Catch Defects on the Line with AI Visual Inspection

On well-defined defects at speed, often yes, and it never tires. On rare or novel defects it can miss what a person would catch, so we design it to escalate uncertain parts rather than pass them. The honest position is that AI handles the high-volume known defects and frees inspectors for the ambiguous ones.

On the line, at the edge, because a cloud round trip is too slow for a per-part decision and line connectivity is often poor. We quantize the model to run next to the camera. The cloud is used for training and analysis, not the real-time pass-or-fail call.

Less than teams expect if we start from a pretrained vision model and fine-tune. We use active learning to label the most useful images first, especially the hard defects. We assess your existing images before quoting a labeling effort, because the hard cases matter more than raw volume.

We tune the model to your tolerance, because an over-sensitive inspector that fails good parts is as costly as one that misses defects. We measure both miss rate and false-reject rate, and route uncertain parts to a human rather than forcing a wrong call.

You do. Your line images, the trained model, and the code remain yours and can run entirely on-site. Operational imagery is sensitive, so we design for your ownership and hand over documentation, with no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A focused build around visual defect detection is a modest, weeks-long project, while a wider rollout across your line, cameras, and MES is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from fewer escaped defects and less manual inspection, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot usually reaches a working version in a few weeks, then tuning on real data, with wider rollout taking longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are defect detection, part classification, surface inspection, and reading part markings. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.