Computer vision development services
Computer vision is the field of AI that lets software understand images and video: detecting objects, reading text, measuring and classifying what it sees. Visions builds computer vision models and applications that run in the cloud, on edge devices and inside mobile apps.
Overview
Computer Vision explained
Computer vision gives software the ability to interpret images and video. It can detect and count objects, read text, inspect surfaces for defects, track movement through a scene or match a photo to similar products. Cameras that are already installed, or the camera in a smartphone, often provide all the input needed.
Success in computer vision depends on the real conditions the system will face: lighting, angles, speed and the variety of what it sees. We start by collecting and reviewing representative images, then train or adapt models and test them in those conditions before deployment to the cloud, an edge device or a mobile app.
- 01Define what to detect
- 02Collect and label images
- 03Train or adapt models
At a glance
Computer Vision
Best for
- Manufacturing defect detection
- Retail shelf and inventory monitoring
- Document and ID scanning in mobile apps
- Safety and PPE compliance monitoring
What you gain
- Consistent inspection
- Automated data capture
- Real-time awareness
Built with
- Python
- OpenCV
- PyTorch
- YOLO
- TensorFlow Lite
- ONNX
What our Computer Vision service includes
Object detection and tracking
Detecting and counting people, vehicles, products or parts in images and live video.
Visual quality inspection
Finding defects, damage or missing components on production lines.
OCR and document vision
Reading printed and handwritten text, number plates, labels and forms.
Image classification and search
Tagging images automatically and finding visually similar items.
Video analytics
Event detection, zone monitoring and summaries from camera feeds.
Edge and mobile deployment
Optimised models that run on devices, cameras and smartphones.
Benefits of Computer Vision
What your business gains when it is designed, built and run well.
Consistent inspection
Every item is checked to the same standard, at any speed or shift.
Automated data capture
Text, codes and labels are read directly from images and documents.
Real-time awareness
Live video is turned into alerts and counts as events happen.
Works on the edge
Models can run on devices on site, keeping images local and working offline.
Our method
How we build Computer Vision solutions
A practical, step-by-step approach that proves value early and reduces risk.
Step 1
Define what to detect
We agree on the objects, defects or events to recognise and the accuracy and speed required.
Step 2
Collect and label images
We gather representative images from your environment and label them for training and testing.
Step 3
Train or adapt models
We fine-tune proven pre-trained models to your data rather than starting from scratch.
Step 4
Test in real conditions
We validate on images from different lighting, angles and situations.
Step 5
Deploy and integrate
We deploy to cloud, edge or mobile and connect results to your dashboards or systems.
Common use cases
- Manufacturing defect detection
- Retail shelf and inventory monitoring
- Document and ID scanning in mobile apps
- Safety and PPE compliance monitoring
- Visual product search for e-commerce
Technologies we work with
- Python
- OpenCV
- PyTorch
- YOLO
- TensorFlow Lite
- ONNX
- Tesseract OCR
- AWS Rekognition
- Google Cloud Vision
- Docker
Where Computer Vision helps
Examples of how teams in different industries apply it.
Manufacturing
Defect detection, assembly verification and quality control.
Retail and logistics
Shelf monitoring, package counting and label reading.
Safety and facilities
PPE checks, restricted-zone alerts and occupancy counts.
Mobile and fintech apps
Document and ID scanning, receipt capture and visual search.
Glossary
Computer Vision key terms
Plain-English definitions of the terms used on this page, so everyone on your team is speaking the same language.
Read the FAQ- Object detection
- Locating and labelling objects within an image or video frame.
- Image classification
- Assigning a whole image to a category, such as pass or fail.
- Optical character recognition (OCR)
- Converting printed or handwritten text in images into machine-readable text.
- Edge AI
- Running AI models on local devices instead of in the cloud.
Our process
How we deliver
Design first, build carefully, test everything. Every AI, web and app project moves through the same six stages, adapted to your requirements.
- Mockups approved before any code is written
- Tested across screen sizes and browsers
- Direct communication throughout
- Support after launch
Step 01
Discover and plan
We review your goals, requirements and existing systems, agree the scope and assemble the right team.
- Requirements review
- Agreed scope
- Project team
Step 02
Design mockups
Our graphic designer creates mockups first. Development starts only after you approve the design.
- Mockups
- Your approval
- Figma or XD files welcome
Step 03
Develop
We build to closely match the approved design, responsive on desktop, tablet and mobile, with content that is easy to edit.
- Responsive build
- Editable content
- Progress updates
Step 04
Test
We test spacing, typography, interactions and functionality against the mockup, across screen sizes and browsers.
- Design accuracy
- Cross-browser
- Mobile and tablet
Step 05
Launch
We deploy to production, make final refinements and hand over everything you need.
- Production deployment
- Final refinements
- Handover
Step 06
Support
We stay available for maintenance, updates, performance improvements and the next release.
- Maintenance
- Updates
- Performance
FAQ
Computer Vision: frequently asked questions
Common questions about our Computer Vision service.
What can computer vision detect?
Computer vision can detect and classify objects, people, text, defects, gestures and events in images or video, as long as there are representative example images to train or test on.
Can computer vision run without an internet connection?
Yes. Models can be optimised to run on edge devices, cameras or smartphones so they work offline and keep images on site.
How many images are needed to train a model?
Many projects start with a few hundred labelled images per class using pre-trained models, then improve as more data is collected. We assess your images before training.
Can you use our existing cameras?
In most cases yes. We check resolution, frame rate, lighting and angles during planning and advise on any changes needed.
Does computer vision need special cameras?
Often not. Many projects use existing CCTV or smartphone cameras. Some inspection tasks benefit from specific lighting, resolution or frame rates, which we assess during planning.
Can it handle video in real time?
Yes. With optimised models and suitable hardware, video can be analysed in real time, either on edge devices close to the camera or on cloud servers.
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