Engineering intelligence
from first principles.
We are building a new class of foundation model that understands the language of engineering—physics, mathematics, and causal reasoning.
What is an SFM?
A Scientific Foundation Model (SFM) is a large-scale model trained to reason with scientific structure—equations, constraints, and empirical data—enabling physically consistent prediction, validation, and design across engineering domains.
Science-Native Learning
We integrate conservation laws, symmetries, and known constraints into training objectives to move beyond text fluency.
From Simulation to Synthesis
SFMs compress iteration cycles: rapid exploration → verifiable designs → deployable solutions, with model-assisted optimization.
Composable by Domain
Adaptable to aerospace, energy, and engineering via modular priors and calibration against domain benchmarks.
Beyond Language: The SFM Core
LLMs master language. SFMs aim to master the laws of science and engineering.
LLM: Pattern Matching
- Trained on text & code corpora
- Statistical correlations
- Describes engineering concepts
- No inherent physical guarantees
SFM: Causal Reasoning
- Trained on simulations & equations
- Encodes constraints (conservation, symmetry)
- Generates verifiable designs
- Targets physics-consistent outputs
How We Build
Rigor over Hype
Testable claims, reproducible pipelines, and honest reporting.
Compound Ownership
Small teams own literature → design → training → eval → integration.
Engineering Excellence
Brings scientists + engineers together.
Velocity with Guardrails
Move fast, measure twice—peer review and safety protocols are default.
Transparent Collaboration
- Open code and experiment artifacts; encourage reproducibility in all projects.
- Frequent cross-team syncs, sharing of insights, and public benchmarks.
- Document decisions (why & trade-offs), not just the outcomes.
Responsible Innovation
- Prioritize data privacy, bias mitigation, and verifiability in model outputs.
- Build for energy efficiency, resource-aware design, and environmental impact.
- Embed safety, fairness, and verification tests early in model and system design.
Collaborations & Advisors
We co-design benchmarks and share ablations with select research and industry partners.
Follow Our Progress
Request our technical brief or get notified when we release evaluations, publications, or new roles.