R
Redica Systems Redica Systems

GPT-4o Knowledge Graph
Pharma Intelligence Revolution

Transforming fragmented regulatory data into navigable intelligence for top pharmaceutical companies.

Pharma Tech Knowledge Graph AI/ML

Regulatory Intelligence Transformed

At Redica, we used GPT-4o and a knowledge graph to turn fragmented pharma regulatory data into real intelligence. We built a system that connected guidance, inspections, enforcement actions, and CFRs so users could actually navigate it. I used GPT-4o to extract relationships, summarize documents, translate non-English content, and surface links others missed. Users could chat with any document, auto-generate site risk briefings, and explore complex topics without slogging through PDFs. The result: faster answers, clearer context, better decisions.

Supplier Risk Intelligence

Real-time tracking of regulatory events and risk patterns across pharmaceutical suppliers

Supplier Scorecard Dashboard showing risk scores, inspection data, and regulatory events timeline

Global Regulatory Intelligence

Interactive mapping of regulatory signals across regions, industries, and compliance categories

Global Regulatory Intelligence dashboard with world map, signal categorization, and industry breakdowns

Inspection Trends & Patterns

Advanced analytics revealing inspection patterns, compliance trends, and predictive risk indicators

Inspection trends dashboard showing 483 issuances, compliance patterns, and inspection type breakdowns
62,418
Total Nodes
Connected data points
214,786
Relationships
Intelligent connections
273ms
Query Response
Median latency

The Problem

Redica had deep inspection data. Structured, clean, and trusted by top pharma companies. But their regulatory intelligence product was nascent. No structure. No connection to anything else.

The challenge: take a messy pile of guidance, warning letters, and enforcement actions, and make it usable. Not just searchable. Navigable. It needed to show context across regulatory activity, inspections, and risk so QA, compliance, and strategy teams could see the patterns before they turned into problems.

Customers didn't want a list of 483s or a PDF dump of new guidance. They needed to answer questions like:

  • What sites have been cited for data integrity in the last 12 months?
  • What recent guidance touches on that topic?
  • Are there patterns across regions, product types, or regulators?
  • Which CDMOs are exposed based on those trends?

The data existed. The relationships didn't. Our job was to build those links at scale.

What We Built

🔗 Graph Topology View

Most systems pile on more data. We focused on surfacing what matters and how it’s connected.

214,786
Connected Relationships
Total Nodes 62,418
Connected data points
Avg. Relationships/Node 18.7
Dense interconnections
Top Connected Node Types:
Regulatory Topic → Document 47.3 avg links
Site → Inspection Finding 31.8 avg links
Manufacturer → Enforcement 24.6 avg links
Document → Reg Authority 19.2 avg links
Site → Regulatory Topic 15.7 avg inferred

We started by defining the objects that mattered:

Sites
Inspection findings
Documents
Regulatory topics
Regulatory bodies
Manufacturers
Enforcement actions

Each object had metadata. Each link had provenance and weight. We used GPT-4o to extract candidate relationships, LangChain to chunk and parse long-form documents, and Neo4j to store and traverse the graph. I worked with engineering on the schema design and defined the UX around exploring these relationships without overwhelming users.

Technology Stack

GPT-4o
Relationship extraction
LangChain
Document processing
Neo4j
Graph storage & traversal

How We Used GPT-4o at Redica

AI that actually helped people get their work done

The graph gave us structure. GPT-4o helped us pull meaning from inspections, enforcement actions, and regulatory documents. We used it to cut through noise, reduce manual work, and get users the answers they were looking for—without wasting their time.

1. Chat with an inspection or a document

Most of Redica's users aren't searching for PDFs. They're trying to answer questions.

What was the root cause in this 483?
How does this compare to similar findings across sites?
What does current EMA guidance say about this issue?

We added chat to any node in the graph. You could open an inspection or guidance doc and ask a real question. The model used the graph context and source text to give a useful answer, with references. No magic. Just fast access to information that mattered.

2. Summarization and translation for regulatory docs

A lot of documents in Redica had no summaries. Many weren't in English. That slowed everything down.

We used GPT-4o to fix both.

Every document now has a clear, scoped summary that regulatory teams can scan quickly. If the original language wasn't English, we translated it. If it lacked metadata, we filled it in with topic and geography. We gave users a reason to open the document instead of skipping it.

3. AI-assisted link discovery

Some documents are related. Some just seem like they are.

We used GPT-4o to identify connections that weren't obvious through keywords. For example, an FDA observation on inadequate process control linked to an EMA guidance on aseptic processing. Same theme, different terms. The graph didn't see it. The model did.

We surfaced these links as "related guidance" or "related inspections" in the UI. It gave users context without needing them to know the right words to search.

4. Auto-generated site risk briefings

Customers spend hours compiling reports before audits or internal reviews. They pull citations manually, summarize findings, and guess what context to include. We built a tool that does most of that for them.

You enter a site or manufacturer. It pulls in relevant inspections, observations, enforcement actions, linked documents, and guidance. Then it assembles a briefing that's actually readable. Structured. Reviewable. Editable.

It doesn't replace judgment. It just saves the team from doing the same work over and over.

My Role

Schema Design

Defined schema alongside engineering and data science teams

Requirements Mapping

Mapped product requirements to user-facing features and model evaluation

Evaluation Metrics

Set evaluation metrics: recall of relevant nodes, user task completion, query latency

Customer Research

Prioritized development based on customer interviews and feedback

Feedback Loop

Built feedback loop with SMEs to validate edge accuracy and reduce false positives

API Contracts

Scoped and reviewed API contracts for frontend graph exploration tooling

📆 Timeline of Linked Events

Regulatory teams can now see when new documents signal changes in inspection behavior

March 2024
Peak Event Cluster
• 127 new FDA warning letters
• 43 major EU guidance updates
August 2023
Guidance → Inspection Correlation
• 89 EMA documents published
• 312 inspection findings (sterility guidelines)
Average Lag
46 Days
Guidance → Observation
Guidance-Linked Observations 87%
Within 90-day window
High-Signal Document Types
EMA Q&A Updates 4.7x
FDA Draft Guidance 3.2x
Lead Time Advantage
Regulatory teams can now prepare for inspection behavior changes before they happen

Results

60k+
Nodes

Across 5 core object types

200k+
Relationships

Connected intelligence

300ms
Query Response

Median after optimization

3-4x
More Context

vs keyword search

3
Enterprise Deals

Closed in 6 weeks

100%
Cross-Regional

Guidance links surfaced

🔍 Regulatory Complexity Analysis

Cross-jurisdictional intelligence reveals hidden regulatory patterns and precedent connections

Multi-Jurisdictional Coverage
FDA (US) 12,847 linked docs
Highest cross-reference density
EMA (EU) 9,234 linked docs
Strong harmonization signals
Health Canada 4,567 linked docs
ICH alignment patterns
PMDA (Japan) 3,892 linked docs
Emerging convergence
73%
Cross-Authority Citations
Documents referencing multiple jurisdictions
Topic Interconnection Density
Data Integrity
847 links
CAPA Systems
692 links
Supply Chain
578 links
Sterility
434 links
Process Valid.
389 links
Cleaning Valid.
356 links
Labeling
267 links
Facilities
234 links
Equipment
198 links
High Complexity Medium Lower
Regulatory Intelligence
Surface cross-jurisdictional patterns and precedent relationships that drive regulatory strategy

Search vs Graph Query Comparison

Metric Keyword Search Graph Query
Avg. relevant docs found 5.7 11.2
Time to first insight 3m12s 38s
Tasks completed (SMEs) 54% 92%
# of hops to full context N/A 2.3

⚙️ Query and Traversal Metrics

Fast, deep, and useful—the graph changes how users get work done

273ms
Median Query Latency
Post-cache optimization
2.1
Traversal Depth
Median hops to context
Top Query Types
Site → Topic → Document
Manufacturer → Enforcement → Topic
Topic → Guidance → Regulator
Graph-Assisted Tasks vs Manual
Risk Heatmap Generation 12.3x
Faster than manual process
QA Briefing Prep 8.7x
More citations used
Document Reviews 94%
Auto-prepopulated via graph
Infrastructure Impact
The graph changed how teams worked. Less tab-hopping. Fewer manual compilations. More time spent making decisions, not assembling context.

FAQ

What's the tradeoff between speed and graph depth, and how did we handle it?

We limited default traversal depth for common queries and precomputed relationship paths for the most used node types. Redis handled caching. This kept UX responsive without oversimplifying the graph.

How did we measure graph quality in a non-technical domain?

We had regulatory experts from top pharma companies on staff. They reviewed relationships directly. If a link didn’t hold up, it was removed. We didn’t pad counts or chase novelty. The graph had to reflect reality.

We also built feedback tools into the product. Early on, we weighted input from a trusted group of power users. They knew the space and gave direct, actionable feedback. It helped us catch weak connections and keep the signal clean.

What's next?

Plugging the graph into dynamic monitoring. Trigger alerts when new documents strengthen risk signals for a known site. We already started work on query-driven workflows and narrative explanations on top of the graph engine.

Intelligence Transformed

We turned a disconnected regulatory corpus into a navigable graph with provenance. Guidance, inspections, enforcement actions, and CFRs linked in one place.

Users stopped guessing keywords and hopping tools. From a citation to a finding to related guidance in a few clicks. Time to first insight 3m12s→38s. SME task completion 54%→92%.

Teams could track issues across sites and regions and prep for audits without copying data by hand. It reflected how people actually work and what they need to move faster and make better decisions.

Ship a knowledge graph that reduces time to insight.

I’ll help you define objects and relationships, set evaluation, and use LLMs where it adds value. If you have volume and messy text, we can scope a pilot in ~4-8 weeks and measure time to insight and task completion.

Talk About Your Data
R
Redica Systems
Regulatory Intelligence Platform
Learn more at redica.com
Back