We engineer vision systems that inspect products, count output and detect anomalies on production lines, integrating with your control systems to act in real time.
Industrial AI applies computer vision and machine learning to manufacturing operations: detecting defects on surfaces and assemblies, verifying packaging and labeling, counting products, and monitoring the state of the line itself. The goal is consistent, documented quality at the speed the process demands.
Human inspectors fatigue, and their judgments vary between shifts. Traditional machine vision based on fixed thresholds and templates works for highly controlled parts but fails when products vary naturally, defects are subtle or new, or lighting shifts. Each change to the product means re-engineering the rules.
Our industrial AI service delivers learned inspection models that generalize across natural variation, flag anomalies that were never seen in training, and connect to PLCs and MES systems so a detection becomes a reject, an alert or a record without manual intervention.
The building blocks we combine for each engagement. Scope is defined by the problem, not by a feature list.
Segmentation and classification models for scratches, dents, contamination, missing components and misalignment, trained on your parts and defect taxonomy.
Models trained on good parts alone that flag deviations from normal appearance, essential when defects are rare, varied or not yet catalogued.
Line-crossing counts on tracked items, with handling for overlaps, gaps and conveyor speed changes, feeding production dashboards in real time.
OCR and detection to confirm label presence, legibility, correct variant, date codes and seal integrity before product leaves the line.
Detecting stoppages, jams, missing operators, blocked zones and equipment states from existing or purpose-installed cameras.
Reject signals, alarms and inspection records delivered to PLCs, SCADA, MES and quality databases through industrial protocols and APIs.
Industrial deployments live or die on imaging quality and integration. We treat optics, lighting and I/O as first-class engineering problems, not afterthoughts to the model.
We characterize the defect catalogue, part variation and cycle time, then design camera placement, optics and lighting so defects are actually visible in the image before any model is trained.
Images are captured under production conditions with part identifiers, labeled against the agreed taxonomy and split so validation reflects real production mix.
Supervised detectors and segmenters handle known defects; anomaly models trained on good parts cover the unknown. Decision thresholds are set against the cost of false rejects versus escapes.
Inference runs on an edge device or line PC synchronized with triggers and encoders, and results are delivered to the PLC within the cycle time with deterministic latency.
The system runs in parallel with existing inspection, discrepancies are reviewed with quality engineers and acceptance criteria are verified before the system takes control of rejects.
Practical applications we design for, each anchored to an operational decision.
Metal, plastic, glass, textile and coated surfaces inspected for defects at line speed.
Confirming presence, orientation and completeness of components before the next process step.
Verifying labels, codes, caps, seals and fill levels on consumer and pharmaceutical lines.
Counting discrete items, bundles or units and sorting by class or quality grade.
Watching for stoppages, blockages and abnormal machine states to reduce unplanned downtime.
Consistent inspection criteria across every shift and line.
Detection of subtle and previously unseen defects.
Every inspected part documented with image and result.
Reject and alert actions executed automatically within cycle time.
Faster adaptation to new products through retraining instead of re-engineering.
Production insight from the same cameras that inspect.
Our five-stage process, applied to this service.
We review the process, defect catalogue, cycle time and existing inspection and collect initial images to assess feasibility.
Cameras, optics, lighting, triggering, compute placement and PLC integration are specified in a design your controls team reviews.
Production images are collected and labeled, models are trained and validated and the real-time integration is built and bench-tested.
Hardware is installed, the system runs in parallel with current inspection and results are reconciled until acceptance.
Borderline samples are reviewed, models are retrained for new variants and the system extends to adjacent inspection points.
Send us sample images of good and defective parts and your cycle time. We will tell you what is detectable and how we would image it.