Industry 02 · Physical AI — Robotics & UAVs
Software for machines that move through the real world.
Robotics and UAV systems answer to physics: hard latency budgets, unreliable networks, and safety envelopes that can't be patched later. We build autonomy, perception, and fleet infrastructure that holds up outside the lab.
What we build
The full stack of autonomy, from control loop to control room.
Autonomy & control
Planning, control systems, and hardware integration for robots that operate safely alongside people and property.
Perception & computer vision
Detection, tracking, and scene understanding tuned to run on embedded compute — not just a datacenter GPU.
UAV platforms
Flight operations software, telemetry, payload data pipelines, and ground-control integration for drone programs.
Simulation & testing
Software- and hardware-in-the-loop validation so behavior is proven before the first field deployment.
Edge & embedded deployment
Model optimization, over-the-air updates, and offline-first design for devices at the edge of the network.
Fleet operations
Telemetry, health monitoring, and mission dashboards that keep a growing fleet observable and dispatchable.
From bench to field
Validated in stages, trusted in production.
Physical AI fails differently than web software — so our delivery process is built around staged, evidence-backed rollouts.
- Hardware-in-the-loop validation — behavior proven against real sensors and actuators before deployment.
- Staged rollouts — bench, then controlled field trials, then production — with rollback at every step.
- Safety envelopes & failsafes — defined limits and graceful degradation, designed in from the start.
- Offline-first architecture — systems that keep working when connectivity doesn't.
- Telemetry from day one — every unit observable, every incident reconstructable.
Proof in this vertical
Related case studies.
Physical AI & Aerospace
Live
Power BI ops tooling
A maintenance operating system for pre-flight operations
Pre-flight maintenance workflows at Bombardier — mapped from reality, then tooled.
Bombardier · 2022
Physical AI & Aerospace
ETL
one pipeline, schema-drift-proof
The data layer behind a web-based drone simulation platform
The relational core and drift-tolerant ingestion behind a Next.js drone-simulation platform.
UofT Institute for Aerospace Studies · 2022
Building something that flies, drives, or grips?
Tell us about the platform, the environment it operates in, and where the current stack falls short.