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.

// 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)

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.

More about me →

START HERE

WRITING

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.

SEC 10-K Filings Graph →

25 annual reports from 5 mega-cap companies as one graph of relationships - competitors, dependencies, and risks tracked over time - built from SEC 10-K filings with a working question-answering demo.

More verticals in progress - new graphs land here as they ship.

CONTACT

Working on a graph?

Tell me what decision the system should support and which data you have. You get a direct answer on whether a graph is worth scoping, and what the first step would be.

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