Pharmaceutical R&D

The Billion-Dollar Rescue

The agent mission

Save a failing $2B cancer trial. Find a hidden root cause for severe side effects buried between millions of genomic data points and thousands of handwritten doctor’s notes before the program is shut down This investigation has to move from hypothesis to proof in days: identify the patient cohorts impacted, pinpoint the causal factor (interaction, dosing window, comorbidity, or protocol deviation), and produce a regulator-ready evidence bundle that traces every conclusion back to raw sources.

The Challenge

The Great Divide

Clinical research data is split across structured systems (LIMS, EDC, genomics pipelines, imaging metadata, adverse event tables) and unstructured clinical context (physician notes, narratives, scanned PDFs, and handwritten annotations). The hard part isn’t just ingestion—it’s joining these worlds on patient, visit, and time, while dealing with inconsistent identifiers and missing fields.

Black Box

The highest-signal clues often live in “dark data”: PDFs, scans, and free text that SQL engines can’t reason over. Without automated text extraction and semantic indexing, teams fall back to manual review, which destroys speed and reproducibility.

Paralysis by Volume

At trial scale, even a small hypothesis (e.g., a supplement interaction) can require correlating genomics, lab deltas, dosing, and narrative symptoms across tens of thousands of pages. Manual cross-referencing becomes a months-long effort—exactly when the program needs a two‑week turnaround

FDA Rigor

A “likely cause” isn’t enough. You need traceability: which records were used, which transformations occurred, what filters were applied, and how each claim maps to the original note, timestamp, and patient context—so the result stands up to internal review and external audit.

How Volumez Agentic Data Platform solves this

Unlocking Text

Volumez Agentic Infrastructure ingests PDFs, scans, and handwritten notes and turns them into queryable, governed artifacts—extracted text, entities (drug, dosage, supplement, symptom), and vector indexes for semantic retrieval. Agents can retrieve relevant passages instantly while preserving a citation trail back to the source document.

The "Impossible" Join

Volumez Agentic Data Platform correlates structured biology (genomics variants, biomarkers, lab time-series) with unstructured clinical language (symptom descriptions, physician impressions, concomitant meds) using deterministic keys (patient/visit/time) plus semantic matching. This enables fast cohort slicing and root-cause isolation without losing provenance.

Automated Cartography

Volumez Agentic Data Platform auto-discovers schemas and relationships across fragmented datasets: it profiles columns, detects join candidates, flags quality issues, and builds a searchable catalog. The agent gets an instant “map” of the research space—what data exists, how it connects, and what’s trustworthy—without weeks of manual modeling.

In-Place Deep Thought

Instead of exporting data into separate analytics stacks, Volumez Agentic Data Platform runs large scans and repeated hypothesis tests close to the data. That supports rapid iteration—testing many hypotheses quickly—while minimizing data movement, cost, and time-to-answer.