Knowledge Graph Blueprint
Schema → Extract → Map → Construct → Index → Query → Reason. Structured intelligence from unstructured data.
Seven stages from raw data to structured reasoning
Click any stage for technical depth.
Schema Design
Ontology definition, entity types, relationship types, property schemas.
Defining the graph's vocabulary — what entities exist, how they connect, what properties they carry. Domain experts and engineers collaborate to model the ontology: node labels, edge types, cardinality constraints, and property schemas that enforce data quality from the start.
Graph database options
We evaluate each engine against your data shape, query patterns, and scale requirements.
Neo4j
Teams needing rich tooling, graph algorithms, and enterprise support
Amazon Neptune
AWS-native teams wanting managed infrastructure with dual-model flexibility
ArangoDB
Projects requiring graph queries alongside document and key-value access patterns
TigerGraph
Enterprise-scale graphs with billions of edges and real-time analytics requirements
How the knowledge graph enhances RAG
Four retrieval strategies that combine graph structure with generative AI.
Local Search
Query-focused entity subgraph extraction. Start at the most relevant entity and expand outward through typed relationships to build a precise context window for generation.
Build a knowledge graph for your domain.
Describe your data sources and reasoning requirements. We'll design the schema, extraction pipeline, and query layer.