AI Engineering for Robots, Drones and Autonomous Machines
Perception and fusion that hold up when sensors degrade, models that fit the compute on board, and the evidence to explain what happened in the field.
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Tell us the machine
Teams building mobile robots, field and warehouse machines and civilian drones bring us three kinds of problem. Fusion that loses its position when GPS drops or a sensor is obscured. Perception models that are accurate on a workstation and too slow on the onboard computer. And fleets in operation whose incidents nobody can reconstruct because the right signals were never logged.
We do not work on military, defence or weapons systems.
Where We Help
Fusion, Onboard Compute and Fleet Evidence
Separate engagements, each scoped to your platform and operating conditions.
Sensor Fusion
Reliability
Fusion pipelines that detect a degraded sensor and keep localisation within measured bounds.
Onboard Deployment
Cost
Port and quantise perception models to the onboard compute and measure them under sustained load.
Fleet Incident Evidence
Reliability
Synchronised logging of inputs, confidence and decisions so incidents can be replayed.
How It Connects
Onboard deployment lands in the AI Porting & Deployment Pack and GPU Performance Engineering; perception and fusion in Computer Vision; fleet logging and incident analysis in the Production AI Monitoring Harness. Road vehicles are covered under Manufacturing & Automotive.
We have not published a robotics case study yet. Tell us about the machine and the condition it fails in.
Autonomous Machines AI FAQ
How do we keep localisation reliable when one sensor degrades or GPS drops out?
Fusion has to know how much to trust each sensor at each moment. We design and test the fusion pipeline so it detects a degraded lidar, camera or radar and down-weights it, and we measure the worst-case drift in the conditions your machines actually meet.
Will our perception model run in real time on the onboard compute?
We port and quantise the model to the onboard hardware and measure latency and accuracy under sustained load, including thermal throttling, so you know what the machine can run before it ships.
After a field incident, how do we show what the machine saw and why it acted?
By recording it before the incident. We set up logging of sensor inputs, fusion confidence and decisions with synchronised timestamps, so an incident can be replayed and its root cause found from evidence rather than reconstruction.
Working With Autonomy Teams
We work alongside your autonomy, hardware and operations teams; we do not replace them. Each engagement ends in something your team keeps and can re-run.
