Ritz

What I built. Verdikt is an AI agent that turns one input — a drug, gene, or aging-related target — into a transparent, confidence-scored decision brief. It reads across five live public biomedical databases (Open Targets, ClinicalTrials.gov, ChEMBL, PubMed, openFDA), then uses Claude as a rubric-driven judge to weigh the evidence, reconcile agreeing versus conflicting findings, and calibrate a 0–100 confidence score — with every claim traced back to its source. It's built with Claude Code as a Python/FastAPI engine and a single-page web app, and it runs live. What I found. Drug discovery doesn't fail because scientists lack data — it fails on which evidence to trust. A landmark analysis found only a small fraction of preclinical findings could be independently reproduced: evidence is abundant, but reliability is uneven. Building Verdikt showed the problem is tractable. By gathering the evidence deterministically first and having the model reason only over what it finds, the system produces honest, calibrated confidence — it stays low when the evidence is thin and surfaces genuine contradictions, instead of hallucinating certainty. Why it matters. A single go/no-go decision can cost billions of dollars and years of work, yet today it rests on weeks of manual, error-prone evidence review. Verdikt is the decision-intelligence layer between scientific evidence and billion-dollar investment decisions — making the evidence transparent, traceable, and honest, so scientists can trust the evidence, not the AI.