For AI labs & model vendors
Evaluation that holds up to scrutiny
Problems
Problems we focus on
Leaderboard numbers don't answer your question
Public benchmarks are measured under different settings, are often saturated, and some have documented flaws.
Claims need provenance
Buyers need to know how a number was produced: settings, versions and logs should be published alongside results.
Failure modes matter more than averages
Aggregate scores hide the failures that matter in deployment.
Services
Relevant services
LLM & model evaluation
PlannedTask-specific evaluation suites and benchmark analysis that answer a concrete decision — which model to ship, whether a release regressed, where a system fails.
Structured adversarial testing of LLM applications and agents — prompt injection, unsafe tool use, data leakage and failure under stress.
Literature reviews, model and vendor assessments, experiment design and technical due diligence for AI initiatives.
Evidence
Evidence you can check
Solutions