Visual inspection & detection
Identify agreed objects, conditions or defects in images and video. Evaluate false alarms and missed detections separately because their operational consequences differ.
Build / See the detail. Surface the exception.
Computer vision development.
Build computer vision systems for inspection, detection and image-based workflows. We connect visual models to the cameras, applications and people who need to act on their outputs.
Discuss this with usThe opportunity
Turn images into useful operational signals.
People repeatedly inspect images, video or physical items for the same patterns. A model may help with that work, but changing lighting, camera angles and rare exceptions can make a laboratory demonstration unreliable in practice.
We define what needs to be detected and what action should follow. Data collection, labelling and evaluation reflect the actual operating environment. Edge or cloud deployment is selected around latency, connectivity, privacy and maintenance needs.
What we can do.
Image-based workflows
Connect detection results to review queues, asset records and business actions. Let operators inspect the relevant image region and correct a classification.
Edge & deployment engineering
Assess camera inputs, compute constraints and connectivity. Implement monitoring and update processes suitable for the chosen environment.
Tangible outputs
What you receive.
The exact scope is agreed around your workflow. A typical engagement can include:
- A visual-task specification and data plan
- A labelled evaluation set and model pipeline
- An operator review experience
- Integration with the agreed operational system
- Deployment and model-update documentation
Illustrative workflow
An inspection image needs review
Capture the image → detect a possible issue → highlight the region → an operator checks it → record the inspection outcome.
An example of how this capability can fit a business process.Define what better looks like.
We agree useful measures before implementation and compare them with the current workflow.
- ↗Missed detections and false alarms
- ↗Performance across real operating conditions
- ↗Inspection workload and useful interventions
Your questions, answered.
Can we use our existing cameras?
We assess their output, placement, resolution and access options. Existing hardware may be sufficient, but that should be tested with real samples.
Can the system run locally?
Edge deployment may be suitable where latency, connectivity or data requirements favour it. Hardware and model choices depend on the task.
How do you handle rare defects?
Evaluation needs to consider how rare cases are collected, labelled and reviewed. A fallback process remains important when evidence is limited.
Useful before you start
Questions worth exploring.
How to choose your first AI project
Start with work you can observe, data you can access and an outcome you can measure.
From AI pilot to production: what changes?
A working demonstration is a starting point. A released service also needs integration, ownership and a dependable response to failure.
What does AI implementation really cost?
A useful budget covers the whole workflow: data, integrations, evaluation, people and the cost of keeping it running.
A useful next step
Bring us the work
that should work better.
One process, one product idea, or a question about where to start.