Cameras and scans that check the work so people can check the exceptions

The model is the easy part. Results depend on the images. Lighting that changes through the day, a camera that shifts a few centimeters, dust on a lens or a new supplier's packaging can all break a system that looked perfect in testing. So we begin with a capture plan: where cameras go, how items are presented, how many examples of each defect exist, and how rare defects will be gathered or simulated.

We then train on your labeled images and test on a separate set the model has never seen, reporting precision and recall for each defect type, not one blended score. A system that finds ninety out of a hundred defects but raises many false alarms may be worse for you than one that catches fewer and rarely cries wolf. You choose where to set that threshold, knowing what each setting costs in review time.

Reading documents is a different problem from inspecting objects. Arabic script is cursive and context-dependent, numbers may be Arabic-Indic or Western digits on the same page, and scans from older printers or phone cameras are often skewed or faint. Standard OCR trained on English fails quietly here. We test several engines on your own documents and add cleanup, layout detection and field validation so a date or an invoice total is checked against rules before it reaches your system.

Where a document type is stable, such as a delivery note, a template-based extractor works well. Where it varies, a vision-capable language model can read the layout, with a person confirming anything below a confidence line you set. Typical timelines run six to ten weeks for a first document pipeline and eight to twelve for an inspection pilot.

We build vision systems that look at products, materials, equipment and documents. We do not build facial recognition or systems that track or identify individuals, and where people appear in images, such as a safety check at a gate, the design counts equipment and blurs or discards the rest. This keeps the system inside the Personal Data Protection Law's principles of purpose limitation and data minimization, and aligned with SDAIA's AI ethics principles on privacy and human oversight.

Deployment is a business decision. An edge device next to the camera keeps images on site and works through network drops. A server in your own facility or an in-Kingdom data center suits heavier models and multiple cameras. Cloud processing is fastest to start but sends images outside your walls, so it is usually reserved for non-sensitive material. Hardware cost depends on camera count, resolution and frame rate, and we price it separately from the software.