Reduce compute time
Replace repetitive numerical evaluations with fast, validated emulators.
AI for Science
More discovery. Less compute.
We combine physical principles, experimental data and machine learning to accelerate simulation, exploration and scientific decision-making.
R&D acceleration
Learn from every experiment and simulation.
When experiments are scarce and simulations expensive, AI must do more than predict: it must incorporate system knowledge and help choose the next action.
Our approach combines data-driven models, physical constraints and uncertainty quantification.
Explore intelligently
Identify promising regions of a design space with fewer trials.
Decide with uncertainty
Produce recommendations paired with actionable confidence estimates.
AI for simulation
Fast models without losing physical meaning.
Accelerate parametric studies, optimization and real-time control by learning from CFD, FEA, multiphysics or process-model outputs.
High-fidelity emulators
Build fast approximations of expensive solvers to explore thousands of configurations in moments.
- Reduced-order modeling and deep learning
- Error- and regime-based validation
- Integration with optimization loops
Hybrid digital twins
Combine a physical model with operational data to estimate, forecast and optimize a system’s real state.
- Sensor-driven model updating
- Diagnostics and prognostics
- In-operation optimization
Inverse design & optimization
Start with target performance and identify the geometries, materials or parameters that make it possible.
- Multi-objective optimization
- Bayesian optimization
- Constraint-aware design
Physics-guided learning
Incorporate known laws, invariances and balances to improve robustness when data is limited.
- Physics-informed loss functions
- Mechanistic–data hybrid models
- Out-of-distribution generalization
Beyond simulation
An intelligence layer across the science.
From instrument to publication, we can connect data, models and knowledge in one research workflow.
Scientific imaging
Segmentation, detection, reconstruction and automated measurement for complex imagery.
Experiment design
Choose the next experiments to maximize information and reduce resource use.
Materials & molecular discovery
Explore candidate spaces, predict properties and prioritize validation.
Literature intelligence
Extract, connect and query scientific findings and internal knowledge.
Instrument data analytics
Denoising, classification, drift detection and quality control for experimental signals.
Scientific MLOps
Traceable pipelines, versioning and validation for research models and data.
Accelerate your research
Which step in your scientific cycle is slowing you down?
Bring us an expensive simulation, a complex dataset or an exploration problem.
Assess the opportunity ↗