Solution — Applied industrial AI
AI that answers to a production metric.
Factory copilots, predictive maintenance, quality prediction and production optimisation — built on your historian and MES data, evaluated against a baseline you already trust.
Approach
The model is the easy part. The data contract is the project.
Most industrial AI projects stall for the same reason: tag names are inconsistent, timestamps are unsynchronised, and the label that would define "a defect" was never recorded. We start by fixing the data contract, because no architecture recovers from bad ground truth.
We evaluate every model against the incumbent method — the experienced operator, the SPC rule, the fixed maintenance interval. If the model does not beat that baseline on held-out production data, it does not deploy. That discipline kills perhaps a third of proposed use cases early, which is the point.
Deployment is on your terms: at the edge on Jetson or industrial PCs where latency and network isolation matter, or in your cloud tenancy where scale and retraining matter. Models are versioned, monitored for drift, and have a defined rollback.
Scope
What is included.
Delivered as a defined scope with acceptance criteria, not as a time-and-materials estimate that drifts.
- 01Data readiness assessmenttags, sync, labels, coverage
- 02Predictive maintenancevibration, thermal, current signature
- 03Quality predictionprocess signals to outcome models
- 04Root cause analysisevent correlation across the line
- 05Production optimisationthroughput and changeover
- 06Factory copilotlanguage interface over plant systems
- 07Vision language modelsflexible inspection and reasoning
- 08Anomaly detectionunsupervised on process signals
- 09Model deploymentedge or private cloud, versioned
- 10Monitoring & retrainingdrift detection, rollback path
Applications
Where this is used.
Factory copilot
A language interface over historian, MES and maintenance records, answering with traceable tag-level evidence.
Predictive maintenance
Remaining-useful-life estimates from vibration, thermal and motor-current data that planners can schedule against.
Quality prediction
Models that flag process drift before parts leave tolerance, shifting inspection from detection to prevention.
Root cause analysis
Automated correlation across events, process values and quality outcomes to shorten investigations.
Production optimisation
Bottleneck discovery from event logs, with quantified throughput upside per intervention.
Industrial AI agents
Agents that plan and act within defined authority, with every action logged and reversible.
Stack
Tools and platforms.
Related
Where this connects.
Very few problems are solved by one discipline. These are the capabilities most often combined with this one.
FAQ
Questions about applied industrial ai
Do we need a data lake before we can do anything?
No. Most valuable first projects need one machine, one year of history and one clearly defined outcome. Building a plant-wide data platform before proving a use case is the most common way industrial AI programmes lose their budget.
Will our data leave the plant?
Only if you want it to. We deploy at the edge on isolated industrial hardware where required, or inside your own cloud tenancy. We do not require data egress to our infrastructure to build or run a model.
How do you prove the model is actually working?
Against a documented baseline on held-out production data, using the metric that matters commercially — false-reject rate, unplanned downtime hours, scrap percentage. We report the confidence interval, not a single flattering number.
What is a factory copilot in practical terms?
A question-answering interface over the systems you already have. An engineer asks why line 3 slowed after 14:20 and gets a response citing the specific tags, alarms and process values that changed, with links to the raw records. It retrieves and reasons over your data; it does not invent plant knowledge.
Next step
Ready to scope your next automation programme?
Send us a drawing, a cycle-time target or a line layout. Our engineers respond with a technical assessment — not a brochure.