Data and artificial intelligence competence lab
Turn your operational data into clear, reliable decisions. In this lab, you work with experts to convert sensor, vision, process, and IoT data into actionable insights and tailored AI solutions. We focus on industrial and infrastructure contexts where performance and uptime matter. Together, we design data-driven tools for monitoring, prediction, and decision support.
The themes of the data & AI lab
Diagnostics
- Feature engineering on sensor, process and telemetry data
- Detection of abnormal behaviour and fault signatures
- KPI modelling and performance baselining
- Hybrid physics-informed and data-driven modelling
- Deployment of PoC monitoring pipelines and dashboards
Predictive maintenance
- Time -series analysis and modelling
- Degradation detection under varying operating conditions
- RUL estimation with confidence intervals
- Physics-informed machine learning
- Prototype predictive maintenance workflows
Decision support AI
- Explainable AI (XAI) techniques
- Physics-informed and domain-aware AI models
- Context-aware inference mechanisms
- Human-in-the-loop validation workflows
- Deployment of decision-support dashboards and APIs
Multimodal AI
- Synchronization and spatial-temporal alignment of heterogeneous data
- Early and late fusion architectures
- Computer vision and scene understanding models
- Event detection and interaction modelling
- Digital twins for infrastructure monitoring
Data readiness
- Data profiling and quality assessment
- Gap and constraint identification
- Definition of data pipelines and feature architectures
- AI-readiness assessments and technical roadmaps
Unlock the 4 pillars of Data & AI Innovation
From predictive maintenance to smarter decision-making: discover how our four Data & AI pillars help manufacturers turn data into real operational impact.
How Sirris supports you
Assess your data, define use cases and identify the most relevant AI opportunities and rovide a clear roadmap based on effort, feasibility and value.
Build proof-of-concepts and validate models on real data and demonstrate feasibility, quantify impact and prepare for industrial deployment.
Support companies in transferring advanced AI techniques and know-how to operational teams, ensuring adoption, robustness and long-term value.
Analyse sensor, process and visual data, engineer features and develop AI models for diagnostics, prediction and perception, tailored to your industrial context.
Build collaborations with industrial partners and launch national and European R&D projects to develop advanced AI solutions, linking industry needs with cutting-edge research.
Equipment and testing capabilities
Multimodal data processing & fusion
Tools and pipelines to synchronise and align heterogeneous data sources (sensor, vision, spatial) and enables development of advanced prediction and digital twin solutions.
Time-series and monitoring pipelines
Analytical workflows for analysing large-scale industrial data streams, detecting anomalies and building continuous monitoring dashboards.
AI model validation and sharing environments
Environments for validating AI models on real industrial data and sharing solutions via containerized deployments (e.g. Docker) and ensure reproducibility, easy integration, scalability and smooth transfer of models to industrial environments.
Edge and deployment testing
Testing of AI models in constrained environments (edge, real-time systems), enabling validation of robustness, latency and scalability.
