Environment Generation
Conversion of proprietary data and evaluations into new training environments.
Converts proprietary data into RL environments
Vmax builds RL/eval infrastructure that converts proprietary company data into training environments, including Unix/terminal task environments such as unix-ctf.
unix-ctf environment
This legacy directory profile is awaiting claim-level source migration. Existing values are retained, not upgraded to verified facts.
| Website | vmax.ai |
|---|---|
| Domains | Machine Learning, Custom Environments |
| Location | San Francisco, New York |
| Team size | 1-10 |
| Founders | Matthew Sargent, Augustine Mavor-Parker |
Focus areas and technical capabilities are shown separately. Missing technical evidence remains Unknown.
Unknown. No source-backed catalog capability record is available yet.
Products and services supported by official company materials. Reviewed 2026-08-16.
Conversion of proprietary data and evaluations into new training environments.
Verified public artifacts and documented private commercial inventory.
Structured sources have not yet been migrated for this profile.
Company representatives and researchers can propose sourced changes. Submissions do not directly overwrite editorial data.
Also listed under Machine Learning, Custom Environments.
| Name | Domains | Type / catalog | Location | Team | Funding | Raising |
|---|---|---|---|---|---|---|
| Rise Data Labs RL environments and tasks across multiple domains, supported by a large expert network | Computer Use, Coding, Finance, Cybersecurity, Legal, Data Labeling, Enterprise, Multi-Domain, RLHF, Custom Environments | Data + environments 3 artifacts | New York, United States | 11-25 | - | Unknown |
| Applied Compute Ex-OpenAI trio applying RL to build specialist enterprise models | Enterprise, Machine Learning, Custom Environments | Pure-play commercial Catalog unknown | San Francisco | 11-25 | $80M total at $700M valuation (Oct 2025) | Yes |
| Artificial Analysis Independent benchmarking of AI models across intelligence, speed, and price | Multi-Domain, Machine Learning | Pure-play commercial Catalog unknown | San Francisco | 11-25 | $2.6M (2024) | Unknown |
| Bespoke Labs Data curation and RL environment recipes from ex-Google DeepMind researchers | Coding, Machine Learning | Pure-play commercial Catalog unknown | Mountain View, Menlo Park, Bangalore, San Francisco | 11-25 | ~$40M (reported) | Unknown |
| Collinear Enterprise simulation, judges, and long-horizon trajectory generation | Enterprise, Long Horizon, Machine Learning, Simulation | Pure-play commercial Catalog unknown | Mountain View, Sunnyvale | 11-25 | - | Unknown |
| Diffuse Labs ML and long-horizon RL environments | Machine Learning, Long Horizon | Pure-play commercial Catalog unknown | Palo Alto, San Francisco | 1-10 | - | Unknown |