LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence

We present a competitor-discovery agent for drug asset due diligence that, given an indication, identifies competing drugs and extracts their attributes — a task complicated by fragmented, paywalled, alias-heavy, and fast-changing data. Since no public benchmark exists, we built one by converting five years of biotech VC diligence memos into a structured evaluation corpus, and added an LLM-judge agent to filter false positives. Our system, Bioptic Agent, achieves 83% recall, beating OpenAI Deep Research (65%) and Perplexity Labs (60%). Deployed in production, it cut analyst turnaround time for competitive analysis from 2.5 days to ~3 hours (~20x).