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I will engineer enterprise neo4j graphrag and hybrid search
AI Systems Architect, Autonomous Swarms, MCP, GraphRAG, Claude, n8n
As an AI Systems Architect (5+ years). I help businesses build intelligent, secure AI systems ready for daily operations.
Whether you need autonomous agents, secure GraphRAG, or complex n8n automatio...
Über diesen Service
Traditional Retrieval-Augmented Generation (RAG) fails at cross-document synthesis and relational queries, leading to costly hallucinations. To query your enterprise's complex data topology accurately, you require a hybrid GraphRAG architecture.
I engineer secure, zero-hallucination vector and knowledge graph search engines using Neo4j, Pinecone, and LlamaIndex. By mapping your unstructured data into explicit relationships, the LLM traverses exact entities, guaranteeing answers grounded entirely in your private corporate data.
Business ROI & Deliverables:
- Unparalleled Precision: Combines semantic vector search with deterministic graph traversal.
- Source Citation Guardrails: Enforced verification loops ensure every output cites the exact document line.
- Cost Optimization: Implementation of semantic caching layers drastically reduces monthly LLM API bills.
- Absolute Privacy: Complete AI data sovereignty via secure indexing; your data is never used to train public models.
Transition from flat document search to intelligent corporate memory.
Discuss with me before placing order!
Datenbanktyp:
Graph-Datenbank
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Andere
Plattform:
Firebase
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mySQL
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neo4j
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PostgreSQL
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SQLite
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SQL Server
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Andere
Expertise:
Big Data
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Datenstruktur
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Design
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NoSQL
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Performance
Mein Portfolio
FAQ
What is the advantage of GraphRAG over standard vector RAG?
Standard RAG struggles with connecting concepts across multiple disparate documents. GraphRAG structures data into a Knowledge Graph (nodes and edges), allowing the AI to understand explicit relationships, providing comprehensive answers to holistic, cross-document queries.
How do you prevent the AI from leaking sensitive data to unauthorized users?
Enterprise architectures include Role-Based Access Control (RBAC) at the retrieval layer. The system only retrieves and processes context chunks that the authenticated user's ID is explicitly permitted to access.
What formats of internal data can this knowledge engine ingest?
The pipeline features multi-modal ingestion, synchronizing seamlessly with PDFs, DOCX, Notion workspaces, Google Drive repositories, Slack archives, and live SQL/PostgreSQL databases.
