--- title: "Industrial and energy AI: systems and plant data" url: https://ypai.ai/solutions/industrial-energy/ description: "AI for industrial operations, from visual inspection to operational agents, plus the sensor, audio, video and rare-event data those systems must have seen." source: "src/copy/routes (route /solutions/industrial-energy/)" --- # We build AI that has seen your failures. Before it watches your line. > AI for industrial operations, from visual inspection to operational agents, plus the sensor, audio, video and rare-event data those systems must have seen. Inspection, work-order intelligence, operational assistants and agents, built into production and trained on data captured in your own plant. AI Implementation · Data Collection · Connected Delivery ## The demo ran on good parts. Production asks three harder questions. The failure that costs you is the one nobody saw. A model watching your line has to know your failures, and be tested before it touches operations. ### Has it seen our failures? - Generic datasets are full of good parts. Your cost lives in the rare weld defect and the sensor that drifts before it fails. Data never captured on your line cannot teach a model your failures. ### Does it respect the plant? - On a live line a wrong action is worse than a wrong answer. Assistants and agents run inside approval gates, hand off to your people and stop at the safety boundary. ### Is it tested before it touches operations? - An evaluation set that represents your line, acceptance against agreed criteria, regression on every change, rollback after release. Nothing ships before it passes acceptance. ## Buy the system. Buy the data. Or both, from one team. ### One team owns the miss. The system and the data it runs on, built and fixed by the same people. Nobody hands the problem to another vendor. Operated from Norway, with EEA processing where required. ## The model flags. Your people rule. The record survives. A model trained on frames from your line lands a flag. A reviewer rules on it. The verdict is logged with its lineage. A flag is marked approved only after a person has ruled on it. Before a vision model is allowed to flag on your line, it passes a calibration gate. The video annotation it learned from meets tracking-accuracy thresholds of HOTA 0.65+ and IDF1 0.75+. That data also goes through five-stage consensus QA, with Cohen's and Fleiss' kappa measuring agreement between annotators. Screen contents are illustrative. Defect taxonomy, severity scale and acceptance thresholds are set per line in the project specification. Captured on your line · fixed camera Logged with its frame, model version and reviewer stage. ### The flag is the model's. - Trained on your defect classes and rare events, calibrated before production. ### The frame is yours. - Captured on your line, by the cameras you run, under your light. Your parts, not stock imagery. ### The verdict is human. The record outlives the shift. - Every routed flag gets a human ruling, logged with its frame, model version and reviewer stage. ## Where industrial AI earns its place. Four systems we build most often, in detail. Each runs on different data, captured inside the plant's own rules, and each stops where your people take over. ### Visual QC and inspection - A model that flags what your cameras see, and a reviewer who rules on the flag. - Model flagging, review queues, severity routing, acceptance testing and monitoring in production. - Rare defects and edge cases captured on the line with fixed and worn cameras, staged to quota, low light and occlusion included. - **Camera**: Fixed, on the line - **Model flag**: flag. Hairline crack · 0.87 - **Severity**: Routed for review Approved · human review · logged ### Maintenance and work orders - Maintenance paperwork turned into a controlled workflow, with review where it belongs. - Document AI for work orders and inspection reports: extraction, classification, routing to the right crew, review queues and approvals. - Work orders, reports and field photographs collected from your sites and crews, exceptions and hard cases included. - **Source**: Crew report, photo attached - **Extracted**: Asset · fault · location · priority - **Check**: Last inspection on this asset - **Mismatch**: flag. Reported fault differs from last inspection Routed to review · 1 exception ### Operational assistants on the floor - Assistants that answer from approved procedures and manuals, and escalate by your rules. - Assistants grounded in your documentation, permission-aware retrieval with source traceability, human handoff, multilingual where the crew is. - Industrial speech captured in operation, PPE-muffled and against machine noise, in the languages your crews speak. - **Operator**: Lockout procedure before the bearing change? - **Answer**: From the procedure, source cited - **Boundary**: flag. Isolation step needs a supervisor sign-off Escalation open · handoff to supervisor ### Sensors, robots and episodes - Machine and robot data made trainable, and agents that act only inside approval gates. - Event workflows with approval gates and failure recovery, multi-step agents with trace-level evaluation, pilot to production with rollback. - Sensor streams timestamp-aligned and calibrated, task demonstrations and inspection episodes from your robots and operators. - **Observation**: Part offset on the fixture - **Reasoning**: Approach deviates from the reference trajectory - **Action**: Re-plan the grasp, proposed - **Gate**: flag. Approval required before motion Held for approval · trace kept ## Build the system. Inspection workflows, work-order document AI, operational assistants and agents, delivered with acceptance testing, monitoring and rollback. ## Capture the plant. Sensor streams, industrial audio, camera video, rare events and robot episodes, collected in your environments with consent and provenance on every file. ## When the model misses, the team that built it fixes the data. A defect class the cameras never captured, a sensor that drifted, an evaluation set that does not represent the line. No hand-off between an integrator, a model vendor and a collection company. Each service line can be bought on its own. - A flag the reviewer rejected, or a defect the model waved through. - Which defect class, shift, camera or sensor the training set never saw. - Collected on your line, to quota, with provenance on every file. - Retrained, re-gated and re-integrated behind the same approvals. - The same evaluation set, plus the failure, before anything ships. - Into operations with monitoring and rollback. Then the next failure. ## Footage of your line at its worst deserves a strict jurisdiction. A Norwegian company under GDPR. Project data is processed in the EEA by default, and both storage and processing are documented. - **Jurisdiction**: Norway · GDPR-native - **Storage**: European by default - **Processing**: EEA by default - **Consent**: Documented per contributor - **Contracts**: Standard DPA terms · SCCs available - **Erasure**: 30-day end-of-contract SLA - **Withdrawal**: Workflow with audit trail - [Data residency in the EEA](https://ypai.ai/data-residency-eea/) - Data processing at YPAI ## Start with one line, one failure mode, one batch. We scope against your workflow, write the specification with you and test the loop on a pilot batch before anything touches operations. Scope · Specification · Pilot batch · Acceptance · Production A bounded pilot carries its own acceptance criteria. You judge the output against your own line. Briefs are treated as confidential. We are used to plants where defects are rare and hard to photograph. Tell us the line, the failure modes that cost you, and the data or system you need. We reply with a feasibility read. We come back with a feasibility read on your line and data. Enquiry details are treated as confidential. ## Questions plant and industrial AI teams ask ### Can we buy the system and the data separately? Yes. AI Implementation builds the system, Data Collection captures the plant, and each line can be bought on its own. Bought together as Connected Delivery, the team that built the model fixes the data when it misses. ### What data is captured in the plant? Sensor streams, industrial audio, camera video, rare events and robot episodes, collected in your environments with consent and provenance on every file. Rare defects are staged to quota, with low light and occlusion included. ### Who decides on a model's flag? A person. Every routed flag gets a human ruling, logged with its frame, model version and reviewer stage. A flag is marked approved only after a reviewer has ruled on it. ### What must a vision model pass before it flags on our line? A calibration gate. The video annotation it learned from meets tracking-accuracy thresholds of HOTA 0.65+ and IDF1 0.75+, and goes through five-stage consensus QA with Cohen's and Fleiss' kappa measuring agreement between annotators. ### Where does footage of our line go? YPAI is a Norwegian company under GDPR. Project data is stored in Europe and processed in the EEA by default, consent is documented per contributor, and data is erased within 30 days of the end of the contract. ### How does a project start? With one line, one failure mode and one batch. We write the specification with you and test the loop on a pilot batch, with its own acceptance criteria, before the system runs in operations.