AI Implementation
Build the system.
Inspection workflows, work-order document AI, operational assistants and agents, delivered with acceptance testing, monitoring and rollback.
Industrial & Energy
We build AI that has seen your failures. Before it watches your line.
Inspection, work-order intelligence, operational assistants and agents, built into production and trained on data captured in your own plant.
Why industrial AI stalls
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.
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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.
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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.
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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.
What you can buy
Buy the system. Buy the data. Or both, from one team.
System Data
Data Collection
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.
- 210,000+ contributors
- 50+ countries
Operated from Norway, with EEA processing where required.
One inspection · frame to verdict to record
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.
Review station Live frame · your line
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The flag is the model's.
Trained on your defect classes and rare events, calibrated before production.
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The frame is yours.
Captured on your line, by the cameras you run, under your light. Your parts, not stock imagery.
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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.
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.
Where it applies
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.
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Visual QC and inspection
A model that flags what your cameras see, and a reviewer who rules on the flag.
- System
- Model flagging, review queues, severity routing, acceptance testing and monitoring in production.
- Data
- Rare defects and edge cases captured on the line with fixed and worn cameras, staged to quota, low light and occlusion included.
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Maintenance and work orders
Maintenance paperwork turned into a controlled workflow, with review where it belongs.
- System
- Document AI for work orders and inspection reports: extraction, classification, routing to the right crew, review queues and approvals.
- Data
- Work orders, reports and field photographs collected from your sites and crews, exceptions and hard cases included.
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Operational assistants on the floor
Assistants that answer from approved procedures and manuals, and escalate by your rules.
- System
- Assistants grounded in your documentation, permission-aware retrieval with source traceability, human handoff, multilingual where the crew is.
- Data
- Industrial speech captured in operation, PPE-muffled and against machine noise, in the languages your crews speak.
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Sensors, robots and episodes
Machine and robot data made trainable, and agents that act only inside approval gates.
- System
- Event workflows with approval gates and failure recovery, multi-step agents with trace-level evaluation, pilot to production with rollback.
- Data
- Sensor streams timestamp-aligned and calibrated, task demonstrations and inspection episodes from your robots and operators.
Connected Delivery
Observe the failure A flag the reviewer rejected, or a defect the model waved through.
Isolate the gap Which defect class, shift, camera or sensor the training set never saw.
Capture the data Collected on your line, to quota, with provenance on every file.
Improve the system Retrained, re-gated and re-integrated behind the same approvals.
Rerun the evaluation The same evaluation set, plus the failure, before anything ships.
Release Into operations with monitoring and rollback. Then the next failure.
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.
Where the data lives
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
Questions
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.
Scope a project
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.
The brief Tell us the line, the failure modes that cost you, and the data or system you need. We reply with a feasibility read.