Knowledge graphs,
built to be used.

Practical Neo4j and graph-AI work — ontology design, multi-source ingestion, and retrieval that holds up in production. By Konrad Kaliciński.

// patterns from graphs in production
// context graphs — grounding LLM answers(:Document)-[:MENTIONS]->(:Entity)-[:GROUNDED_IN]->(:Context)
// supply chain — component traceability(:Supplier)-[:SHIPS]->(:Component)-[:ASSEMBLED_INTO]->(:Product)
// crop protection — biorationals against pests(:Biorational)-[:TARGETS]->(:Pest)-[:THREATENS]->(:Crop)
// legal — case law over regulations(:Ruling)-[:INTERPRETS]->(:Article)-[:PART_OF]->(:Regulation)
01 — ABOUT

I build and ship knowledge graphs on Neo4j — ontology design, multi-source ingestion, GraphRAG, and the unglamorous plumbing that makes a graph survive contact with real data.

I write about this work every week: what is working, what is not, and what most teams get wrong on their first knowledge-graph project.

— Knowledge graph practitioner and writer: building, teaching, and consulting on graph-AI projects.

02 — WRITING
Medium LONG-FORM · WEEKLY Long-form articles on Neo4j, GraphRAG, and applied graph-AI. LinkedIn SHORT TAKES Shorter takes and project notes from ongoing graph work.
03 — SELECTED WORK
Polish plant-protection product register →

A graph of registered pesticide products, active substances, crops, target pests, application windows and dosage — built from official register data and product labels, with a working question-answering demo.

04 — CONTACT

Working on a graph?

I take on consulting engagements, knowledge-graph reviews, and collaboration on graph projects. Send me a few lines about your graph on LinkedIn — I reply to every serious message.

Message me on LinkedIn