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Research-led
Vision systems built on pretrained models, with fewer labels than you think.
Custom computer vision systems that start from what a large pretrained model can already do, before anyone labels an example. That is how we scope inspection, document, video and
Who it is for
Built for the team that does this by hand.
Manufacturers, utilities and operators inspecting parts, assets or sites from images
Organizations digitizing archives, forms and identity documents where the image, not the text layer, holds
Sports, health and training products that need pose, keypoints or motion measured from
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
Document image understanding
Layout, tables, stamps, handwriting and signatures read from scans and photos where OCR alone fails, feeding the document
Pose, keypoints and correspondence
Body pose, object keypoints and point matching across images and frames: the problems behind motion analysis, fitting, tracking and
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
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
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
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
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
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
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 the work starts, which is how every other line on this page earned its
Governance
How it checks its own work.
- 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
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
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.
AI agent development · Document
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
Clients include 3M, Jiffy Lube, Goodwill, Western University, Hino Motors, Lundin Mining, Extendicare, Calian, Wabtec, ARGO, and
Kleo
Goodwill
GameGo
Hino Motors
Sad Panda Studios
StoryNest
Blush Blush
Stoic Forge
Beyond Score
MyPetHealth
TaskHive
Sincere Notes
Voices in my Head
Black Top Cabs
BeNow Trader
Crystalyze
24/7 AI Health
Aero Imagery
RepairFind
Prepr
Guiding Light
Freestyle Dance Studio
First Step Mobility
Noa Coach
Pilot builds $25,000 to $60,000 fixed, production retainer from $8,000
Real ranges, published. Ad spend stays in your
Where this fits: see the Strataigize Growth System, the ladder every engagement
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
Start with one workflow.
A free 30-minute consult with the senior team names the workflow this system should run first and what it should cost, whether you build it with us
Canadian and looking at funding? How the BDC LIFT program works. Public sector? How we build for government.
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