Industry
Operational intelligence for industrial environments: dashboards, alerts, prediction and decision automation on real operational data, from the first visualisation to models that anticipate failures and deviations.
Operational intelligence for industrial environments
Most industrial plants do not lack data; they lack data that works. Machines log signals and systems accumulate history, yet daily decisions still rely on calls, spreadsheets and memory. Captia AI is the intelligence layer on top of industrial data: real-time operational dashboards, predictive maintenance and early alerts and decision automation built on real operational data, including industrial digital twins.
Projects follow a practical progression: visibility first, then alerts, then prediction, then automation of repetitive decisions. Each piece must trigger a concrete action; anything else is noise. The precondition is connected data, covered by the connectivity layer for industry in Captia Connect. The starting point is one bounded use case, validated with the plant team before scaling.
Frequently asked questions
- What is the difference between having plant data and having operational intelligence?
- Having data means machine and process signals reach a platform. Operational intelligence means that data triggers decisions: a dashboard the shift manager acts on, an alert that warns before a failure stops the line, a rule that executes automatically what used to require a spreadsheet. Captia AI works on that second layer.
- Do I need connected machines before applying AI in my plant?
- Yes, at least the signals relevant to the use case. Analytics and prediction feed on real operational data, so connectivity is the precondition. If that layer is missing, Captia’s industrial connectivity area solves it first. You do not need to connect the whole factory, only the perimeter of the first use case.
- Where should an industrial AI project start?
- With a bounded, measurable case: visibility over one line, alerts on a problematic asset or automation of a repetitive flow. Build on the data available, validate with the plant team, then expand. Predictive models come later, once there is enough history and trust in the data.
- Does AI replace the plant team?
- No. The goal is to remove monitoring and manual data handling from the team, not their judgement. Alerts and automatic rules handle the repetitive part; decisions with context, such as stopping a line or bringing forward maintenance, remain with people, now with better information in front of them.