PRODUCT UPDATE

Introducing GraphRAG: Knowledge Graphs Without the Manual Labor

Our new graph extraction engine discovers entities, relationships, and domain structure automatically — no ontology design required.

The Problem

Standard RAG treats your documents as a bag of independent chunks. You embed them, store them in a vector database, and retrieve the most similar ones when a query comes in.

This works well for direct questions: "What is our refund policy?" finds the chunk that contains your refund policy. Simple.

But real knowledge isn't flat. It's a web of relationships.

Consider this query: "What are our contractual obligations to companies connected to Acme Corp?" A standard RAG system will find chunks that mention Acme Corp directly. But what about Acme's subsidiaries? Their parent company? Partners they've referenced in agreements? That information might exist in your documents — but vector similarity won't surface it, because those chunks don't mention "Acme Corp" by name.

The structure of knowledge matters. And flat retrieval ignores it.

The Traditional Solution: Build a Knowledge Graph

The industry knows this. That's why knowledge graphs exist. Extract entities from your documents, define relationships between them, and query the graph to traverse connections.

The problem is what it takes to build one.

Traditional knowledge graph projects require:

• Ontology design — Data architects spend weeks defining what entity types exist (Person? Organization? Contract?) and what relationships connect them (employs? owns? references?)
• Schema iteration — The first ontology is never right. You discover edge cases, refine definitions, and rebuild
• Extraction pipelines — Custom NER models, relationship extractors, and entity resolution systems
• Ongoing maintenance — New document types break the schema. New entity types require pipeline updates

This is a multi-month project for a team of data engineers. For most companies, it's not feasible — so they settle for flat retrieval and accept the limitations.

The Solution: Automated Knowledge Domain Analysis

Today we're releasing GraphRAG with a capability we call automated knowledge domain analysis. It eliminates the manual ontology design step entirely.

Here's how it works: When you ingest documents, our graph extraction engine reads them and discovers the domain model itself. It identifies what kinds of entities exist in your corpus, what relationships connect them, and what attributes matter — without you defining any of it upfront.

The schema emerges from the data.

What Gets Extracted

• Entities (nodes) — People, organizations, products, concepts, locations, documents — automatically typed based on context
• Relationships (edges) — Employment, ownership, contractual obligations, references, dependencies — labeled with the nature of the connection
• Domain structure — The ontology itself: what categories of things exist in your world and how they typically relate

Example: Before and After

Query: "Show all contracts with Acme Corp"

The graph traversal surfaces connected information that flat retrieval misses. And it does this without anyone manually mapping out "Acme Corp → subsidiaryOf → Acme Holdings." That relationship was discovered automatically during ingestion.

Why This Matters

For data teams, automated domain analysis changes the economics of knowledge graphs. What used to require weeks of ontology design and custom pipeline development now happens at ingestion time. You get structural understanding of your documents without the upfront investment.

For end users, queries become more powerful. Questions that require understanding relationships — "Who reports to Sarah's manager?", "What vendors are connected to our delayed projects?", "Which policies affect employees in acquired companies?" — now return complete answers.

For organizations, institutional knowledge becomes navigable. The connections between people, projects, contracts, and concepts don't live only in the heads of veteran employees — they're captured in a queryable structure.

Getting Started

GraphRAG is now available on all GuidedMind plans. If you're an existing customer, graph extraction will run automatically on your next document ingestion. No configuration required.

To take advantage of graph-aware retrieval:

• Dashboard: Enable "Graph Traversal" in your pipeline settings
• API: Pass enable_graph: true in your search or agent requests

You can also explore the extracted graph directly through our dashboard to see what entities and relationships were discovered in your corpus.

Try it now