Advanced AI Models
Artificial intelligence models trained with real operational data for prediction, classification and optimisation. Each model is built on the plant’s own history and evaluated against its real behaviour before it is put to use.
Advanced AI models for industry: what the service covers
We build AI models trained on a plant’s own operational history for prediction, classification and optimisation. The first question in every project is whether an advanced model is needed at all: if the problem fits a clear condition, a rule or basic statistics solve it more cheaply and transparently. Advanced models earn their place when many variables interact in non-obvious ways, when the input is an image, or when the task is optimisation under constraints.
Data comes before modelling. We review the history for coverage (does it contain examples of what you want to predict?), quality and context, and continuity of the signals the model will need in production. The model lifecycle then runs through training, validation against real plant behaviour on unseen periods, supervised operation alongside the team’s judgement, and continuous drift monitoring with retraining when the plant changes. A model without drift monitoring quietly degrades, which is worse than having no model.
Typical applications include equipment degradation forecasting, which underpins predictive maintenance and early alerts, visual quality inspection and process optimisation. The AI unit always works on data that is already connected; see all solutions at Captia AI.
How it connects to the system
Captia AI is a capability of Captia Technology. This solution relies on data Connect integrates and priorities Consulting defines.
Key concepts
Other solutions
- Operational Dashboards
Real-time visualisation of the operational data that matters, designed for decision-making, not contemplation. We build dashboards on top of connected plant data, with the views each role needs to run day-to-day operations.
- Intelligent Reporting
Automatic reports that synthesise what is relevant in the operation without manual intervention. Connected plant data becomes periodic, comparable reports that are generated and distributed automatically to the people who need them.
- Automatic Workflows
Automatic workflows that execute actions when defined conditions are met in the operation. Each workflow is designed on real plant data, chaining the notifications, records and tasks that today depend on manual steps.
Frequently asked questions
- When do you need an advanced AI model instead of simple rules?
- If the problem can be described with clear conditions, such as a temperature threshold or a combination of machine states, a rule or a statistical calculation solves it better and cheaper. An advanced model makes sense when many variables interact at once, when the relationship between them is not obvious, or when the problem involves images, text or constrained optimisation. We always evaluate the simple option first.
- How much historical data is needed to train an industrial AI model?
- It depends on the problem, but coverage matters as much as volume: the history must contain examples of the behaviour you want to predict, including the anomalous cases. A year of data with no recorded failures is of little use for predicting failures. That is why each project starts by reviewing what data exists, its quality, and whether a capture period is needed before training.
- What happens to the model once it is running in the plant?
- A model is not installed and forgotten. Plants change: equipment gets replaced, raw materials vary, recipes are adjusted, and a model trained on the previous history gradually loses accuracy. The lifecycle therefore includes drift monitoring, comparing predictions against what actually happens, and periodic retraining when the deviation justifies it.
- Are the models trained on generic data or on my plant’s data?
- On your plant’s data. Every site has its own equipment, operating regimes and quirks, and a generic model does not capture them. At Captia each model is built on the operation’s own history and evaluated against its real behaviour before being put to use. The precondition is that the data is connected and accessible, which Captia’s industrial connectivity unit can solve if it is not yet the case.