Your knowledge is connected. Your AI should be, too.
Technical documentation, business entities, and operational data often describe the same world in incompatible ways. A graph is useful when those relationships matter to your queries. It is not automatically better than a simpler retrieval stack; the first step is to establish whether graph structure will solve your actual failure cases.
How we work together
The work moves from representative questions and source data to graph modeling, ingestion, entity resolution, retrieval, and answer evaluation. With Neo4j and orchestration tools such as LangGraph, I connect the data architecture to a usable application, keeping traceability and maintenance in view.
What the engagement can include
- Knowledge graph schema and ingestion architecture
- Graph-aware retrieval and context assembly
- Grounded answers with source traceability
- Evaluation examples, operational guidance, and handover
Is this the right fit?
A useful conversation for industrial knowledge retrieval, enterprise search, and applications where entity relationships are central to reasoning. My existing industrial GraphRAG case study illustrates the approach without disclosing client-identifying details.
I’m Paolo Chignoli, a senior data scientist, AI engineer, and AI architect with an MSc in Physics from the University of Milan. I’m based in Milan and open to contractor roles and fractional partnerships with teams around the world. Remote collaboration is the default; we agree on practical arrangements together.
View my professional background on LinkedIn or explore my GitHub profile.