Every inference.
Every joule.
Every machine.
The full physical AI stack. Microcontrollers to datacenter GPUs. Real-time inference, simulation, digital twins, fleet orchestration. Energy-metered at context-switch granularity.
Robots don't know what anything costs.
Every robotics stack runs inference blind. No energy budgets, no per-task metering, no way to know if a 7B VLA model fits an edge device until it fails in the field.
Six layers. One energy budget.
From the OS kernel to fleet-wide orchestration. Every layer speaks joules.
pai-os
Capability-based microkernel. Hybrid scheduling: fixed-priority, EDF, round-robin. Energy budget enforcement at context-switch granularity. Kernel-scheduled inference. One API from MCU to SoC.
Edge AI
17 compute backends. CPU, CUDA, Metal, Vulkan, wgpu, TPU, NPU, Hailo-8, ONNX, GGUF, TinyML, neuromorphic. Dispatch picks the right backend for the model shape, latency target, and energy budget. Q15 fixed-point on bare MCU.
Simulation
Rigid-body physics, contact dynamics, sensor simulation, and scenario generation. 8 built-in scenarios from warehouse pick-and-place to outdoor navigation. Energy-metered per simulation step.
Digital Twin
Bi-directional sync between physical robots and their digital counterparts. State mirroring, drift detection, predictive maintenance alerts, and what-if scenario testing against the live twin.
Fleet
Manage thousands of heterogeneous devices from a single pane. OTA model updates, rolling deployments, energy-aware task assignment, telemetry aggregation, and automatic rollback on anomaly detection.
Safety
IEC 61508 SIL-3 and ISO 13849 PLd safety certification built into the runtime. Watchdog timers, safe-state fallback, emergency stop propagation, and continuous safety integrity monitoring.
One runtime. MCU to datacenter.
The same API runs on a $4 microcontroller and an NVIDIA Jetson Thor. Write once, deploy to any compute tier, and know the energy cost before it ships.
Right Compute, Right Time
The scheduler knows every backend's power profile, latency envelope, and memory ceiling. It assigns each inference to the cheapest compute that meets the SLA. Automatic. At runtime.
7B Models on Edge
Per-layer mixed-precision quantization for vision-language-action models. Vision encoder at INT4, action head at FP16, language backbone at INT8. Sensitivity-aware. Action fidelity is never sacrificed for compression.
Train Without Sharing Data
Federated averaging with differential privacy gradient perturbation and secure aggregation. Each robot improves the fleet model without sending raw sensor data to the cloud.
MCU First, Cloud Last
16-level deterministic cascade. Cheapest compute first. Only escalates when confidence drops below threshold. Most inferences never leave the device. You see the energy receipt for each level.
The only physical AI stack priced in joules.
Every inference, every sensor read, every motor command. Metered and receipted. Know the energy cost before deployment. Optimize after.
Built for Certification
Real energy measurements feed directly into safety and sustainability certifications. Not estimates. Not vendor claims.
From $4 MCU to $10,000 SoC.
One API. 70+ platform variants. The same code compiles for a Cortex-M7 and a Jetson Thor.
From prototype to production.
Simulate in software. Test on hardware. Deploy to fleet. The energy budget follows the model through every stage.
Run scenarios in the physics engine. Measure energy per task.
Compress models to target hardware. Verify accuracy retention.
OTA push to fleet. Energy budgets enforced per device.
Pay for compute. Not licenses.
No per-robot fees. No per-seat pricing. $5 minimum balance to start. Pay only for the energy your workloads consume. Every operation metered in joules.
pai-os, edge inference, simulation, digital twin. All run locally on your hardware. No internet required.
Fleet orchestration, cloud training, federated learning, large-model inference. Billed at energy cost.
- ✓ pai-os kernel + 10 OS crates
- ✓ 17 compute backends (CPU to TPU)
- ✓ Physics simulation + 8 scenarios
- ✓ Digital twin with live sync
- ✓ Fleet orchestration + OTA
- ✓ IEC 61508 / ISO 13849 safety
- ✓ Energy metering (picojoule precision)
- ✓ 70+ hardware platforms
- ✓ Everything in pay-as-you-go
- ✓ Volume energy pricing
- ✓ Custom safety certification
- ✓ Private fleet regions
- ✓ Dedicated support & SLA
- ✓ SSO, audit logs, compliance
- ✓ Invoiced billing
Ship robots that know their cost.
Install the SDK. Run a simulation. See the energy receipt. Deploy to hardware when you're ready.
cargo add pai-os edge-ai physical-ai-sim