Clear, owned outputs.
- Feasibility assessment with sample data
- Labelled dataset
- Trained vision models
- Evaluation under real conditions
- Deployment on edge or cloud
Computer vision lets software interpret images and video: spotting defects, counting objects, reading text or recognizing activity. We build vision systems from data collection and labelling through to deployment on cameras, edge devices or the cloud, with evaluation under real conditions.
The problems Computer Vision solves.
Detection, inspection, classification, OCR and video analytics.
Visual inspection is manual, slow and inconsistent.
Camera footage is recorded but never analysed.
Information on images and scans must be typed in by hand.
Lighting, angles and variation make simple rules unreliable.
What Computer Vision covers.
Image classification
Sorting images into categories.
Object detection
Finding and counting objects in images and video.
Visual inspection
Detecting defects and anomalies in products.
OCR
Reading printed and handwritten text from images.
Video analytics
Analysing events and activity in video streams.
Data labelling
Planning and managing image annotation.
Computer vision tasks, explained.
Vision projects succeed when the camera setup and the labelling are designed as carefully as the model.
| Task | What it does | Example |
|---|---|---|
| Classification | Assigns a label to a whole image. | Pass or fail on a product photo. |
| Object detection | Finds and boxes each object in the image. | Counting items on a shelf or pallets in a yard. |
| Segmentation | Labels every pixel, giving exact shapes. | Measuring the area of a defect on a surface. |
| OCR | Reads printed or handwritten text from images. | Capturing serial numbers or delivery notes. |
| Edge deployment | Runs models on devices near the camera. | Inspection on a production line without sending video to the cloud. |
Why it's worth it.
Consistency
Inspection quality doesn't depend on fatigue.
Speed
Images and video processed continuously.
New insight
Footage and images become usable data.
When Computer Vision is the right move.
- Defect detection on a production line.
- Counting stock or pallets in a warehouse.
- Reading labels, forms or meter readings from photos.
Technologies & platforms
- PyTorch
- OpenCV
- YOLO
- Azure AI Vision
- Amazon Rekognition
- Google Cloud Vision
- NVIDIA Jetson
- Label Studio
How Computer Vision work runs.
- 01
Frame
The decision or task to improve, the measure of success and the constraints.
- 02
Assess data
Availability, quality, access, privacy and labelling needs.
- 03
Prototype
A small, honest test of whether the approach can work.
- 04
Build and evaluate
Model development with documented evaluation against agreed criteria.
- 05
Deploy and monitor
Integration, monitoring for drift and performance, and retraining plans.
Computer Vision questions.
How many images do we need?
It varies with the task and variation in the images. A feasibility phase with a sample set gives a realistic estimate.
Can vision run on existing cameras?
Often, if resolution, angle and lighting are adequate. We assess this during feasibility.
What about privacy with video of people?
Privacy must be designed in: limiting what is captured and stored, anonymizing where possible and following applicable law and your policies.
Talk to us about computer vision.
Tell us which decision or process you want to improve and what data you have. We'll reply with whether it's a good fit and how we would test it.