RL Environments
The model is not the moat.
The data is.
We build simulation engines that generate infinite training data and compound your advantage with every rollout.
Capabilities
What we build
01
Custom Simulation Engines
We build the physics of your domain.
We formulate your domain as a Markov Decision Process with custom state-action spaces, transition functions, and physics constraints. Not a wrapper around a generic gym. A ground-up engine tuned to your exact operational physics.
02
Synthetic Data Generation
Infinite rollouts. Infinite data.
Every simulation step produces labeled training data. State transitions, reward signals, agent trajectories. Millions of samples per hour. Domain randomization ensures diversity no real-world dataset can match.
First Principles
The model architecture is a commodity. The data is the asset.
Every lab has access to the same architectures and scaling laws. The organizations that win generate proprietary training data at scale. Not by scraping the internet. By encoding your domain into a simulator.
03
Sim-to-Real Transfer
Train in simulation. Deploy in reality.
Domain randomization across hundreds of parameters. Curriculum learning that scales task complexity. Digital twin pipelines that close the sim-real gap. Policies perform in the real world on day one.
04
Reward Engineering
Translate business KPIs into reward signals.
Latency versus cost. Safety versus throughput. We build composite reward functions that encode your actual business objectives. Reward shaping that avoids pathological optimization. Inverse RL from expert demonstrations when the objective cannot be explicitly specified.
The Flywheel
Simulation compounds. Static datasets depreciate.
Better simulation generates better data. Better data trains better models. Better models reveal where the simulation needs to improve. Each cycle compounds. The environment is the product.
The environment is the competitive advantage.
Tell us about your domain. We will tell you what the simulation engine looks like.
Talk to us