How we use AI to unleash full potential of your data

AI WITH SCITIS

Bringing AI and agents into real industrial operations

We focus on AI that is directly connected to your industrial reality: forecasting maintenance windows and failures, predicting quality parameters during production and supporting teams with AI agents in their daily work. Using tools like BigQuery Canvas and the Data Science Agent on top of our data framework, we turn IoT data into concrete decisions, visualised in our own framework, Looker Studio, Tableau or your existing systems.

AI LAYER

Data foundation and administration

The scitis.io framework collects and combines data from large fleets of similar assets into one central, consistent data layer. This makes it much easier to start new AI and analytics use cases and keep KPIs comparable across machines and sites.

VIRTUAL SENSORS

getiting Values before they actually measured

AI uses live process data to predict measurement parameters that are only available later in the process, for example from lab tests or end-of-line checks. This enables real-time quality insight and earlier parameter adjustments

PREDICTIVE MAINTENANCE

From reactive fixes to planned interventions

AI models analyse time series and event data from your assets to forecast when maintenance will be needed. This turns unplanned downtime into planned interventions that fit your production schedule.

FAILURE PREDICTION

Predicting failures before they happen

By learning typical patterns before faults occur, AI detects rising failure risk early. Operators receive clear early warnings and can react before small deviations become costly breakdowns.

AI AGENTS

From AI models to actions with agents

With tools like BigQuery Canvas and the Data Science Agent, we build AI workflows and bring the results into daily operations. Dashboards and AI agents deliver predictions via our framework, Looker Studio, Tableau or your own systems and trigger concrete actions.