Sourabh

Mosaic is an MCP server for preclinical target diligence. Our user — a comp-bio at a 10–30 person biotech, or a solo diligence consultant — gets asked "which of these five targets deserves the next six months of budget?" and answers it by hand: PubMed, ChEMBL, Lens, DepMap, ClinicalTrials.gov, three days to two weeks per target. The real problem isn't scattered data; it's that they deliver the verdict without being able to see their own blind spots. Open Targets shows data but takes no position; Causaly is enterprise-priced; a raw LLM invents confident answers with no provenance. Mosaic runs inside their existing Claude session, composes across sources via MCP without touching proprietary data, and returns a coverage-honest verdict — one that states confidence per dimension and explicitly flags where its coverage is too thin to judge. For a budget decision, a system that tells you where it's blind beats one that just sounds sure.