The challenge. The approach.
Architected and implemented a GraphRAG (Graph-based Retrieval-Augmented Generation) system for an industrial automation client in the packaging sector. The system integrates a knowledge graph (Neo4j) with LLM-powered retrieval to answer complex technical queries across thousands of process and maintenance documents.
My scope covered the document-to-graph data flow, embedding and indexing pipelines, and orchestration of retrieval and generation in LangGraph. The system brings retrieved subgraphs and document context into the LLM’s answer generation, rather than relying on the model’s parametric knowledge alone.
The technical focus was making connected information usable: organizing process knowledge in Neo4j, retrieving relevant context for a technical question, and retaining the source lineage needed to inspect an answer. That traceability supports review, troubleshooting, and compliance workflows without implying that a generated answer is automatically correct.
Inside the system.
Graph-grounded retrieval
Neo4j subgraphs and document context supply the evidence for technical answers.
Orchestrated generation
LangGraph connects the retrieval and generation stages into an explicit workflow.
Source traceability
Document ingestion, embeddings, and indexing preserve a route back to the underlying source.
From input to impact.
Data flow: ingestion (documents → graph) and query (user question → retrieval → LLM → answer).
The technical foundation.
Technologies reflect the original project. Outcomes are reported from the existing portfolio; client-identifying details are withheld.
Where I would explore next.
These are potential research and engineering extensions, not methods claimed as implemented in this project or additional measured outcomes.
Hybrid, multi-hop retrieval
Evaluate vector and lexical retrieval with graph traversal for questions spanning multiple connected entities.
Neurosymbolic constraints
Explore ontology-aware query validation and domain rules around LLM-generated graph queries.
Evidence-based evaluation
Benchmark retrieval coverage, citation faithfulness, and answer quality on domain-specific questions.