision. It is built to HIPAA requirements. Claims recovery rose 42%, and staff saved 15k hours.
We built an underwriting workflow that combines banking and business data and applies clear lending rules. Straightforward applications are decided automatically, and borderline cases go to an underwriter.
Common uses are detecting, tracking and counting objects, reading text from meters, labels, forms and ID documents, and spotting defects on a production line. Our case studies include edge AI that locates likely tumours on hospital CT and MRI scans, and a buffet system that tracks tray levels to cut food waste.
Accuracy holds up when the training data matches your real conditions. We build datasets that cover different lighting, cameras and seasons, add varied training examples and test against golden samples. Once live, confidence monitoring and drift alerts show when accuracy slips, and a retraining loop brings it back.
Models can run on modest edge hardware once they are tuned for it. We compress models to INT8 or FP16, batch the work and tune runtimes such as TensorRT, OpenVINO or TFLite to your device's speed, memory and heat limits. We agree those limits during the first audit.
Camera frames can stay on the device. Processing happens locally, and only signals, metadata and alerts leave the site, such as a count, a defect flag or a meter reading. This keeps footage out of the cloud and limits who can see it, with an activity record for every event.
Results below a confidence threshold go to a human review queue, and a person makes the final call. Region rules and other checks also run before any result triggers an action. Reviewed cases feed back into training, so the model gets better at the cases it found hard.
Timing depends on your camera set-up, how much labelled data you already have and the limits of your edge hardware. We begin with an environment and data audit, then produce a feasibility blueprint before any full build. A scoped project can kick off in 1–2 weeks.
You own 100% of the code, trained models, designs and IP we create, and this is written into the service agreement. We can sign an NDA before you share camera footage or other sensitive data, so your images are protected from the first conversation.
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