Artificial Intelligence
Computer Vision
Use images or video to help a team check something they currently inspect by eye. We develop computer vision pilots for defined tasks such as object detection, photo checks and counting, then assess whether the result is reliable enough for the intended use.
Discuss this serviceChoose a visual check you can describe clearly
Is the required item visible in a delivery photo? Is a package label readable? How many objects cross a defined area? A useful computer vision project begins with a question that can be checked against real examples.
We work through what a correct result looks like and what happens when the image is unclear. A pilot might sort incoming photos into those ready to process and those needing review. It should help the team focus its attention without quietly treating uncertainty as a pass.
The camera conditions are part of the scope
Lighting, angle, motion and background can change the result. A model tested on clear sample photographs may perform differently on images captured at night or by a worker in a hurry.
We ask for representative examples, including the awkward ones. For a live camera system, we also review where processing takes place, how much video must travel over the network and whether the existing hardware can support it.
Measure the errors that matter to your business
A single accuracy percentage can hide an expensive problem. Missing a defect and flagging a good item for review have different costs. We look at both, along with the proportion of images that need human attention.
The first engagement is a feasibility pilot around one visual task. It produces evidence for a decision about integration or further data collection. We do not assume a successful demonstration means the system is ready for every camera and location.
A typical workflow
- 01Provide representative images
- 02Check the agreed visual condition
- 03Review uncertain results
What we would scope together
The exact work depends on your systems and the first useful release.
- ✓Visual task definition and sample-data review
- ✓A scoped image or video analysis pilot
- ✓Results showing missed detections and incorrect flags
- ✓Recommendation for review workflow and deployment
Computer Vision questions
Can you work with our existing cameras?
We check their resolution, frame rate, placement and available video interface. Depending on the task, a better camera angle can matter more than a more complex model.
Do we need labelled images?
For many custom tasks, yes. We need examples showing what is correct and what is not. An initial review will establish whether an existing model is suitable or additional labels are needed.
Can the system reject items automatically?
That decision depends on the cost of a mistake and the evidence from testing. We can begin with suggestions for an operator to confirm before considering automatic actions.
Not sure where to start with AI?
AI Navigator helps you assess a use case and plan a focused first pilot.
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Show us the check you want a camera to help with.
A set of representative, non-sensitive images and an explanation of a correct result will help us assess the task.