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
The Recipe Is Not the Part: Recovering cure-cycle margin with instance-informed runtime physics
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Research: Free Intelligence Economics - Paradigmatic, Technical, and Systemic Shifts in AI Discourse
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Research: NSBU - Open-Source Deterministic Runtime for Rigorous Navier-Stokes Diagnostics
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Grounded adjudication: The unnamed scarcity and what it means for Physical AI
Phoenix Astrid's Medium article "When Intelligence Becomes Free", published in January 2026, described AI intelligence as commoditizing toward zero cost, with attention becoming the last scarce human resource. Anyone who has used LLMs at depth has seen a different pattern: output that reads well at first glance, then reveals generic framing, insights that are off, references that are fabricated. Niva Platforms' September 2026 position paper, Free Intelligence Economics, names what is actually scarce: grounded adjudication - the capacity to evaluate whether an output is accurate, appropriate, reasonable, and competent. In the physical world, the stakes are far more significant. Niva examines native determinism as a solution to the scarcity problem in Physical AI.
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Navier-Stokes 2026: Niva publicly releases NSBU, highlighting architectural divergence in physical AI
Niva Platforms has publicly released NSBU, an open-source deterministic runtime for incompressible three-dimensional Navier-Stokes simulation, on GitHub. The release arrives at a moment of unusually high public attention on the Navier-Stokes equations, one of mathematics' seven Millennium Prize problems, following two AI-assisted proof announcements on 8 September 2026. Coverage described AI as having 'solved' it. The prize remains officially unclaimed, and the more important story is what the two approaches, and Niva's contribution, reveal about where AI in the physical sciences is actually heading.
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SmallSat 2026: Niva defines physics-native understanding and control for space operators
Niva attended the 40th Annual Small Satellite Conference in Salt Lake City (24-26 August 2026) with a clear architectural claim. Manifold is a runtime coupled physics engine: small, fast, edge-deployable, hyper-accurate, and end-to-end deterministic. It represents a class of architecture in which the operational computation itself is deterministic and runs continuously against a live world-state, and it opened conversations across the show floor with primes, government agencies, and university research groups working adjacent physics.
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