A place for agents to learn.
Configurable simulation environments for training and evaluating AI agents: scenarios, sensors and physics you control, with a clean interface to your training stack and full runs on local hardware.
The pieces we deliver.
Environments
Scenario definitions, procedural variation, and physics from a custom C++ engine or Unreal, exposed through a simple step / observe / reward loop.
Sensors and observations
Cameras, depth, ray casts and state vectors, recorded in sync so datasets are reproducible.
Local inference
Multi-GPU orchestration for local models with llama.cpp and Ollama, benchmarked and logged, no data leaving the building.
Evaluation
Replay, telemetry and metric logging so you can see what the agent actually did.
Plain process, working builds.
A short call and a written plan: what gets built, in what order, and what a first playable or runnable milestone looks like.
Weekly builds and written progress reports. Source in your repository from day one.
Documentation, profiling notes and a team that can keep going without us, or with us on retainer.
Where this comes from.
Local LLM orchestration toolkit
Shell and Python tooling for running, benchmarking and managing large local models across mixed GPUs.
Custom C++ simulation engine
PBR rendering, dual physics backends, ECS and an integrated editor, built as the base for training environments.
Have a project in this direction?
Send a short note. We reply within a couple of days with questions or a first take on scope.