Knowledge Graphs & GraphRAG
Structured, relationship-aware intelligence. From entity extraction to multi-hop reasoning over your enterprise data.
Graphs vs Vectors
Vector databases find similar content. Knowledge graphs understand how things are connected — enabling causal reasoning, multi-hop queries, and structured intelligence.
Vector Database
Similarity-basedQuery: Which products does Acme Corp sell in Europe?
Embeds the question → retrieves top-k chunks by cosine similarity → may miss that "Acme Corp" → CEO → subsidiary → European product catalog requires 3 hops.
Knowledge Graph
Relationship-awareQuery: Which products does Acme Corp sell in Europe?
Traverses: Acme Corp →[subsidiary_of]→ Acme EU →[sells]→ {Widget X, Widget Y} →[region]→ Europe. Each hop is explicit.
Why graphs excel at enterprise reasoning
Enterprise data is inherently relational — org charts, supply chains, regulatory hierarchies, product catalogs. Vectors flatten this structure into a soup of embeddings, losing the edges that encode causality, ownership, and temporal ordering.
Knowledge graphs preserve these relationships as first-class citizens. A multi-hop query that requires traversing “subsidiary → product → region → regulation” is a native graph operation — not an emergent behavior hoped for from embedding proximity.
GraphRAG Pipeline
Seven stages from raw documents to multi-hop, citation-backed answers. Each stage is independently observable and tunable.
Document Ingestion
Raw text, PDFs, databases
Source documents are loaded and normalized — PDFs are OCR-parsed, tables are linearized, and structured records are converted to text. Each document is assigned a provenance ID for downstream traceability.
How Graphs Are Built
Four approaches to populating a knowledge graph — each balancing automation speed, accuracy, and cost for different deployment contexts.
LLM-Automated
Feed raw text to OpenAI- or Anthropic-class APIs with structured output schemas. The model extracts entities, infers relationship types, and outputs triples that are directly inserted into the graph.
Schema-First
Define a formal ontology (entity types, relationship types, cardinality constraints) before populating. Extraction pipelines are constrained to the schema, ensuring consistency at the cost of flexibility.
Hybrid
Combine automated LLM extraction with human-in-the-loop curation. Automated passes generate candidate triples; domain experts review, correct, and approve before graph insertion.
Incremental
Continuous ingestion pipeline that processes new documents as they arrive, extracts entities, deduplicates against existing nodes, and merges new edges — keeping the graph perpetually current.
How Graphs Answer Questions
Four fundamental traversal patterns that power graph-based question answering — from single-hop lookups to community-level analysis.
Single-hop
1 hop“Who is the CEO of Acme Corp?”
Direct entity-relationship-entity lookup. One traversal step from the source node across a typed edge to the target node.
Multi-hop
3 hops“Which products does the CEO's company sell in Europe?”
Three or more traversal hops chaining through intermediate entities. Each hop narrows the result set while preserving the reasoning chain.
Path Finding
variable“How are Entity A and Entity B connected?”
Discover the shortest or most relevant path between two nodes. Returns intermediate entities and relationship types that form the bridge.
Community Query
global“What topic clusters exist in this dataset?”
Queries against community summaries rather than individual nodes. Returns high-level thematic clusters with representative entities and inter-community relationships.
Production Metrics
Benchmarks from production knowledge graph deployments across enterprise domains — measured against human-annotated ground truth.
Semantic Alignment
Measures how well extracted entities and relationships match the semantic intent of the source documents. High alignment means the graph faithfully represents the knowledge within.
Query Accuracy
Percentage of multi-hop queries that return the correct, complete answer set when validated against human-annotated ground truth. Accounts for both precision and recall over traversal paths.
Construction Speed
Thousands of entities extracted, deduplicated, and inserted into the graph per minute using parallel LLM extraction with batched graph writes.
ROI
Typical return on investment from structured knowledge graph deployments — driven by reduced research time, fewer errors, and automated compliance checks across enterprise data.
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Build a knowledge graph for your domain.
Describe your data sources and relationships. We’ll architect the graph schema, extraction pipeline, and GraphRAG integration.