Information retrieval for life sciences AI
Retrieve information intelligently, rank results accurately, and provide scientifically sound answers
Request a demoHow Scientific RAG works
While standard information retrieval and generation pipelines use only similarity search to return a limited and nondeterministically ranked list of source documents, Scientific RAG combines this and a graph-based search and ranking into a sophisticated, hybrid process. The result: clear, accurate, cited answers that draw on the totality of all biomedical facts, concepts and relationships in our best-in-class knowledge graph.
A complete, up-to-date picture of the biomedical landscape
Causaly’s next-generation Scientific RAG works in tandem with Causaly’s expansive, high-precision biomedical knowledge graph. By combining standardized vocabularies and domain-specific ontologies with custom, controlled ontologies and dictionaries,Causaly’s AI understands millions of biomedical topics absent from other graphs. These custom ontologies — on topics like drug targets and disease pathology — are built, curated, and maintained by human PhD research science subject matter experts.
500 million facts and 70 million directional relationships
8 relationship types including multiple kinds of directionality and refuting evidence
Ontologies linking 150+ categories across 5 million biomedical concepts
Automatic updates via Causaly’s enterprise data fabric
Flexible, scalable ingest enabling customers to add internal and licensed data to the graph
An AI copilot purpose-built for scientists
Where most generative AI copilots are a black box producing unverifiable and ever-changing responses, Causaly Copilot provides accurate, transparently sourced answers to the uncommon questions that drive innovation.
These unique capabilities come from two core systems — Causaly’s Scientific RAG and Causaly’s GenAI Operating System. Working together, these systems enable the copilot to deliver fast, accurate answers safeguarded from hallucinations and other AI pitfalls.
Applications and agents to accelerate R&D
Causaly’s product suite helps scientists find, analyze, leverage, and share biomedical knowledge.
Discover
A research portal that provides actionable, categorized, textual, and visual answers suited to the query and connects to relevant custom applications as needed
Bio Graph
A visual knowledge exploration tool allowing R&D teams to investigate tens of millions of multidirectional relationships across life sciences
Target Assessment Agent
Deprioritize unviable targets and accelerate target selection with comprehensive, automated Target Assessment reports that only take minutes to generate
AI Report Agent
An AI agent that generates an initial draft report that otherwise takes days or weeks, on topics from disease pathology to regulatory and market queries
A powerful combination of external and internal data
Enterprises deploy Causaly at scale to de-silo and protect institutional knowledge, and to increase the value of their internal and third-party data by contextualizing it against all the public information in Causaly’s knowledge graph.
Scalable ingest pipelines constantly pull and update data from millions of scientific data sources, including:
- Over 35 million scientific preprint, full-text, and abstract documents from PubMed
- Genome-wide association studies
- Patent filings
- Clinicaltrials.gov data
- Proprietary customer information, secured in a virtual private cloud
- Third-party applications and licensed datasets
Strategic partners in AI change management at every stage
Causaly’s digital transformation and change management teams support customers with a range of professional services and deep domain expertise, from AI strategy to enterprise-level PhD research science. These experts work alongside customers throughout their journey including strategy and business alignment, deployment, program management, and day-to-day research support.
Get to know Causaly
What would you ask the team behind life sciences’ most advanced AI? Request a demo and get to know Causaly.
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