AI Research & Engineering Company
We build AI systems
for science and engineering.
We help teams understand complex systems, generate solutions that work under real-world constraints, and build systems that keep adapting as conditions change.
What We Build
Platforms and systems.
Core Capabilities
Three things every system we build can do.
Understand complex systems
Discover structure, behavior, causality, and patterns that help teams reason more clearly.
Generate under real constraints
Create candidates already shaped by rules, limits, and trade-offs that matter.
Adapt after deployment
Keep AI systems learning and improving as contexts change.
CDE
A causal dynamics engine for science and engineering.
Causal Dynamics Engine
CDE discovers directed causal structure, governing equations, and causal mechanisms from the data your team already has — so decisions rest on mechanism and causation, not correlation alone.
- Discovers directed causal graphs with probabilities, identifiability analysis, and falsification tests.
- Extracts governing equations grounded in causal structure, not curve fitting.
- Designs targeted experiments to resolve ambiguous causal edges and maximize information gain.
- Validates every causal claim with negative controls before promotion.
- Works across physics, biology, chemistry, energy, finance, and industrial systems.
- Available through APIs, SDKs, CLI, MCP, and private deployments.
MatterSpace
Valid generation under real-world constraints.
Platform for Constrained Generation
MatterSpace generates candidates that already satisfy key constraints — so teams spend time evaluating viable options instead of filtering invalid ones.
- Start from target properties, feasibility rules, and trade-offs — constraints shape generation, not post-hoc filtering.
- Lattice generates materials and energy designs with crystallographic validity built in.
- Vital generates longevity and epigenetic reprogramming candidates under biological constraints.
- Origin, Algo, and Tessera extend constrained generation to protein design, algorithms, and structural design.
- Return multiple viable options across trade-offs instead of one brittle answer.
- Works across materials, molecules, chips, algorithms, and biological design surfaces.
ACI
Continual learning and adaptation after deployment.
Adaptive Continual Intelligence
ACI keeps deployed AI systems learning and improving — per-tenant updates, device-local memory, and edge adaptation without full retraining or cloud-only workflows.
- ACI Inference updates one tenant or workflow inside a shared service without retraining the backbone.
- ACI Personal Agents keep memory, reset, snapshot, restore, and erase on the user's own device.
- ACI Edge Runtime runs bounded local adaptation on robots and embedded systems under strict latency limits.
- Plasticity, stability, and exact unlearning are the operating contract across all ACI surfaces.
- Supports shared services, local devices, robotics, and edge environments.
- Add ACI Safety & Policy only when the host product needs hard enforcement or signed evidence.
It’s quite mind-blowing that you can explain almost everything — from the subatomic universe to galaxies — from a control systems theory perspective.
Research
Published research with validation data.
Tell us what you want the system to do.
Whether you need to understand a system, generate viable candidates, or keep a deployed model adapting — we can help you get there.