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We build private evaluation suites that find what public benchmarks miss.
You have internal evals. They're good. But they're built by people who know how the model works, which means they test for the failure modes the team already imagines. We provide the external perspective—fresh assumptions, different angles, signal that doesn't come from inside the building.
- Private capability evals targeting low-frequency behaviors your internal suites don't cover
- Outcome-based grading that doesn't penalize models for finding better solutions than the test designer anticipated
- Continuously rotating suites that never appear in training data and can't be optimized against
- Eval design for emerging capability classes: multi-agent coordination, long-horizon tasks, domain-specific reliability
The behaviors you're not testing for are the ones that reach production.
Request a pilot →You're betting on a model for a production use case. Maybe you're choosing between Claude, GPT, and Gemini for a customer-facing application. Maybe you've already deployed and need to justify the investment, or know when to switch. Public benchmarks won't answer your question because they don't test your domain, your edge cases, or your definition of failure.
- Model selection evals built around your specific use case, data, and failure tolerance
- Domain-specific reliability profiling—how the model fails, how often, and in what contexts
- Ongoing monitoring suites that catch regression when providers update their models
- Hard numbers for the board deck: accuracy, hallucination rates, latency, cost-per-task, measured against your actual workload
Due diligence before you commit. Continuous assurance after you deploy.
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