Motivation for New Benchmarks
Generic academic tests are useful—but they are not enough to decide whether an AI system can safely perform real work.
Public benchmarks can leak into training data, quickly turning measurement into memorization. Provider-reported results also tend to omit the operational details that shape performance: prompts, tools, retries, model parameters, latency, and cost.
Pramaana creates versioned, domain-specific eval sets with independent review. Private test items stay private. Published releases preserve their full lineage, and corrections create a new revision instead of rewriting history.
Our Plans
We are expanding into the workflows where model selection has material consequences: software delivery, finance, legal research, healthcare operations, education, and government services.
Our public platform makes the results legible. Our private Studio gives evaluators the tools to version datasets, enter or import results, review evidence, and publish immutable snapshots.
Work with us
Tell us what you need to measure. We’ll help shape a rigorous evaluation plan.