Every output of your AI gets a score against fixed criteria before it moves on. You set what counts as good. For every output it is on record how it scored. That tells you whether your AI meets your standard.
The Agentic Part
An agent is an AI that carries out work steps on its own. Here two kinds of checkers work together. Code checks what fixed rules can establish. A judge assesses the remaining criteria: an AI model from a different model family than the model that produced the output. A model family is the line of models of one provider. Both checks run for every output before it moves on, and nobody has to read each output.
Main Features
- Fixed criteria against which every output is scored
- A score before every output moves on
- Checks by code for everything that fixed rules can establish
- Assessment of the remaining criteria by a judge from a different model family
- A score result for each output
Use Cases
- Your AI writes replies for customers. Every reply is scored against your criteria before it moves on.
- Your team uses AI for texts. A fixed check decides whether a text meets your criteria.
What we build for you
- Your criteria for what a good output is
- The outputs of your AI that should be checked
- The split of which criteria code covers and which the judge covers
- The point where the score steps in before the output moves on
