Enterprise AI / Knowledge Retrieval
Knowledge Intelligence & GraphRAG
Hybrid search and connected knowledge turn scattered documents into grounded, traceable answers.
THE ENGINEERING STORYFrom complexity
From complexity
to capability.
The challenge
Retrieve relevant context across documents and relationships, then make the source of each answer understandable.
Engineering contributions
- Expanded hybrid retrieval with graph-based and multimodal approaches.
- Represented knowledge relationships in Neo4j and integrated pgvector semantic retrieval.
- Applied query decomposition, metadata filtering, and reranking.
- Supported source-grounded answer generation within the broader Vertex platform.
Performance & reliability
Retrieval relevance improved from 76% to 85% across the broader knowledge platform work.
REPORTED ENGINEERING OUTCOME85%retrieval relevance
Results are specific to this workstream and drawn from the supplied professional history. Preview interface data is illustrative.
Tools behind the work
PythonFastAPILangChainLangGraphGraphRAGNeo4jPostgreSQLpgvectorElasticsearch
Explore the reconstructed interface
The visual demonstration shows how these capabilities can be presented. It uses local sample data and does not connect to employer systems.
Open illustrative demonstrationNEXT ENGINEERING STORY