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AI & Automation6 min read

Devesh JoshiCo-founder, product

Nine years building AI platforms serving 12,000+ engineers. LLM platforms, agentic systems (MCP), and multi-model safety evaluation.

Moving beyond vector search: How GraphRAG connects complex business entities

Combining vector embeddings with Neo4j/Memgraph Knowledge Graphs to solve complex multi-hop entity queries that standard RAG fails to answer.

The limitation of naive vector similarity

Standard vector search retrieves text chunks based on semantic similarity, but struggles to synthesize relationships across disparate organizational documents. GraphRAG connects entities into a structured knowledge graph before LLM inference.

Topic Focus & Target Concepts

GraphRAG enterprise searchknowledge graph RAGvector database hybrid searchenterprise LLM knowledge graphadvanced RAG architecture

This is the work behind our AI and automation practice — agents with real grounding, voice intake, and retrieval that answers from your records rather than the model's training data.

Agents with real tool access

Running into this in your own stack? Twenty minutes, no deck.

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