Rigorous, task-grounded environments that measure what your agents can actually do in production — and close the gap when they can't.
Methodology
Real workflows. Simulated at scale.
Scored against real outcomes.
Your live production traffic replays silently against challenger models and providers — without touching the live path. Real tasks, real conditions, real failure surface. No synthetic inputs invented in a lab.
We build a high-fidelity simulation of the environment your agent operates in — reconstructing the exact surfaces, task structures, and conditions of your domain. The agent runs against it. We measure what happens.
Environments are calibrated against ground truth — either your own historical data or Mersault's domain data partners. The reward signal knows what correct looks like in your specific workflow, not just in general.
What we're building toward
Enterprise agents don't fail on single questions. They fail across sequences — wrong tool, wrong order, unrecoverable state, a decision that looked right until step seven. Mersault builds environments that expose exactly that failure surface.
Each benchmark is domain-grounded, long-horizon, and scored against verifiable ground truth. AI-native companies use them to know where their agents break before their customers do.
We work with enterprise teams and AI-native companies deploying agents in production. If reliability is on the line, let's talk.
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