Clinical Decypher.

The problem

Clinical trial protocols and Target Product Profiles are dense, multi-stage documents authored by cross-functional teams under regulatory and timeline pressure. Implicit assumptions ("expected enrollment rate," "anticipated dropout," "assumed standard of care") slip through reviews because human reviewers can't systematically catch every probabilistic claim across a 100+ page document.

The downstream cost is enormous: R&D costs are rising ~10% per year, 30% of protocols undergo amendments (often because an early-stage assumption broke), and trial failures linked to poor feasibility cost the industry billions. The Clinical Development Lead (CDL) / Global Clinical Lead (GCL) owns the decision but doesn't have a tool that systematically surfaces and registers assumption risk before approval.

The solution

Clinical Decypher runs a structured assumption intelligence layer over uploaded protocols and TPPs. The AI auto-extracts numeric and qualitative assumptions, detects probabilistic language, identifies assumptions lacking direct evidence, maps each assumption to supporting citations, and produces a Structured Assumption Register with confidence scoring and risk prioritization.

The human stays in the loop at every stage: extraction → classification → evidence mapping → risk scoring, each with override and edit capability. Final output exports as PDF or CSV for downstream review, regulatory submission preparation, or portfolio decision-making.

How it works

Flow: User starts new analysis → uploads TPP + protocol draft → AI runs document ingestion → assumption extraction (explicit + implicit, numeric + qualitative, probabilistic language flagged) → structured classification (AI organizes assumptions into categories) → evidence mapping (AI links assumptions to supporting citations or flags lack of evidence) → risk scoring → user reviews each stage with edit and override → final Structured Assumption Register export (PDF / CSV) → save & resume across sessions.

Model choice: a strong general-purpose LLM (the MVP doesn't require domain-specific clinical models because the task is structured reasoning over provided protocol inputs, not open-ended clinical diagnosis). Domain-specific models may improve terminology handling later, but the foundational task is well-bounded.

AI tone: expert Clinical Decision Intelligence Analyst — analytical, objective, evidence-based, structured, concise; transparent about uncertainty; never speculates; flags missing or unclear evidence explicitly rather than inventing it.

Who it's for

Internal users: Clinical Development Lead (CDL) / Global Clinical Lead (GCL) — the role that owns molecule clinical development from asset strategy through study monitoring and amendments. The CDL/GCL works across asset strategy, TPP definition, protocol construction, adaptive decision intelligence, study monitoring, and amendment management — Clinical Decypher's assumption intelligence layer plugs into Phase 3 (Protocol Design & Cross-Functional Review) where the cost-of-fixing-later compounds fastest.

Customer: B2B — pharma companies and Contract Research Organizations (CROs) running clinical R&D programs.

Why it matters

The clinical decision intelligence tools market is at $2.5B in 2026, growing to $3.9B by 2030 at 12% CAGR — and the incumbents (Veeva Vault, Medidata, Oracle Health) are structured around document management, not assumption intelligence. The wedge for AI-native players is exactly the high-leverage analytical work that human reviewers can't reliably do across dense documents under timeline pressure.

Strategic bet: catching unvalidated assumptions before protocol approval has a multi-hundred-million-dollar return on investment per trial avoided-amendment or avoided-failure. The category is willing to pay for tools that demonstrably reduce protocol amendment rates and trial failure rates — and the structured assumption register is the auditable artefact regulators, sponsors, and CROs all need but currently produce informally if at all.

At a glance

Project

Clinical Decypher

Built by

Pranathi Vangeepuram

One-liner

AI assumption-extraction engine for pharma clinical R&D — turns TPPs and protocol drafts into structured Assumption Registers with confidence scoring and evidence mapping.