Computer vision, LiDAR & machine learning
Recognise what is there, measure where it is, and turn that understanding into a useful action. We combine machine-learning (ML) computer vision with LiDAR and 3D sensing for inspection, counting and site monitoring. Start with one real job, prove it works, then expand.
Computer vision with machine learning
Computer vision extracts useful information from images and video. Machine learning lets a model learn patterns from examples: which object is in the frame, where it is, or whether a part shows a defect. Text recognition can turn a visible label into a value your system can use.
We choose or adapt models around your objects and working conditions. A useful inspection needs examples of normal work, faults and ambiguous cases, followed by testing on images the model has not trained on. The output should be a count, a recorded observation or an alert your team knows how to handle.
LiDAR adds depth and distance
LiDAR stands for Light Detection and Ranging. It uses laser measurements to capture distances to surfaces. A 3D scan produces a point cloud: a set of measured points describing the shape and position of the surroundings.
That makes it useful when a task needs geometry as well as an image: mapping a space, locating obstacles, measuring clearance or comparing the shape of an area between scans. We select the sensor and validate its measurements against the range, surfaces and conditions of the actual job.
Bring recognition and 3D sensing together
A camera model can identify a pallet. LiDAR can add measured information about its position and the space around it. Where both are useful, we align the camera and LiDAR data so the recognised object and the measured geometry refer to the same place and moment.
That combined result can feed a dashboard, an ERP record or a robotics workflow. We use the sensors the decision needs, and route uncertain results to a person for review. For systems that also need to move, see our robotics and physical AI work.
What this can do for your team
Inspect and verifyCheck for missing parts, visible defects or unreadable labels, and attach the image evidence to the result.
Count and locateCount items, check whether a bay is occupied and add depth measurements when position matters to the next action.
Map and monitorCapture 3D site geometry and compare scans to flag changes for review, from a fixed installation or a mobile platform.
Prove one decision before scaling up
We start with the observation you need and what should happen next. Then we collect representative images or scans, set up the sensors, and test the model and measurements on real work. We check missed detections, false alarms, measurement error and response time against agreed acceptance criteria.
Lighting, blocked views, changing layouts and unfamiliar objects all belong in that trial. Once the result is useful, we connect it to your existing workflow and monitor performance as conditions change. Decisions with safety consequences keep a person in the loop.
Before you brief a camera job
Most of the decisions that determine whether a vision deployment works are made before any model is chosen: which of the four jobs it really is, which kind of mistake costs you more, and whether the conditions on site are stable enough to detect anything reliably.
Read the full explainer - what a camera can actually do, why "99% accurate" is not an answer, and what to have ready first.
From the homepage? This expands the Computer vision & LiDAR card in the first services grid. Jump back to that grid.
Have a vision or 3D sensing job in mind?
Tell us what you need to recognise, measure or track. We will start with one decision and the camera, LiDAR or combined setup needed to support it.