Artificial Intelligence
Delivering Decision-Grade Intelligence Through Advanced Machine Learning
What We Do
Multimodal AI Fusion: Seamlessly integrate satellite imagery, sensors, weather data, and proprietary datasets into unified intelligence frameworks
Transformer & Vision Models: Deploy state-of-the-art architectures (Vision Transformers, Attention mechanisms) for pattern recognition at scale
Reinforcement Learning: Train intelligent agents that learn optimal strategies through simulation and real-world deployment
Secure LLM Integration: Deploy RAG-enhanced language models on-premises with full data control
Why It Matters
Transform Data Into Decisions
Extract actionable insights from overwhelming complexity, reducing analysis time from weeks to hours
Mission-Critical Reliability
Robust models engineered for real-world operational conditions with adversarial resilience
Explainability at Scale
Understand why AI recommends specific actions through SHAP, attention maps, and interpretability frameworks
Security First
Keep proprietary data and intelligence under your control with on-premises deployment options
How We Do It
Data Assimilation: Modern techniques (4D-Var, Ensemble Kalman) to fuse heterogeneous data sources
Physics-Informed Learning: Combine domain knowledge with deep learning for robust generalization
Distributed Training: Leverage HPC supercomputers (CINECA Leonardo) for rapid model development
Uncertainty Quantification: Bayesian and ensemble methods to quantify confidence bounds
Computer Vision & Remote Sensing
Autonomous Intelligence from Satellite and Aerial Imagery
What We Do
Object Detection & Classification: Autonomously identify threats, infrastructure, and anomalies across planetary-scale imagery
Semantic Segmentation: Pixel-level classification for land cover, urban sprawl, and environmental monitoring
Spectral-Spatial Analysis: Mine multispectral and hyperspectral data for nuanced Earth system insights
Change Detection: Temporal AI networks for monitoring evolving threats and resource dynamics
Why It Matters
Scale Beyond Human Limits
Monitor millions of images and detect planetary-scale changes in hours, not weeks
Superior Accuracy
AI models exceed human baseline performance while eliminating fatigue-induced errors
Subtle Threat Detection
Identify camouflaged assets, covert infrastructure, and anomalies hidden from traditional methods
Real-Time ISR
Enable operational responsiveness with near-instantaneous analysis of incoming imagery
How We Do It
Vision Transformers (ViT): Next-generation architectures for superior patch-based understanding
Spectral-Spatial GNNs: Graph neural networks modeling spatial-spectral relationships explicitly
Transfer Learning & Domain Adaptation: Leverage pre-training and adapt to new sensors/regions
Few-Shot Learning: Achieve high accuracy with minimal labeled data via meta-learning
Weather & Climate Modeling
Decision-Grade Forecasting from Hours to Decades
What We Do
Hyperlocal Nowcasting: 1-5 km resolution weather forecasts with 12-72 hour lead times
Climate Downscaling: Regional-scale climate projections from global models with uncertainty quantification
Space Weather Forecasting: Predict solar flares and geomagnetic storms to protect satellite and grid infrastructure
Ensemble Modeling: Probabilistic forecasts with credible uncertainty bounds for decision-making
Why It Matters
Operational Precision
Navy route planning, military ops, and emergency response require local weather at decision-relevant resolution
Strategic Resilience
Long-term climate intelligence informs infrastructure and energy investment with defensible confidence
Asset Protection
Early space weather warning prevents satellite, comms, and power grid disruptions
Quantified Risk
Probabilistic forecasts with uncertainty bounds support rigorous risk-based decision-making
How We Do It
Data Assimilation: 4D-Var and Ensemble Kalman Filters for optimal integration of observations
Physics-Informed Neural Operators: Deep learning that respects atmospheric dynamics
Multi-Model Ensembles: Combine global forecasts with regional expertise for robust downscaling
Bias Correction & ML Postprocessing: Learn systematic forecast errors and correct them
Agent-Based & System Modeling
Digital Twins and Reinforcement Learning for Complex Systems
What We Do
Digital Twin Design: High-fidelity virtual replicas of cities, ports, supply chains, and battlefields
Multi-Agent Simulation: Agents representing people, vehicles, organizations with emergent behaviors
Deep Reinforcement Learning (DRL): Agents that learn optimal strategies through thousands of simulated scenarios
Systemic Risk Analysis: Model cascading failures across interdependent infrastructure networks
Why It Matters
De-Risk High-Stakes Decisions
Test strategies safely in virtual environments before committing resources in the real world
Uncover Emergent Dynamics
Discover non-obvious patterns and unintended consequences that analytical methods miss
Optimize Complex Systems
Find superior policies for port operations, logistics, and emergency response automatically
Resilience Planning
Identify vulnerabilities and design interventions to withstand disruptions and shocks
How We Do It
Cognitive Architectures: Agents modeled with bounded rationality, perception-action loops, and dynamic social network interactions
Multi-Agent RL (MARL): Agents learn to coordinate with each other for system-level objectives
Network Modeling: Represent interdependencies and critical chokepoints explicitly
HPC Acceleration: Scale simulations to millions of agents across supercomputing clusters
HPC & Quantum-Ready Computing
Scaling Intelligence Across Supercomputers and Distributed Systems
What We Do
Distributed AI Training: Scale deep learning across GPUs and HPC clusters for petabyte-scale datasets
Optimized Inference: Deploy models on edge, GPU, and secure on-premises infrastructure with low-latency execution
Model Compression & Quantization: Reduce model size and memory footprint without sacrificing accuracy
Quantum Algorithm Design: Prototype quantum-classical hybrid workflows for future readiness
Why It Matters
Accelerate Time-to-Value
Train large models in days using your existing HPC infrastructure, not months
Real-Time Operations
Achieve millisecond-latency inference for autonomous systems and ISR processing
Maximize Hardware ROI
Extract full performance from GPUs, TPUs, and supercomputing systems through expert optimization
Future-Proof Advantage
Position your organization to harness quantum computing benefits as the technology matures
How We Do It
PyTorch/TensorFlow at Scale: Expert optimization of distributed training pipelines (data parallelism, gradient checkpointing)
European HPC Expertise: Direct experience with top-tier European HPC infrastructure: CINECA Leonardo, Vienna Scientific Cluster 5 (VSC-5)
ONNX & Model Portability: Deploy models across heterogeneous hardware seamlessly
Quantum Algorithm Design: Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and variational quantum algorithms with classical simulation