Build and Scale Physical AI Systems
Train models, process data, and orchestrate autonomy workflows across any environment.
Applied Intuition’s Tools for Vehicle Intelligence is built for petabyte-scale ingestion, curation, and processing across fleets and long-running programs. Reliable orchestration, cost-aware execution, and reproducible lineage keep workflows stable as data volume and model complexity grow.
A powerful SDK lets teams construct and manage complex workflows across cloud, on-prem, and air-gapped environments. Modular primitives allow teams to bring their own models, simulators, and metrics while integrating existing infrastructure—preserving interoperability and lineage end to end.
A Data Flywheel That Compounds Performance
Turn real-world sensor data into training, simulation, and evaluation at scale.
The platform converts real-world sensor data into curated, labeled segments that power downstream training, simulation, and evaluation. Each stage of the pipeline generates metrics and insights that feed directly into the next iteration.
Over time, this closed loop continuously improves autonomy performance. As more data flows through the system, models become more robust, edge cases are identified earlier, and safety progress compounds across the entire autonomy stack.
Agent-Driven Workflows for Physical AI Development
Transform advanced AI research into reliable autonomy workflows.
The platform is built on state-of-the-art AI, including vision-language models for data understanding and curation, World Foundation Models, and neural reconstruction and rendering for high-fidelity simulation. ML behavior agents generate realistic outcomes and controllable scenario variation for training and validation workflows.
A dedicated research-to-production pipeline continuously turns cutting-edge methods into reliable, scalable product capabilities. The system is designed to be orchestrated by agents, with tokenized UI designs and MCP-ready interfaces. These agents understand the context, data types, and workflows required for physical AI development and can drive complex tasks end-to-end.
