Capability portfolio

Six domains. One engineering standard.

Every capability below is delivered the same way in a consortium: as containerised, version-controlled, documented components with defined interfaces, automated tests and a deployment path to the pilot site.

Privacy-preserving AI Vision & edge AI Generative AI Robotics Digital twins Trustworthy AI
01 · Privacy-preserving AI & cryptography

Learn from sensitive data without ever exposing it

The hardest problem in European data-driven projects is legal, not technical: health records, financial data and industrial telemetry cannot leave their owners. We solve it with cryptographic machine learning, training and inference with mathematical privacy guarantees, not just policy promises.

  • Cross-institution federated learning (Flower, custom aggregation, non-IID robustness)
  • Fully homomorphic encryption (FHE) for inference on encrypted data
  • Zero-knowledge machine learning (ZKML): verifiable inference via zk-SNARKs
  • Secure multi-party computation and differential privacy budgets
  • Post-quantum-ready cryptographic protocol selection
PyTorchFlowerOpenFHE / SEAL Circom / EZKLOpacus
Institution A data stays on-site Institution B data stays on-site Institution C data stays on-site Secure aggregator encrypted updates only differential privacy (ε) Global model returned to every site
Federated learning: model updates travel, patient and industrial data never do

Typical consortium contribution

Privacy work package: threat modelling → protocol design → reference implementation → integration with partner models → pilot validation with DPO/DPIA support.

EU relevance

GDPR-critical use-cases in health (Cluster 1), security (Cluster 3), data spaces and trustworthy AI topics (Cluster 4), European Health Data Space.

02 · Computer vision & edge AI

Perception that runs where the data is created

Cloud round-trips are too slow and too expensive for industrial perception. We compress, quantise and deploy state-of-the-art vision models onto edge hardware - keeping latency low, bandwidth minimal and data on-site by design.

  • Real-time image recognition, object recognition and multi-object tracking (YOLO family, transformer detectors)
  • Human pose estimation for safety analytics and ergonomics
  • Industrial defect and anomaly detection with limited labelled data
  • TensorRT optimisation: INT8 quantisation, pruning, distillation
  • Fleet deployment on NVIDIA Jetson, and TinyML on microcontrollers
YOLOTensorRTNVIDIA Jetson OpenCVONNXDeepStream
Real YOLO footage: people detected and tracked with bounding boxes and IDs

Real YOLO inference: detection & tracking with persistent IDs. More demos →

Real footage · Ultralytics YOLO

03 · Generative AI & LLM systems

Sovereign language AI for European organisations

We build generative systems that respect European constraints: open-weight models, fine-tuned on your domain, deployed on your infrastructure, with retrieval grounded in your documents, no data leaving the EU, no dependency on external APIs.

  • Enterprise retrieval-augmented generation (RAG) with hybrid search and citation grounding
  • Domain-specific fine-tuning of open-weight LLMs (LoRA, QLoRA, DPO)
  • Multi-agent workflows with strict human-in-the-loop safety protocols
  • Evaluation harnesses: factuality, robustness, multilingual (incl. Greek) quality
  • On-premise and EU-cloud serving with GPU-efficient inference
Open-weight LLMsvLLMLoRA / DPO pgvector / QdrantLangGraph
Documents your corpus, on-prem Chunk + embed multilingual · EL/EN Vector DB hybrid search User query audit-logged LLM open-weight · fine-tuned LoRA / DPO · on-prem Answer with citations
Sovereign RAG: retrieval-grounded answers, zero data egress from your infrastructure

Typical consortium contribution

Language-AI work package: corpus preparation → fine-tuning → RAG integration → multilingual evaluation → pilot deployment with usage analytics and safety monitoring.

EU relevance

Digital Europe AI adoption, Cluster 4 GenAI topics, public-sector digitalisation, European language-technology equality.

04 · Robotics & autonomous systems

Autonomy validated in simulation, proven on site

We develop ROS 2 perception, navigation and manipulation stacks with a simulation-first methodology: every behaviour is stress-tested virtually before a robot moves in an operational environment, cutting pilot risk and cost.

  • Autonomous mobile robots (AMR) for logistics and industrial inspection
  • Robotic manipulation cells: bin picking, machine tending and assembly (MoveIt 2)
  • SLAM, sensor fusion and navigation in GPS-denied environments
  • Vision-guided manipulation and human–robot collaboration safety
  • Gazebo / Isaac Sim digital environments with Sim2Real transfer
ROS 2Nav2Gazebo / Isaac Sim ros2_controlMoveIt 2
Real robot arms performing learned manipulation tasks

Real footage: learned robotic manipulation. More demos →

Real footage · Hugging Face LeRobot

05 · Digital twins & predictive maintenance

A living model of the physical asset

We fuse IoT telemetry, vision streams and historical data into dynamic virtual replicas, enabling zero-risk what-if simulation, energy optimisation and failure prediction for factories, grids and buildings.

  • Sensor-fusion pipelines from PLC/SCADA, IoT and vision sources
  • Predictive-maintenance models with uncertainty quantification
  • Energy-consumption forecasting and optimisation loops
  • Scenario simulation for planning and operator training
  • Open standards: OPC UA, MQTT, NGSI-LD / FIWARE data models
Time-series MLFIWARE / NGSI-LD OPC UA · MQTTGrafana
Physical asset factory line · grid · building PLC / SCADA · IoT · vision Digital twin live virtual replica failure forecasting · what-if energy optimisation telemetry → ← predictions · actions KPIs: downtime ↓ · energy ↓ · forecast accuracy ↑
Closed twin loop: sense → model → predict → optimise → act

Typical consortium contribution

Twin platform work package: data-model design → connector development → predictive analytics → dashboard and decision-support delivery → pilot KPI measurement.

EU relevance

Cluster 4 manufacturing partnerships (Made in Europe), Cluster 5 smart grids and positive-energy districts, resilient infrastructure topics.

06 · Trustworthy AI & MLOps

AI the consortium can defend, to reviewers and regulators

Every model we ship comes with the evidence around it: documentation, explainability, robustness tests and monitoring. This is how project outputs survive review, procurement and the EU AI Act.

  • EU AI Act risk-classification support and conformity-oriented documentation
  • Explainability tooling (SHAP, attention analysis, counterfactuals)
  • Robustness and adversarial testing, bias and drift monitoring
  • Model cards, datasheets and FAIR-aligned data management plans
  • Reproducible pipelines: CI/CD for ML, experiment tracking, versioned datasets
MLflowDVCDocker / K8s SHAPEvidently
Data + training versioned · tracked Evaluation gates bias · robustness · XAI Sign + doc model cards Production EU AI Act evidence Monitoring drift · incidents · KPIs retrain loop
Every model ships with its evidence: gates, signatures, documentation, monitoring

Typical consortium contribution

Cross-cutting trust work package: AI governance framework → documentation templates → automated evaluation infrastructure → compliance reporting across all technical WPs.

EU relevance

Trustworthy-AI topics across all clusters; every project deploying AI in regulated domains needs this role filled.

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