Integrate intelligence into machines, production lines, and physical systems using open-source robotics AI. From perception pipelines to production-deployed ROS2 nodes, we handle the full robotics AI stack.
Traditional industrial automation costs €500K–2M per production line and requires years of custom programming
Proprietary robotics ecosystems lock you into single vendors with inflexible upgrade paths
Industrial cameras and sensors generate data that existing rule-based systems cannot interpret intelligently
The gap between computer vision research and production-deployed robotics remains wide for most teams
Maintenance complexity: vision-language model updates require robotics engineers, ML engineers, and domain experts simultaneously
Six stages from physical process mapping to production-deployed intelligent systems.
Identify which physical tasks are suitable for AI augmentation vs full automation. Define success criteria: accuracy, speed, fallback behaviour, human oversight requirements.
Select cameras, depth sensors, LiDAR, and edge compute based on the task requirements and environmental constraints.
Build the computer vision stack: YOLO v11 for detection, SAM 2 for segmentation, depth models for 3D understanding, calibration and preprocessing.
Integrate vision-language models (Pixtral Large, LLaVA) for scene understanding and decision-making — the bridge between perception and action.
Implement ROS2 nodes connecting the AI pipeline to robot actuators, PLCs, or control systems. Safety systems, emergency stops, and human override controls.
Edge compute deployment, OTA model update pipeline, failure detection, and monitoring dashboards for uptime and accuracy metrics.
You run manufacturing, logistics, or agricultural operations with repetitive physical tasks that currently require human inspection or intervention. You're exploring AI-enhanced quality control, pick-and-place automation, or intelligent monitoring. You want open-source robotics AI, not proprietary vendor lock-in.
Not necessarily. We integrate AI into your existing machinery, cameras, and sensors first — no new robot hardware required for perception and inspection use cases. For manipulation tasks (pick-and-place, assembly), we work with Pollen Robotics Reachy 2 or your existing robotic arms.
The Physical AI consulting service (at /services/physical-ai) covers strategic advisory, the Physical AI Stack™ framework, and architecture design. This service covers hands-on implementation — writing ROS2 nodes, deploying computer vision pipelines, and integrating AI into your production line. They're complementary, and many clients engage both.
YOLO v11 achieves 85–95% detection accuracy on structured tasks after calibration to your environment. Accuracy depends heavily on lighting, camera placement, and the variability of your physical process. We always run a feasibility assessment before committing to accuracy targets.
With proper documentation and training, ROS2 is maintainable by any engineering team. We deliver full source code, ROS2 package documentation, and a 2-day knowledge transfer session. Ongoing support is available.
Let's discuss how this service can address your specific challenges and drive real results.