Full-stack physical AI engineering.
From perception layer to enterprise deployment — we engineer every layer of the physical AI stack.
01 — Core capability
Embodied AI Systems
Engineering the architecture that lets AI models operate in unstructured physical environments. We build the interfaces, execution layers, and feedback loops that connect AI reasoning to physical action.
$ ai.perceive(env)
→ sensors: [vision, depth, imu, tactile]
→ latency: 12ms
→ confidence: 0.97
$ ai.decide(observation)
→ policy: production_v3
→ action: MOVE_TO_TARGET
→ safety_check: PASS
$ ai.execute(action)
→ executed in 8ms
→ outcome: SUCCESS
Perception
Multi-modal sensor fusion
Integrating visual, depth, IMU, and tactile sensor streams into coherent environmental representations — at the latencies physical systems demand.
Execution
Real-time policy execution
AI policy execution with hard real-time constraints. Deterministic control loops that maintain safety guarantees even under adverse operating conditions.
Safety
Layered safety architecture
Safety constraints embedded at the architecture level — not as a software layer, but as a fundamental design principle across every component.
Interfaces
Hardware abstraction layer
Standardized interfaces across diverse physical platforms — enabling AI systems to be developed once and deployed across multiple hardware configurations.
02 — Core capability
Physical AI Infrastructure
Cloud and edge infrastructure designed from the ground up for physical AI workloads — not adapted from web-service architecture. Built for the latency, reliability, and security constraints that physical systems demand.
// infrastructure_stack
Connectivity
Low-latency secure tunnels
End-to-end encrypted, low-latency communication between cloud infrastructure and physical systems — designed for sub-200ms response requirements.
Observability
Real-time telemetry
Live telemetry streams, event logging, and audit trails across all physical AI systems — with storage and query infrastructure for retrospective analysis.
Security
Enterprise access control
Role-based access control, session management, and authentication designed for multi-user enterprise environments operating physical AI systems.
Deployment
On-premise and cloud options
Infrastructure deployable in enterprise-managed environments for data-sensitive and regulated industries — without sacrificing capability or observability.
03 — Core capability
Real-world Validation
Closing the sim-to-real gap requires validated testing against the full complexity of physical environments. We build the environments, tooling, and methodology that make real-world evaluation practical.
// validation_metrics
// Typical improvement vs. sim-only dev
Environments
Real-world test environments
Physical testing environments configured for specific deployment contexts — controlled conditions that still capture the variance that breaks sim-validated systems.
Data
Session data & replay
Synchronized recording of sensor streams, telemetry, and execution logs — enabling reproducible evaluation, retrospective debugging, and dataset generation.
Methodology
Benchmarking protocol design
Standardized evaluation methodologies that produce defensible, comparable performance data — not vendor-curated demos, but reproducible numbers.
Cross-platform
Multi-system evaluation
The same task, the same environment, the same metrics — across different physical platforms. Giving enterprises the data to make defensible system selection decisions.
Ready to talk specifics?
Tell us about your physical AI challenge.
We'll tell you whether we can help.