Project vkg · HTML Copy Enterprise Vector Knowledge Graph – Case Study | Krish Shah
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Data network knowledge graph visualization
Knowledge Graph Pharma Compliance Neo4j

Enterprise Vector Knowledge Graph

Cross-Domain Compliance Engine for Pharmaceutical Research

Industry Pharma Research
My Role Lead Architect
Documents Indexed 5,000+
Accuracy 98%+
Governing Bodies 7

The Challenge

A pharmaceutical research centre operated in a regulatory labyrinth spanning multiple domains. Researchers needed a way to validate SOPs, compliance documents, and procedures against guidelines from seven different governing bodies — a process that previously took days and was prone to human error.

Business Need

The goal was to build a cross-domain validation and compliance engine that centralised regulatory guidelines, enabled audit-ready responses, and provided instant clarity to engineering and scientific teams. The solution had to run entirely within the client's private network and integrate with existing knowledge systems — no cloud dependency.

Key Constraints

  • Seven separate governing bodies with varying regulations and vocabularies
  • Requirement to run entirely within the client's secure private network
  • Support for 5,000+ pages of documentation with near-real-time updates
  • Interoperability with existing document management and search tools
  • Legally defensible, auditable responses for regulatory inspections

System-Thinking Approach

We approached the platform as a living knowledge ecosystem. Each regulation, SOP, and domain entity became a node in the graph, with vector embeddings capturing semantic meaning. A hybrid search strategy combined vector similarity with rule-based compliance logic to deliver precise, traceable responses. The MVP ingested documents from seven regulators, extracted entities, generated embeddings via a domain-tuned transformer model, and stored them with graph relationships. A search API offered contextual retrieval and validation suggestions, with a full audit log for traceability.

System Architecture

Ingestion Service

Parses PDF, Word, and HTML documents across all seven regulatory sources

Embedding Service

Domain-tuned transformer models converting documents to semantic vectors

Graph Database

Neo4j with vector indexes and rule-based edges for compliance relationships

Compliance Engine

Applies regulatory rules across domains with conflict detection and resolution

Search & Validation API

GraphQL and REST endpoints for contextual retrieval and SOP validation

Audit Log Layer

Tamper-evident logs of every query and response for regulatory inspection

Impact & Outcomes

5K+ Documents indexed
98%+ Contextual accuracy
Days→Min SOP validation time
7 Regulators unified

Research teams could validate SOPs and resolve knowledge gaps in minutes instead of days. The platform's audit trail gave compliance officers full confidence during regulatory inspections.

Technologies Used

Python FastAPI Node.js Transformer Models (domain-tuned) Faiss Vector Indexing Neo4j Vector Search Extensions Docker Kubernetes (on-prem) GraphQL REST APIs
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