Catch coating defects on the line, not in the lab.
AI quality assurance for paint and coating production. Real-time defect detection and yield analytics deployed on the factory floor, where the data is generated and where regulators want every decision auditable.
// not in the lab · inside the perimeter
Lab-based QA is too slow for production-scale coating.
Coating defects propagate fast. By the time the lab confirms a problem, the line has produced hours of compromised product. Manual inspection is inconsistent and does not scale across shifts, lines, or sites.
Cloud-based vision AI is a non-starter on the factory floor. Production data is competitively sensitive, regulators expect on-premise evidence trails, and the line cannot wait on a round-trip to a remote inference service.
Real-time inspection, on the line, with the trail.
Limit Standard: Coatings runs vision and process-analytics models against live line data, classifies defects against the standard, and writes evidence the auditor accepts.
- 01
Real-time defect detection and classification
Vision models flag coating defects as the work passes the camera. Defects are classified against your defined standard, with confidence scores and per-frame evidence captured for every decision.
- 02
Yield analytics across runs, shifts, and sites
Aggregate metrics across product runs, shift transitions, and plants. Trend deviations surface before they become quality incidents.
- 03
Auditable evidence trail for every decision
Every classification lands in the evidence store with the frame, the model version, the standard reference, and the operator response. Audit reviews reconstruct the line state, not just the outcome.
- 04
Integrations with the systems already on the floor
Line PLCs, MES platforms, and quality systems feed the model and consume its output. No parallel system to maintain. The existing operator interfaces stay in control.
Runs on the line. Not in the cloud.
Limit Standard: Coatings deploys inside the plant network on customer-provided hardware. Vision pipelines run at the edge near the cameras. Model weights, line data, and evidence trail stay on premise.
When connectivity to the corporate WAN is intermittent or air-gapped, the application continues to operate locally. Aggregated metrics sync upstream when the link is available, governed by the customer DPA.
- Where it runs
- Edge nodes near production cameras and a plant-floor coordination node. No off-site dependency at runtime.
- What stays on premise
- Line imagery, defect labels, model weights, evidence records, and operator-response data. All controlled by the customer.
- Who controls the data
- The customer is the controller. Limit Systems is a processor under the per-deployment DPA. No telemetry by default.
- Evidence the auditor can use
- Per-decision records with frame, model version, standard reference, timestamp, and reviewer action. Export to PDF and JSON for inspection.
Every application runs on the same sovereign platform. The identity layer, the audit trail, the policy engine, the evidence retention. Built once, used by all. Explore the platform →
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Sensitive-data discovery and redaction
Discover, classify, and redact personal and confidential data before AI ever touches it. PII, financial identifiers, privileged information: all governed before the model sees them.
See it in your environment.
Walk us through your perimeter, your evidence requirements, and the systems already in place. We'll show you how the deployment looks.