Train agents in systems that push back.
Agents can hold a conversation. The open question is whether they can operate a system. We build reproducible, stateful simulations of real software — where actions have real consequences on state, feedback is step by step rather than pass/fail, and every episode resets deterministically so training scales.
Stateful simulation
Actions mutate persistent state, not a transcript. Every step lands on a real system that remembers what came before.
Dense reward signal
Step-level feedback instead of a single pass/fail at the end — the gradient an agent needs to actually learn the task.
Deterministic reset
Each episode restarts from a pinned image, so runs are reproducible and training scales without drift.
Built around the workflows you care about.
Each environment is built to spec — but every one is held to the same bar for state fidelity, reward density, and reproducibility.
Enterprise messaging
Channels, threads, and DMs with the read/write semantics real teams rely on.
E-commerce & customer service
Live catalog, orders, and inventory — resolve a case without breaking the ledger.
Recruiting & HR
Postings, candidate stages, and scheduling, operated inside strict privacy bounds.
CRM / ERP back-office
Records, pipelines, and approvals across the systems a business actually runs on.
Browser & terminal
Linux/Ubuntu shells and full browsers for tool use and multi-step operations.
Custom enterprise domains
Your workflow, built to the same standard — from schema to reward to checker.
From first call to training loop.
Define goals
We agree on target behaviour, domain, complexity, and the success metrics that matter to you.
Build the environment
A full-stack sim — API, database, seeded data, and a GUI or tool interface, MCP-compatible throughout.
Author tasks & rubrics
Task suites across graded difficulty tiers, each rubric rewarding judgement on ambiguous steps and exact execution on deterministic ones.
Integrate & scale
Ships as infrastructure-ready containers that plug straight into your training loop, with support as the agent improves.
Judgment and precision — strong agents need both.
Workflow evaluation
Multi-step tasks that demand prioritisation and decision-making. Success is measured by sound judgement, not merely by completing the steps in order.
System execution
Verifiable software interaction, checked directly against system state — the outcome either holds up under inspection or it does not.
Evaluate only one side and you get an incomplete picture of what an agent can be trusted to do.
What ships
- Containerized environments
- Task & evaluation suites
- Reward / rubric spec
- Verification checkers
- Difficulty tiers
- Trajectory logs
Ready to build your training environment?
Tell us the workflow you want your agents to master, and we’ll build the system that trains them on it.