Intelligence for the physical world.
Amastack combines physics-based models, real-time worlds, robotics, and measured evidence to make complex systems visible and testable.
Perception, simulation, policy learning, and robot execution.
zₜ = h(xₜ) + νₜ
Every scale of the physical system.
Choose a discipline to bring its models, technologies, and visual world into focus.
Simulation becomes an engineering instrument.
Synthetic environments, custom solvers, controlled scenarios, and physically grounded replay make behavior inspectable before deployment.
One loop. Six coupled capabilities.
Action creates evidence. Evidence improves the next model. Select a stage or directional flow.
Turn the physical world into evidence.
Cameras, synthetic sensors, hardware signals, and telemetry become synchronized observations with known provenance.
zₜ = h(xₜ) + νₜ
From a contact patch to an operational world.
Build reality before touching it.
Run contact, fluid, rigid-body, sensor, and actor simulations as one measurable system rather than isolated demos.
M(q)q̈ + C(q,q̇)q̇ + g(q) = τ
Models need governed systems.
Model runtime
Run perception, planning, reinforcement learning, or control policies with explicit state, timing, and resource boundaries.
Harnesscontrol plane
Human decisions become testable context.
Operator and teammate roles exercise communication, intervention, handoff, and workload inside repeatable worlds.
Built around the physical problem.
Robots learn inside physical limits.
Humanoids, manipulators, vehicles, and coordinated machines can be evaluated through simulation, policy testing, hardware-aware control, and replay.
Conceptual engineering visualization—not customer evidence.
Small team. Deep systems.
Direct collaboration from model to working system.
Amastack is a Swedish deep-engineering studio working across simulation, robotics, real-time software, computer vision, interactive 3D, and digital twins.
Define
DevMind makes the harness visible.
Model context, evaluate a plan, execute controlled work, and keep consequential evidence visible.
Structure, relationships, constraints, and intended changes are represented before execution begins.
Bring us the hard physical problem.
Start with what must be sensed, simulated, learned, controlled, or measured—and the environment in which it must work.
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