How often every agent gets your work right, measured by your rules, for your team, your board and your auditors.
A separate grader scores every answer against a key the agent never sees.
Watcher sits outside your agents and checks their work the way your best reviewer would, on every task.
Evals built from your past cases and your rules, so the score means something in your field.
We are testing known mistakes seeded into the work, so you know the grader catches what it should.
A separate grader scores every answer against a key the agent never sees.
Measurement developed with the San Diego Supercomputer Center (SDSC), open to inspect.
Broken rules, data leaks and unsafe actions are flagged as they happen.
If accuracy drops after a model or data change, you hear about it the same day.
AIUC-1 is a strong standard for AI agents, and Watcher maps to all six of its areas. But an agent can pass every standard check and still get your work badly wrong. Catching that takes domain and workflow policies, tested on every task.
The same evidence answers your team, your board and your next model.
Let agents run on their own job by job, as the numbers earn it, and pull it back if they slip.
A clear record of accuracy, rules kept and time saved, mapped to AIUC-1.
Every graded task becomes training data in your Context, for a model that does your work your way.
Thirty minutes with our team, on the workflow you would run first.