The first Physics-Native AI
for Runtime Operations
One physics-native platform, applicable wherever AI interacts with the physical world from edge to cloud. Grounded by 27 governing physics from thermal to fluids, all at 60Hz or faster - deterministic, repeatable, and validated. Everything that Physical AI promised - delivered.
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Physics-native AI for the physical world
Manifold is a groundbreaking State Space world model that calculates physics. Not guessing from patterns, but through the fundamental equations that govern how the physical world actually works. Predictions, optimizations, and activities are validated before action occurs - ensuring reliable, safe, and accurate operations.
Wherever AI meets the physical world
Physics generalizes in fundamental ways that pattern-recognition inference cannot. The same governing equations apply across materials, domains, and industries. Without re-training, without re-engineering - requiring minimal compute and data resources.
Manufacturing
Battery, concrete, composites, steel, pharma
Robotics
Contact-rich manipulation, deformable materials, fluids
Autonomous Systems
Navigation, terrain physics, spatial intelligence
Spacetech
Satellite data, flood prediction, orbital mechanics
AI Stack Augmentation
Physics layer for LLMs, VLAs, robotics platforms
Scientific Discovery
Route optimization, materials research, engineering
Advancing Physical AI through groundbreaking research
Research: Resolving Coupled Orbital Drag Physics at Runtime within a Deterministic World Model
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Research: Spatial Constraint Satisfaction Does Not Scale In Auto-Regressive Models
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Research: Structured State with Higher-Fidelity Observations Dramatically Improves Spatial Reasoning
Read moreLatest news from Niva
Web Summit Vancouver 2026: Niva brings Manifold's runtime physics to cross-industry audience
Niva attended Web Summit Vancouver 2026 (May 12-14), introducing Manifold to a cross-industry audience of founders, investors, and enterprise buyers after a satellite-industry debut at SATShow in March. Conversations clustered around manufacturing, robotics, and materials discovery, with the runtime-versus-design-time distinction and a head-to-head comparison against Physical Intelligence's π0.5 as the recurring anchors.
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Closing the coupling chain: Applying Manifold's runtime physics to orbital state prediction
Object-specific orbital state prediction rests on a physics chain from material exposure through gas-surface interaction to ballistic behavior to orbit realism. The literature supports each link. It does not close the chain as a continuous runtime process. Niva's Manifold platform resolves the chain end-to-end at microsecond solver latency with deterministic commits - a world's first.
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Demo: Satellite ADCS, bus shutdown prediction with coupled physics warm start recovery
Manifold predicts a satellite's attitude, orbit, and thermal state through a complete bus shutdown window, then hands the warm-start prior to ADCS recovery. Conventional cold-start recovery takes 5 to 12 minutes, sometimes longer. Manifold cuts it to under 2 minutes. Try it on the Demos page.
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