Vision systems built on pretrained models, tested on your own images.

Test a pretrained model on your inspection images, documents or video before committing to a larger build. A labelled evaluation set establishes what it can detect and which errors need human review.

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Who it is for

Teams that need this workflow.

Manufacturers, utilities and operators inspecting parts, assets or sites from images and video

Organizations digitizing archives, forms and identity documents where the image holds information the text layer misses

Sports, health and training products that need pose, keypoints or motion measured from ordinary cameras

What it does

Computer vision systems, by the job.

Inspect

Visual inspection and monitoring

Defects, wear, occupancy and change detected from fixed cameras, drones or handheld photos, with a labelled evaluation set that says how often the system is right before it is trusted.

Read

Document image understanding

Layout, tables, stamps, handwriting and signatures read from scans and photos where OCR alone fails, feeding the document processing agents.

Measure

Pose, keypoints and correspondence

Body pose, object keypoints and point matching across images and frames: the problems behind motion analysis, fitting, tracking and augmented reality.

Adapt

Foundation-model adaptation

Pretrained vision and diffusion models adapted to your domain with the smallest labelled set that reaches the target accuracy, and a measured answer to whether a smaller, cheaper model would do.

How it is built

The build, in order.

The pilot covers one defined workflow. We agree its success metric and kill threshold in writing before the build; the steps below show how this system is developed and evaluated.

Evaluation set first

A sample of your images, hand-labelled with your team, becomes the number the system is judged against. It is agreed before any model is chosen.

Baseline with pretrained models

Detection, segmentation, vision-language and diffusion-based features are run against the set without training. Often that baseline is most of the way there.

Adapt only where it falls short

Fine-tuning, prompt tuning or a small labelled set, chosen by the gap the baseline left and the cost of closing it. The accuracy curve is shown before more labelling is bought.

Deploy where the data is

On device, on your hardware, or in a Canadian cloud region, with drift monitoring that re-runs the evaluation set on a schedule.

Where this stands

What we run today.

We have not published a client computer vision case study yet. The first builds are scoped as pilots, with the evaluation set and the kill threshold written before work starts.

Governance

Controls and human review.

An evaluation set your team labelled decides whether the system ships
Images of people are handled under a written privacy impact assessment, and faces are not identified unless the use case, the law and you all require it
Models can run on your hardware or in a Canadian cloud region so images never leave your control

The rest of the posture is published on trust and security: client-granted access you can revoke, credentials in managed stores, an AI governance summary aligned to the NIST AI Risk Management Framework, our subprocessors, and a DPA on request. We do not hold SOC 2 and we say so there.

Questions

Computer vision systems, answered.

How much labelled data do we need?

The amount depends on the task; the first two weeks assess the starting data against the agreed evaluation set. Pretrained models carry most of the visual knowledge. A few hundred labelled examples is a common starting point for a narrow task, and some tasks need none. Whatever the number, you see the accuracy curve before you pay for more labelling.

Do you build on PyTorch, YOLO or cloud vision APIs?

PyTorch for anything we train, and the pretrained families that fit the task: detection and segmentation models, vision-language models, and diffusion-based features where labels are scarce. Cloud vision APIs are used where they meet the bar and the data may leave your tenancy. Otherwise the model runs on your hardware.