Board support & silicon bring-up
Boot chains, BSPs, devicetree, peripheral drivers, clocks and interfaces for MCUs and application processors.
- Board and SoC enablement
- Sensor and bus drivers
- Reproducible platform builds
Physical AI enablement · Intelligent machines
Engineering the Embedded Foundation for Physical AI.
Intelligent machines need more than perception and planning models. We engineer the embedded software that connects AI-enabled applications to sensors, controllers, networks and actuators—and validate how that software behaves on real hardware.
From AI decisions to dependable device behavior.
Robotics OEMs and industrial device teams need a maintained, secure and testable platform beneath their perception and planning software.
Real-time control interfaces
Zephyr RTOS, FreeRTOS, interrupt design, scheduling and measured timing at the edge.
- Low-latency data paths
- MCU and application-processor partitioning
- Timing and fault analysis
Embedded Linux & ROS 2
Linux BSPs, Yocto, U-Boot, middleware and ROS 2 connectivity for robotic and autonomous products.
- Kernel and device integration
- Middleware interfaces
- Product distributions
Sensor & actuator integration
Firmware and protocols linking perception systems, motion controllers, industrial networks and physical outputs.
- I²C, SPI, CAN, UART and Ethernet
- Device diagnostics
- Interface-level verification
Security & device lifecycle
Hardware Root of Trust where supported, secure boot, device identity, signed firmware and update/recovery design.
- Threat-model-led hardening
- Secure updates and rollback
- Long-term maintenance
Emulation & hardware validation
Testable engineering from software models and CI to hardware-in-the-loop results and physical measurements.
- Build and test automation
- Hardware-in-the-loop checks
- Latency and interface measurements
Deliverables
An engineering foundation, not another robot brain.
Physical AI models decide what to do. ArivEmb focuses on the software layers that allow intelligent products to communicate with hardware, operate within measured timing constraints and remain maintainable in the field.
- Architecture and target-hardware assessment
- BSP, driver, RTOS and Linux enablement
- ROS 2 and edge-application interface integration
- Hardware validation with documented limits
- Security, update and lifecycle engineering
Where Embedded DX fits
AI-assisted engineering for the platform beneath Physical AI.
Embedded DX assists with requirements, source-connected development, configuration, builds and validation evidence. It is an embedded engineering tool, not a robot-control foundation model, autonomous policy or a substitute for product-level safety assessment.
Explore Embedded DX →- Requirements and hardware-documentation context
- Driver, BSP, devicetree and configuration work
- Reviewable builds, tests and human approval
- System safety and product compliance assessed by the responsible engineering team
Start with the technical context
Bring us the hard embedded problem.
Tell us the board, the current software baseline and the target milestone.
Discuss a Physical AI integration