Executive Summary
What if scientific hypotheses could be generated not just faster—but with traceable logic, real-time citations, and domain-specific reasoning? A global biopharma company exploring AI’s role in early-stage R&D teamed up with Factspan to test this idea. The focus: apply Agentic AI to automate hypothesis generation for use cases like biomarker discovery and diagnostics.
Factspan designed a multi-agent prototype that orchestrated LLMs, integrated biomedical knowledge graphs, and produced audit-ready scientific outputs. Built for explainability and compliance, the early-stage platform showed how GenAI could complement human researchers in complex, high-stakes environments.
Business Impact:
- ~60% reduction in literature review and drafting time
- 3x faster creation of structured assay reports
- Complete traceability of outputs via embedded logging
- Significantly reduced review effort through integrated human validation