Scientific knowledge is scattered, siloed, and slow to search. Amass integrates across this fragmented landscape — patents, papers, regulatory filings, proprietary documents and data — and applies AI to create a structured, cross-linked, and queryable knowledge graph.
We build our own domain-specific embedding models to capture subtle scientific signals. We go beyond simple retrieval — enabling users to triangulate across multiple sources and trace the evidence behind any output. We’re not just summarizing documents — we’re helping teams reason with them.
The result: faster insight cycles, smarter pipelines, and more effective research-to-decision handoffs
Everything we design for GEMA starts with that premise and is governed by a set of non-negotiable principles.
GEMA keeps scientists at the helm: you set hypotheses and constraints, we automate the grind. Every suggestion is explainable, cite-backed, and editable—so the human always makes the final call.
From model provenance to pricing, we share our assumptions, metrics, and roadmap in the open. Feedback loops are baked into the product and our company culture, ensuring we improve out loud with the community.
Developed in the most concentrated life science cluster in the world
Co-Founder & CEO
Venture advisor in Antler, CMO in Valuer, CCO in VC fund. Founder in Product / Venture-Studio -> Exit Patent / Product to Coloplast
Previous mngmt. consultant, clients among otters Novartis, Novo Nordisk, Alcon, Lundbeck
Co-Founder and CTO
Former Head of AI/ML Corti, Sr. Data Scientist Novo Nordisk, Research at Novo Foundation, BioInformatics Research NCBI. Lecture Assistant - KU
Alex's research on building knowledge discovery tools for researchers received more than 13 thousand citations
