Third-party evaluation and certification for generative AI

LLMs are unpredictable, but internal observability and guardrails aren't enough. Failing to provide robust assurances that your generative AI product excels in realistic scenarios is alienating your customers. We can fix that.

How does it work?

Human domain experts realistically use and evaluate your product

We assemble a large pipeline of humans with domain expertise who can provide realistic usage and informed evaluation of your product across contextually relevant indicators. Building for law and health? Let's see what lawyers and physicians think!

Agents perform a large sample of simulations to pressure test your product

Our middleware will generate and execute a large sample of test cases to capture how your product performs (and excels) across a breadth of realistic tasks and scenarios. Building a FP&A copilot that can't deal with messy accounting data? Not good!

You publicize the aggregate results to win adopters, build trust

You publicize the aggregate results to win adopters

We compile the results into a public report and the skeptical enterprises who don't trust LLMs, much less trust you, are given the evidence they need to benchmark and buy your product. People flock to the most reliable product. Why should they believe it's yours?