01
Env Compiler
Ingest a workflow, app, robot task, simulator, or codebase and package it as a versioned environment with typed state and reset hooks.
Sim + real EnvOps for AI agents
Trainable worlds for agents that need to work in reality.
EnvLoop.ai turns real work and physical tasks into maintained RL environments, combining simulated scale with real-machine truth.
30-minute setup targetGym / MCP / VM / Isaac-readySim -> real calibration
STATE
TOOLS
RESET
REWARD
Not a dataset marketplaceA living environment layer for setup, reset, rollout, verification, and replay.
Atomic unitTask spec + world state + action surface + verifier + traces.
BuyersFoundation model labs / Embodied AI teams / Enterprise deployers
Product surface
Three products, one feedback loop.
02
Rollout Runtime
Run parallel rollouts across browsers, VMs, MCP tools, APIs, simulators, and connected real machines with replay and observability.
03
Verifier Studio
Build rewards, golden states, physical outcome checks, adversarial QA, and correction workflows that resist reward hacking.
Two-legged data engine
Simulation gives scale. Real machines give truth.
Simulated worlds
- Parallel rollout variants for curriculum learning.
- Domain randomization across assets, tools, failures, and edge cases.
- Cheap coverage before expensive real-world exploration.
Calibration loop
sim -> real
real -> sim
sim -> real
real -> sim
Real-machine evidence
- Teleop demos, robot rollouts, recoveries, and failure traces.
- Sensor calibration, latency, contact dynamics, and messy scenes.
- Verifier validation against physical outcomes.
Founder-market fit
Algorithm judgment meets environment infrastructure.
The founding insight: useful agent environments require both product truth and operational discipline.
Algorithm + Product
Environment Infrastructure