Physics-native AI
for the physical world.
Manifold provides machines and systems with an understanding of the physical world. Not through pattern recognition or brute-force training, but through the foundational equations that govern how the physical world actually works.
Accurate
98.9% zero-shot accuracy on fluid dynamics. 77% on complex rigid-body tasks. Versus 39% for NVIDIA using advanced training. Physics calculates, it doesn't guess.
Reliable
Deterministic predictions: the same inputs produce identical outputs, every time. No drift, no hallucination, no model collapse. Fully auditable and traceable.
Safe
Every action is physics-validated before execution. The platform will not allow non-conformal actions: physics impossibilities, material constraints, or hardware limits.
Encode. Predict. Validate. Adapt. Execute.
Manifold ingests real-time sensor data, calculates multi-domain physics continuously, and validates outcomes before actions are taken.
Proven across domains
Manifold applies wherever AI interacts with the physical world. Every new domain validates the architecture rather than breaking it.
Manufacturing Intelligence
Real-time process optimization across battery formation, concrete production, composites, and steel. Predicts outcomes before completion, identifies deviations, optimizes within constraints.
Robotic Manipulation
98% accuracy vs 14% for Physical Intelligence on identical hardware. Zero-shot. Contact-rich environments with deformable materials, fluids, and granular substances.
Route Optimization
Matched purpose-built algorithms on the travelling salesman problem, scaling to 1M+ points. Manifold was never designed for this. Physics generalizes.
Flood Prediction
Two-week coastal flooding forecast for the Netherlands using ESA Sentinel satellite data. The same physics-native approach applied to an entirely different domain.
Spatial Intelligence
Terrain classification from sensor data, vehicle dynamics coupled with terrain physics. Autonomous navigation in unmapped and novel environments.
AI Stack Augmentation
Physics intelligence as a layer that integrates with existing AI platforms, giving LLMs, VLAs, and robotics systems the physical understanding they lack.
Why it's different
No training required
No massive datasets, no simulation-to-real transfer, no teleoperation. Physics generalizes inherently: the rheology of concrete is the same physics as battery slurry or pharmaceutical gel.
Order-of-magnitude compute efficiency
Linear O(n) scaling runs on standard hardware. Training under $10K versus ~$2M for VLA competitors. Estimated serving costs roughly 10% of comparable approaches at scale.
New domains in weeks
Modular plugin architecture enables new physics domains without re-engineering or re-training. Six integrated domains today: rheology, thermal, mechanical, fluids, electrochemistry, granular.
Production-ready
500K+ lines of production code. v6.0 stable, v7.0 in development. Six provisional patent applications with 114+ claims. This is not a research prototype.
AI guesses. Manifold is certain.