Valuable information sits in photos, drawings, scans and footage that people have to review by hand.
09 / Computer Vision
Computer Vision
Teach software to read images and video, then act on what it sees.
Image and video analysis that turns photos, scans and camera feeds into structured data.
We begin by collecting and labelling a small, honest sample of your images, because the data decides how far the model can go. Then we train, set confidence thresholds with you, and build the review screen so people can correct what the model gets wrong.
What the build includes
A complete production workflow.
Labelled dataset and evaluation set
Your images annotated to a written guide, with a held-out set the model never trains on.
Trained model and thresholds
A model with agreed confidence cut-offs for auto-accept, review and reject, plus the measured error cases.
Camera and file ingest pipeline
Code that pulls images from cameras, uploads or storage, normalises them and queues them for analysis.
Review interface
A screen where staff confirm or fix detections, with each correction saved for the next training round.
Where it applies
Typical computer vision work.
- →Spotting defects on a production line
- →Counting items in site photos
- →Reading dimensions off drawings
- →Flagging safety gear missing in footage
How a project runs
Four steps, no surprises.
- 01
Discovery call
A short call about the goal and the first workflow or feature worth building.
- 02
Written scope
What is built, which tools it touches, what a person approves, and the timeline, agreed before work starts.
- 03
Build and review
Short iterations connected to your tools, reviewed with your team before anything ships.
- 04
Launch and handoff
Go-live with documentation and an owner on your side. You own what we build.
Technology and fit
Built with the right tools, not every tool.
The exact stack follows the workflow, security requirements, existing systems, and deployment environment.
Technologies
PyTorchOpenCVClaude VisionGPT vision modelsPython
Industries
Expected outcomes
- →Photos turned into structured records
- →Inspection that does not depend on one person
- →Clear rules for what gets human review
- →A model that improves from corrections
- →Less time spent scrolling footage
Common questions
Before we scope it.
How many images do we need?
It depends on how varied the scenes are. We often start with a few hundred well-chosen examples and use a pretrained model, then add more only where errors show up.
Can it run on a camera or edge device?
Yes, when latency or connectivity requires it. We size the model for the target hardware and compare it with a cloud option before committing.
What if lighting or angles change?
We include those conditions in the evaluation set and report accuracy for each. Where the model is weak, we collect more examples for that case.
Keep exploring
Related services and technologies.
Related services
Technologies we use
Free Audit
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We review the workflow, recommend the first build, and give you a rough price range and timeline before you commit.