Beyond Vector Search
Most AI systems use vector-based RAG (Retrieval Augmented Generation) — they chunk documents into pieces, convert them to numerical embeddings, and find the “closest” match when you ask a question.
It works for simple lookups. It breaks down when you need context.
Ask a vector-based system “What’s our relationship with Acme Corp?” and it might find a contract document. But it won’t connect that Acme’s CEO mentioned budget concerns in a March call, that their renewal is in Q4, that they’ve been unresponsive for two weeks, and that their industry just had layoffs.
GraphRAG stores knowledge as a graph of entities and relationships, not isolated document chunks. Every client, project, person, decision, and preference is a node. The connections between them are edges. When you query, the system traverses relationships — not just proximity.
How It Works
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Entity extraction: When information flows through the system (emails, Slack messages, CRM updates, meeting notes), GraphRAG identifies entities — people, companies, projects, dates, decisions.
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Relationship mapping: It maps how entities connect. “Sarah at Acme Corp is the decision-maker for Project Atlas, which is blocked by budget approval from their CFO.”
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Graph traversal: When you need information, the system walks the graph. A question about Acme Corp pulls every connected node — deals, contacts, preferences, history, risks — in a single contextual retrieval.
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Temporal awareness: The graph understands time. It knows that a preference stated in January might be outdated by October. It tracks how relationships evolve.
Why 3.4x Matters
In benchmarks comparing GraphRAG to traditional vector RAG on business-context queries, graph-based retrieval delivers 3.4x higher accuracy. The gap widens on multi-hop questions — anything that requires connecting two or more pieces of information.
For business operations, almost every useful question is multi-hop:
- “Which clients are at risk?” (requires connecting engagement signals, contract dates, and sentiment)
- “What should I prioritize today?” (requires understanding deadlines, dependencies, and stakeholder expectations)
- “How should I approach this proposal?” (requires recalling client history, preferences, and past outcomes)
The Compounding Effect
Unlike stateless automation that resets with every session, a knowledge graph compounds. Every interaction adds nodes and edges. After six months, your Alacritous instance understands your business with a depth that no new hire could match — because it never forgets and never loses context.