Back to case studies
Agentic RAG System with Vector Search and Knowledge Graph Integration
Client name withheld · AI & Machine Learning
Answer accuracy
vector-only baseline
significantly higher
Retrieval
single-hop
multi-hop
Security review
passed
A confidential enterprise client required a high-accuracy question-answering system combining vector retrieval and graph-based knowledge management to navigate complex, inter-related business data.
What changed
Before
Query
User question
Retrieve
Vector search
single-hop only
Answer
Incomplete answers
on connected data
After
Query
Agent
plans retrieval
Retrieve
ChromaDB
vector
Neo4j + Graphiti
temporal graph
Reason
Multi-hop synthesis
Guard
AWS Bedrock
guardrails
Vector search alone missed connections across documents; adding a temporal knowledge graph made multi-hop questions answerable.
What we built
Standard vector-only RAG approaches produced incomplete answers for the client's complex, relationship-rich enterprise data. The project required combining vector similarity search with graph traversal to capture explicit connections between entities, while Graphiti provided temporal knowledge management for data that evolves over time.
Key Features
AWS Bedrock Knowledge Bases as the primary managed RAG foundation.
Multi-agent system with specialized agents for retrieval, reasoning, and synthesis.
Hybrid retrieval: vector search (ChromaDB, PGVector/Supabase) combined with Neo4j graph traversal.
Graphiti integration for temporal knowledge management — tracking how entity relationships evolve.
Neo4j + APOC-Cypher for building and querying the enterprise knowledge graph.
LLM-powered semantic document chunking for improved retrieval precision.
AWS Bedrock Guardrails for PII protection and adversarial prompt injection prevention.
Results
Working Agentic RAG POC demonstrating significantly higher answer accuracy vs. vector-only baseline.
Temporal knowledge management enabled the system to reason about how facts change over time.
Graph-based retrieval surfaced multi-hop connections invisible to pure vector search.
Guardrails implementation passed client security and compliance review.
Stack
Engagement: 6 months · AI Infrastructure & MLOps, Real-time & Industrial Data Platforms, Product Engineering
Next step
Let's look at your platform together.
45 minutes · an engineer, not a sales rep · no obligation