AI models generate confident, fluent answers.
We build the benchmarks and the audit trail that test whether the reasoning holds up: under expert scrutiny, under adversarial conditions, under regulatory review.
Models produce fluent, confident, and subtly wrong scientific claims. No trusted standard exists to catch them.
Models generate scientific reasoning that reads well but overstates evidence, flattens disagreement, or relies on outdated consensus.
Existing benchmarks test recall and pattern-matching. None evaluate whether a model reasons like a careful scientist under real-world complexity.
AI labs cannot credibly validate their own models for external trust — just as drug developers cannot run their own clinical trials.
The EU AI Act requires traceability and auditability for high-risk AI systems. The compliance deadline is August 2026.
And the infrastructure to handle them doesn't exist yet.
Pharma, biotech, and clinical workflows now run on frontier models. The question is no longer whether they're capable, but whether their reasoning can be trusted in decisions that matter.
High-risk AI rules under the EU AI Act take effect August 2026. Documented, traceable evaluation becomes a compliance requirement, not a research preference.
Labs publishing their own benchmark results is no longer sufficient. Every comparable category, e.g. drug trials, credit ratings, financial audit, product safety, eventually required an independent third party.
These are real failure patterns. The outputs read well. They're still wrong.
A vertically integrated system for evaluating, benchmarking, stress-testing, and auditing AI in science.
Scientific problems designed by domain experts to test what generic benchmarks miss.
Versioned, domain-specific evaluation suites. Quantitative. Comparable across models and over time. Built to cite, not to publish once.
Systematic stress-testing of scientific reasoning under conditions designed to surface failures that matter.
Full provenance, reproducibility, and traceable methodology. Built for high-stakes and regulated environments.
This is not annotation. It's structured expert judgment under uncertainty — versioned, scored, and auditable.
Existing benchmarks reward models for producing the right answer. We evaluate how they reason: whether they handle complexity, uncertainty, and contested evidence the way a careful scientist would.
Our methodology separates reasoning quality from robustness under adversarial conditions, scored independently by credentialed domain experts using proprietary evaluation frameworks.
The benchmark is versioned. Science evolves; the evaluation evolves with it.
Independent evaluation and structured failure analysis, designed to surface the reasoning errors that matter — not the ones that look good on a leaderboard.
Compare and evaluate AI tools for scientific workflows with audit-grade documentation.
Traceable, reproducible methodology designed for high-risk AI system requirements.
We are not owned by or beholden to any AI lab. External validation requires structural independence — the same logic that governs drug trials, financial audit, and product safety certification.
Credentialed domain scientists, not annotators or user votes. Our evaluators detect the failures that matter in science.
We evaluate how models handle complexity, uncertainty, and contested evidence — not whether they can pick the right multiple-choice answer.
The benchmark isn't a snapshot. It's a longitudinal record. The value grows as models evolve and the field moves.
NeuroBench v1 is our first benchmark. We're working with a small number of partners — AI labs, biotech teams, scientific institutions — to define what rigorous evaluation looks like in practice.
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