Verification for enterprise
Hold AI to the judgment that makes your business yours.
Evaluate
Know whether an agent did the work, not whether it passed a test.
Grade the records, not the summary
We check the state an agent changed against rules that must hold at every step.
Validate before launch
Run your agent against a replay of your own workflow and see where it breaks first.
Re-test on every change
Re-run the same checks as models, prompts and tools change.
Simulate
Turn your real outcomes into a reward your team or model provider can train on.
Replay the same world
Change only the agent’s action, hold everything else fixed, settle by your real rules.
Seconds, not months
A close or a trade that takes weeks to prove out settles inside the episode.
Counterfactuals by construction
One decision becomes as many branches as the budget allows.
Reward
Make the judgment that sets your business apart the rule every model is graded against.
Write the rule once
Your experts say what must be true. The mechanism grades every run after that.
Attack it before anyone trains on it
We try to beat every verifier first, and record how often it was beaten.
Measure the gain
Held-out tasks and human baselines, so improvement is measured, not asserted.
Why SimReal
Mechanistic, not learned
The reward is the mechanism itself. There is no judge model to game.
A human denominator
Where one exists, we score against what people actually achieved.
Failures on the record
We attack every grader before release and keep a record of what gets through. On the Puzzle Benchmark, 400 cheating attempts were run and none did.
Independent of model companies
No AI model company owns any part of SimReal. A measurement is worth what its independence is worth.
Bring us the problem you’re trying to solve
Your agent passes every check and still gets the work wrong.
Your outcomes arrive months after the decision.
You can’t tell a good decision from a lucky one.