Data & Simulation Platform
Develop and validate. The deepest layer of the stack and the least mapped — where the training data lives, where models get built, where data moves cleanly between tools, and where "it works" becomes a measurable claim instead of a demo.
Four named engagements, each scoped and repeatable, each backed by systems running in production.
Physical-AI Data
Turn every robot-hour into training data.
Collection, curation, and annotation pipelines for embodied-AI data: teleop demonstrations, multi-sensor episodes, edge-case libraries. Built so datasets compound across deployments instead of rotting in buckets — with the provenance and versioning your model team needs to trust what they train on.
Demonstration-capture infrastructure running across a 38-robot public-street deployment — every intervention a labeled episode.
Data Interoperability
Make robot data speak one language across every tool.
Schemas, converters, and connective tooling that move episodes, logs, and telemetry cleanly between capture, training, simulation, and analysis — MCAP, ROS 2 bags, and custom formats reconciled into one pipeline instead of a pile of one-off scripts.
ML Pipelines & MLOps
From a researcher's checkpoint to a robot in the field.
Training, evaluation, and deployment infrastructure: experiment tracking, model registries, eval harnesses, and the rollout path that gets a model onto hardware safely. The discipline of software CI/CD, applied to models that move machines.
Model deployment pipelines built for clients running perception and autonomy models in production fleets.
Simulation & Test
Make "it works" a falsifiable claim.
Sim environments, scenario libraries, regression suites, and hardware-in-the-loop rigs. Every autonomy update runs the gauntlet before it touches a machine — so shipping is a routine, not a held breath.
Regression and test infrastructure behind the 16 production systems we shipped in the past 12 months.
Work touching this layer
A dedicated Layer-01 case study is in the pipeline; these deployments each stand on data infrastructure we built underneath them.
The people whose models are only as good as the pipeline.
— Head of ML / Autonomy — needs data they can trust and evals they can cite.
— Research lab lead — wants researcher time on research, not plumbing.
— VP Engineering — answers for whether the next model ship is safe.
Point a Pathfinding Sprint at this layer.
Two weeks. We audit your data flow — capture to training to eval — and hand you a scoped plan, whether or not you build it with us.