Causaly is an AI research platform for life sciences that extracts causal evidence from millions of papers to accelerate drug discovery and biomedical insight.
Causaly reads the biomedical literature at machine scale, extracting not just mentions but stated relationships between genes, diseases, pathways, and compounds. Researchers query in natural language and receive synthesized evidence with citations back to the underlying sentences, dramatically compressing the weeks typically spent assembling background for a target or indication. The platform supports target identification, safety assessment, and clinical trial planning, and its knowledge graph keeps pace with new publications continuously. Enterprise deployments integrate with internal data so proprietary experiment results sit alongside public evidence. Unlike general search tools, Causaly’s models are tuned for biomedical causality, distinguishing correlation from asserted mechanism in ways that matter for scientific conclusions. Collaboration features let teams share evidence collections and annotate findings, which turns individual discovery work into institutional knowledge that persists as people move between projects. Teams use it to benchmark evidence quality across therapeutic areas with consistent methodology and traceable sources.
Causaly is sold to organizations through enterprise agreements based on seats and modules. Deployments include onboarding, data integration support, and training for scientific and medical affairs teams.
Pharma and biotech organizations whose scientists need to navigate the literature faster, and medical affairs teams building evidence dossiers with defensible, citable support. It fits settings where conclusions must withstand regulatory scrutiny and every claim needs a traceable line back to its source. Analysts preparing portfolio decisions use it to compare scientific evidence across indications with consistent methodology.