Submissions scattered across systems
Regulatory evidence lives in LIMS, ERP, RIM, and document repositories — each with its own formats and terminology. Every filing starts with a hunt across systems that were never designed to talk to each other.
Industry · Pharma and life sciences
From molecule to market, one governed data spine.
Fluree connects clinical, regulatory, and commercial data into a single governed knowledge graph your teams — and your AI — can trust. No rip-and-replace, and no more assembling compliance lineage by hand.
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Pharma runs on data its own systems can’t connect.
Decades of best-of-breed systems left life-sciences data fragmented exactly where it matters most — submissions, studies, and audit trails.
Clinical data trapped per-study
Each trial captures data in its own schema and vocabulary, so nothing learned in one study carries into the next. Cross-study questions mean manual reconciliation, again and again.
Compliance lineage assembled by hand
When a reviewer or auditor asks where a number came from, teams reconstruct provenance manually — tracing spreadsheets, emails, and system exports back to the source, one data point at a time.
Four ways life-sciences teams put the graph to work.
Start with the workflow that hurts most. Each one runs on the same governed foundation, so the second use case ships faster than the first.
Regulatory reporting
Generate submission-ready reports from connected, governed data instead of compiling evidence by hand — every figure traceable back to its source system.
Medical & commercial Q&A with traceable answers
Give medical affairs and commercial teams governed, source-cited answers over connected pharma data — so every response carries its provenance instead of a shrug.
AI-ready in weeks, not months.
Not a benchmark — a production deployment at a $2B+ pharmaceutical company.
Featured case study
How a $2B+ Pharmaceutical Company Made Its Data AI-Ready in Weeks Instead of Months
10x
faster data delivery
$3M
manual labor cost reduced
3 weeks
new use cases live
Go deep on the workflow that matters to you.
Each solution page covers the architecture, the partners, and the path to production for one life-sciences workflow.
Written by the people who build this.
Practitioner guides and reference material on semantic data, knowledge graphs, and regulatory compliance in life sciences.
Pharma and life sciences data questions, answered.
Short answers to what regulatory, clinical, and data leaders at life-sciences companies ask us most.
How are knowledge graphs used in pharma and life sciences?
They connect clinical, regulatory, and commercial data into one governed graph, so answers are traceable and audit-ready. Teams use them for regulatory submissions, clinical data that connects across studies, and medical and commercial Q&A.
How can pharma companies speed up regulatory submissions?
By unifying CMC data from LIMS, ERP, and lab systems into a reusable Drug Manufacturing Knowledge Asset aligned to gist-Pharma. Submission-ready reports are then generated from connected, governed data instead of compiled by hand. The approach behind a 33% cut in time to regulatory approval is detailed on Fluree’s Regulatory Intelligence page.
How does Fluree connect clinical trial data across studies?
It builds a governed clinical knowledge graph with entity resolution across studies and Part 11-ready lineage on every record. Trial data, safety signals, and operational metrics connect, so what is learned in one study is no longer stranded in its own schema.
How do you show an auditor where a number came from?
Every figure is traceable back to its source system, because lineage travels with the data. Teams stop reconstructing provenance by tracing spreadsheets, emails, and system exports one data point at a time.
Can AI answer medical and commercial questions with citations?
Yes. Fluree gives medical affairs and commercial teams governed, source-cited answers over connected pharma data, so every response carries its provenance.
Does Fluree work in production at a large pharmaceutical company?
Yes. At a $2B+ pharmaceutical company, Fluree consolidated 10+ warehouses, reduced manual data remediation labor by $3M, and brought new analytics use cases live in 3 weeks instead of 9 months.
Where should a life-sciences team start?
Start with the workflow that hurts most: regulatory submissions, clinical data, or medical and commercial Q&A. Each runs on the same governed foundation, so the second use case ships faster than the first.
Load the demo graph on a fresh ledger.
Pharmaceuticals ships as a working knowledge graph: 15 classes, 146 entities, 5 JSON-LD files, and 15 packaged competency questions. Load it, ask, verify.
- 01
Create a free workspace
Start free on Fluree AI, no credit card, and create an empty database. Developers can use the Fluree CLI instead.
- 02
Import the graph
Load the 5 JSON-LD files, model first. The whole dataset commits in seconds; the kit’s README walks through it click by click.
- 03
Ask the questions
Put the 15 competency questions to the AI agent in plain English and check its answers against the expected results.
Make your pharma data compliant, connected, and AI-ready.
Tell us where your data lives today — we’ll show you what a governed knowledge graph looks like on your systems.
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