Enterprise Vector Knowledge Graph Platform
Problem
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.
Context / 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 within the client’s private network and integrate with existing knowledge systems.
Constraints
- Seven separate governing bodies with varying regulations and vocabularies.
- Requirement to run entirely within the client’s secure network.
- Support for 5,000+ pages of documentation with near-real-time updates.
- Interoperability with existing document management and search tools.
- Ensuring accurate, legally defensible responses for audits.
My Role
As lead architect, I defined the vector knowledge graph data model, designed the ingestion and embedding pipelines, selected similarity metrics and oversaw the API layer. I collaborated with domain experts to ensure regulatory completeness and consulted security teams to meet stringent privacy requirements.
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 responses.
MVP Design
The MVP ingested documents from seven regulators, extracted entities, generated embeddings via a transformer model and stored them with graph relationships. A search API offered contextual retrieval and validation suggestions, with an audit log for traceability.
Architecture Breakdown
- An ingestion service parsing PDF, Word and HTML documents.
- An embedding service built on domain-tuned transformer models.
- A graph database supporting vector indexes and rule-based edges.
- A compliance engine applying regulatory rules across domains.
- An API layer providing search, validation and audit logging.
Final Solution & Results
The deployed platform indexed 5,000+ documents and delivered 98%+ contextual accuracy. Research teams validated SOPs and resolved knowledge gaps in minutes instead of days, with strong traceability for audits.
Tech Stack
- Python, FastAPI and Node.js
- Transformer models (domain-tuned) with Faiss vector indexing
- Graph database (Neo4j) with vector search extensions
- Docker & Kubernetes (on-prem)
- GraphQL and REST APIs