Home / Services / AI systems / Computer vision systems
Research-led
Vision systems built on pretrained models, with fewer labels than you think.
Custom computer vision systems, led by our Chief AI Scientist, whose doctoral research showed how much a large pretrained model can do for a new visual task before anyone labels an example. That research is now the way we scope inspection, document, video and measurement systems.
Who it is for
Built for the team that does this by hand.
Manufacturers, utilities and operators inspecting parts, assets or sites from images and video
Organizations digitizing archives, forms and identity documents where the image, not the text layer, holds the information
Sports, health and training products that need pose, keypoints or motion measured from ordinary cameras
What it does
Computer vision systems, by the job.
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.
Document image understanding
Layout, tables, stamps, handwriting and signatures read from scans and photos where OCR alone fails, feeding the document processing agents.
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. Our lead's published methods find keypoints without hand-labelled training data.
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.
Every system here is scoped as one bounded workflow with a written success metric and a kill threshold agreed before the build. The steps below are the ones specific to this one.
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.
This line is led by Eric Hedlin, PhD, our Chief AI Scientist. His first-author papers at NeurIPS 2023 and CVPR 2024 showed that a pretrained diffusion model already knows where the corresponding points in two images are and where an object's keypoints sit, with no labelled examples, and his CVPR 2025 paper, written with co-authors at Qualcomm AI Research, made adapting large models cheaper to train. 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 the work starts, which is how every other line on this page earned its case studies.
Governance
What a person still approves.
- An evaluation set your team labelled decides whether the system ships, not a demo
- 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.
Price
Published, not gated.
Pilot builds $25,000 to $60,000 fixed, production retainer from $8,000 per month. Every AI build starts with a $7,500 AI Opportunity Audit over two weeks that is credited toward the build if you proceed, and a pilot is one bounded workflow on your real data over 30 to 90 days with a written success metric and a kill threshold. Cameras, hardware and labelling time are priced separately and in writing.
The full price list is on the pricing page, and the cost drivers, with the questions that expose a padded quote, are in how much AI agents cost in 2026.
Questions
Computer vision systems, answered.
How much labelled data do we need?
Do you build on PyTorch, YOLO or cloud vision APIs?
What does a computer vision project cost?
Related
Where this connects.
Eric Hedlin, Chief AI Scientist · AI agent development · Document processing agents
The other systems in the catalogue: lead qualification agents, outbound sales agents, customer support agents, document processing agents, internal knowledge assistants, ad optimization systems, client reporting automation, content engines, mcp servers and agent-callable surfaces, always-on operations agents.
Teams we've helped grow
Pilot builds $25,000 to $60,000 fixed, production retainer from $8,000 per month
Real ranges, published. Ad spend stays in your own accounts.
See the full pricing table →Where this fits: see the Strataigize Growth System, the ladder every engagement runs on.
Talk to the build team about computer vision systems
Name the workflow you want it to run and we reply within 24 hours with whether it is a fit, the bounded scope we would propose, and the payback math.
Start with one workflow.
A free 30-minute audit call with the senior team names the workflow this system should run first and what it should cost, whether you build it with us or not.
Book your growth audit →Canadian and looking at funding? How the BDC LIFT program works. Public sector? How we build for government.
Rated 5.0 on Clutch