Formalizing Research Judgment with AI

We build AI systems that learn how scientists evaluate research — turning years of tacit expertise into explicit, evolving judgment frameworks.

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What We Build

AI-driven systems that capture, formalize, and operationalize the evaluative frameworks scientists use to identify promising research directions.

Research Judgment Systems

Structured evaluation frameworks that capture a scientist's criteria for promising research directions, making tacit expertise explicit and reproducible.

AI-First Research Infrastructure

Complete research loop from literature discovery and hypothesis generation to experimental planning — with taste models serving as directional filters at each stage.

Dynamic Calibration

Judgment standards that evolve through iterative human-AI collaboration — distinct from static reward models or community-level bibliometric signals.

How It Works

Our approach centers on a specific challenge: scientific taste is typically tacit, accumulated through years of domain immersion, and resistant to direct articulation.

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Extract

We work closely with domain scientists to surface and formalize their evaluative criteria through structured collaboration.

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Calibrate

Each round of feedback refines the system, producing increasingly accurate research direction assessments.

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Evolve

The evaluation framework itself improves with use, adapting as research priorities sharpen through discovery.

Track Record

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Who We Are

Research Collaborators

University Partners

Ongoing research collaborations with leading university labs in applying AI methodology to experimental sciences.

Industry Advisors

Technology & Strategy

Advisors from top technology companies providing guidance on product strategy, engineering infrastructure, and scaling AI systems.

Contact Us