Most companies approach agent training the wrong way.

We are building toward a world where coding agents autonomously solve the hardest engineering problems. The biggest bottleneck is training data — specifically the high-fidelity environments and expert-led pipelines that give models the feedback they need to get there.

Verism builds RL environments for agentic model training and runs full post-training data pipelines end to end for frontier labs.

Agents that need to practice real workflows require environments where every tool they touch — Gmail, GitHub, Slack, internal systems — exists and behaves as expected, without real-world consequences. Building that well is a research and engineering problem. We handle environment architecture, tool simulation, task design, verifier logic, and reward structure. If your agent needs to practice a workflow thousands of times, we build the world it trains in.

On the data side, we produce SFT demonstrations, RLHF preference pairs, and agentic task traces for coding and reasoning workflows, delivered in whatever format your training infrastructure expects.

The quality of the training signal is determined by the quality of judgment in the people producing it. Our network is sourced from open source contributors, hyperscaler engineers, and technical founders — identified by what they have shipped, not by how they perform in a screening funnel. On coding and agentic tasks, that difference compounds across a dataset.

If you are at a frontier lab working on post-training data quality, reach out.