neo4j-field

Design, visualise, and edit Neo4j property graph schemas as interactive React artifacts. Use whenever the user wants to design, create, draw, model, or visualise a graph schema, graph data model, Neo4j schema, property graph model, or node-relationship diagram. Handles both custom domains ("design a schema for my e-commerce system") and 32 pre-built Neo4j reference models across Financial Services, Insurance, Healthcare & Life Sciences, Manufacturing, Cybersecurity, and Industry Agnostic use cases. Trigger for "design a graph schema", "Neo4j data model", "property graph", "arrows.app diagram", "reference model", "industry template", "start from the claims fraud model", "patient journey schema", "CMDB", "IAM effective access", "entity resolution", "UBO", "FAERS drug safety", "BOM", or any Neo4j use case. Also trigger when the user uploads an arrows.app JSON or reference-model JSON. Do NOT trigger for graph charts (bar/line/pie), mathematical graphs, or visualising query results.

Progressive Use Case Value Workshop for Neo4j Solutions Engineers. Column-by-column discovery — never dumps the board all at once. Generates facilitation questions for any gap the SE can't answer. Trigger on: "workshop", "value workshop", "use case workshop", "board", "discovery session", "prepare for customer", "SE workshop", "challenger review", "business case workshop", "POV plan", "value narrative", "ROI for Neo4j", "help me build the business case". Do NOT use for graph schema design (use graph-schema-editor).

Generate realistic synthetic data for a Neo4j property graph by asking the user the right questions, then writing a Faker script tailored to their answers. Trigger when the user wants to populate Neo4j, generate fake/synthetic/test data for a graph schema, or build a dataset to test detection logic (fraud rings, entity resolution, anomalies). Trigger when the user uploads an arrows.app schema or describes a graph model and asks for sample data. Do NOT trigger for schema design (use graph-schema-studio).

POC plan and executive summary for a Neo4j deal. Reads from the current conversation. Produces a single-page HTML artifact with two sections: a structured POC plan and a slide-ready one-pager executive summary. Trigger on: "POC", "PoC plan", "proof of concept", "exec summary", "executive summary", "POV plan", "proof of value", "next steps document", "send to customer", "build the POC".

Business case and ROI snapshot for a Neo4j deal. Reads from the current conversation (se-workshop output or direct input). Produces a single-page executive-ready HTML artifact. Trigger on: "business case", "ROI", "value narrative", "value case", "financials", "cost of inaction", "value drivers", "help me justify this", "quantify the value", "build the business case", "what's the ROI".

Design a domain ontology in Neo4j with a user, optionally loading a folder of PDFs and extracting entities against that ontology. Use whenever a user wants to design a domain model / ontology, turn documents into a queryable knowledge graph, do legal / HR / finance / regulatory document analysis, structure unstructured text, or asks anything like 'help me model my domain', 'how do I make a graph from my documents', 'I have a folder of contracts/CVs/reports — help me explore them', 'extract entities from PDFs', 'build a domain model' — even if they don't say 'ontology' or 'knowledge graph' explicitly. Always trigger when the conversation involves domain modeling, PDF documents plus Neo4j, or both.

Develop Neo4j graphs end-to-end: analyze source data, design data models, ingest CSV and/or PDF data, extract entities, and validate with queries. Use when importing data to Neo4j, designing graph data models, creating knowledge graphs, or building chatbots or analytical applications backed by Neo4j.

Use when answering renewal-risk questions that need BOTH the document knowledge graph (graphrag: QBRs, calls, account plans, deals, competitors, concerns) AND structured business data (Neocarta + BigQuery: ARR, usage, tickets, subscriptions) — e.g. ARR at risk, competitive threats, usage decline, champion moves, renewal timing, or anything asking for a dollar figure or percentage. Do not answer from graphrag alone when the question needs a number that only lives in BigQuery.

Build the Mistral+Neo4j webinar renewal-intelligence knowledge graph from the hot QBR/account-plan/call-note/win-loss PDFs in data/pdf/, using the pre-built extraction schema in outputs/schemas/renewal_extraction_schema.py. Use when the user wants to run the Mistral webinar demo, load the demo PDFs, build the renewal-intelligence graph, or go from documents to a Q&A report for this specific dataset. Narrower and faster than develop-neo4j-graph: no data modeling or schema design, the schema already exists and is fixed.