We build simulation agent systems that let hardware teams start algorithm research, data production, model evaluation and customer demos before the first prototype exists, then keep improving as real-world data flows back in.
Synthetic data is not a substitute for real-world data. It establishes the scenarios, datasets, evaluation and tuning pipelines early. Once real data arrives it enters the same loop, calibrating the simulation and driving the next round of targeted generation.
SIMULATION FIRST
Algorithm teams begin training and validation in parallel, long before prototypes and large-scale field capture are finished.
REAL-WORLD SYNC
First-hand data from real deployments calibrates the distribution, reproduces failures and raises the value of the next generation round.
Simulation and real-world operation share the data schema, scenario definitions, version tracking and evaluation standard, which removes duplicated build-out and the cost of switching between phases.
Anchored on AI-native simulation, we organise the physical environment, fused data production, control-loop evaluation and pre-deployment decision rehearsal into a deployable, reusable development system.
Controllable, randomisable, reproducible
Platform, actuators, dynamics, environment and sensors are modelled together, so noise, occlusion, latency, faults and changing conditions become configurable engineering parameters with stable replay.
Data production driven by algorithm gaps
Scenario generation, simulation runs, automatic annotation, cleaning, quality control, version management and distribution, turning high-value conditions and failure samples into a data asset that keeps compounding.
Every iteration reproducible, comparable, scored
Any algorithm module can be the unit under test, with simulated stand-ins, replayed data or real software completing the loop around it, and continuous regression driven by a unified clock, fault injection and ground-truth metrics.
Rehearse agent-cluster operations before deployment
Our next-generation AI digital twin for drones, autonomous driving and embodied intelligence. It reproduces the environment, the platform, agent roles, policies, coordination and task constraints so agent clusters can run, be tested and be evaluated in a closed loop, with failed rollouts and counterfactual trajectories recovered as post-training data.
World generation, simulated data capture and real-world scene reconstruction. Together these engineering results form R&D infrastructure that keeps evolving.
We do not hand over a one-off simulation tool. We build autonomous R&D infrastructure with you, and it is reproducible, continuously regressible, and designed to keep evolving alongside your product.
Physics, scene generation, data capture and task scheduling are all exposed as capabilities an agent can understand, call and compose, which is what puts simulation inside your development loop rather than beside it.
Decision, planning, control, the controlled platform, sensors and the environment are modelled as interchangeable roles in one loop, so any module update can be regressed, compared and scored automatically.
We work inside your real development chain and turn non-standard scenarios, device models, data pipelines and evaluation processes into reusable engineering modules, instead of stopping the moment a project ships.
Deploy on your corporate network, on-premise GPU cluster, private cloud or hybrid cloud. Your data and algorithms stay protected while elastic compute stays available, and the engineering experience accumulates inside your organisation.
From a new product programme, to post-launch OTA releases, to long-tail failures in the field. Tell us which stage you are in and we will scope the simulation, data and evaluation work around it.
Helpful details: platform type, sensor configuration, current algorithm stage and the bottleneck you most want removed
