The Operating System of Experimental Science

• —Most research infrastructure is invisible outside its home institution. Equipment, expertise and capacity sit underutilized.
• —Industry teams with real R&D problems can't find the right academic partner without weeks of manual searching and warm introductions.
• —Researchers publish papers but have no structured way to signal what they can actually do next.

• —Maps research capabilities: publications, equipment, techniques and the people behind them.
• —Routes problems to capabilities using AI-driven matching across researchers, labs and facilities.
• —Lets users interact with research through natural language — ask questions, explore publications and generate collaboration ideas.
• —Makes scientific infrastructure discoverable and accessible beyond personal networks.

• —Open metadata from OpenAlex, ORCID and institutional databases finally makes it possible to map the research landscape programmatically.
• —Large language models can now read, summarize and reason about scientific text at scale.
• —Universities and research facilities are under increasing pressure to demonstrate utilization and societal impact.
• —Industry R&D cycles are accelerating. The cost of not finding the right partner is measured in months and missed markets.

A world where any research problem can find the right capability — and any capability can find the problems worth solving. Abstract removes the friction between knowing and doing in science.