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THE FOUR-STATION CONVEYORENVIRONMENT & DATA
  1. 01Seed data

    Tasks, examples & goals

  2. 02Environment design

    Reproducible task suites

  3. 03RL training loop

    Train, evaluate & iterate

  4. 04Agent performance

    Results become new seed data

Continuous Evals to
Train Agents for Your Workflows

Build reproducible environments from your seed data, train and evaluate agents on real workflows, and turn their results into better data for the next iteration.

Why Environments Matter for Agents

Agentic enterprise automation is shifting from pure text training to real interactions with business systems. To improve reliability, you need reproducible environments to train and validate agent behavior, plus an end-to-end evaluation loop for continuous measurement and iteration. These environments also enable scenario-specific model training—so you can deploy agents (and models) optimized for your enterprise workflows.

What We Deliver

Enterprise-grade environments, evaluations, training loops built for real systems.

Real-world Benchmarks Built from Enterprise Scenarios
A Reproducible RL environment for Ongoing Training
Training Loop for improving Model Performance in Real-World Environments
Better-performing, more Adaptive Eigent Performance in Your Scenario

Use Cases

Checkout our use cases for our clients.

CRM / ERPBrowser Automation

RL Environment for Browser Automation (CRM / ERP / E-commerce)

We built a suite of reproducible browser-based RL environments for ERP/CRM and e-commerce back-office workflows, paired with 2,000+ real-world task samples for training and evaluating automated task execution models across multi-page enterprise systems.

  • Environments: ERP / CRM / e-commerce management platforms
  • Tasks: single-step search, multi-step chained search, cross-table lookup, data analysis, edit and creation operations (leads/orders/invoices)
RL Environment for Browser Automation
Terminal Environment for Terminal Agents
Linux / UbuntuTerminal Agents

Terminal Environment for Terminal Use

We built a dataset designed to improve terminal agent capabilities, specifically for training and evaluating models on task execution in real Linux/Ubuntu environments. Each entry corresponds to a reproducible containerized environment and a complete terminal task package.

  • Environments: Linux/Ubuntu terminal environments
  • Tasks: tool usage, multi-step operations, and error handling

Build environments that make your agents reliable

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Open Source Cowork Desktop

100% open source - host it yourself, free, with your own API keys or local models.

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