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IT & SaaS

AI — Self-hosted GraphRAG on local models

For a large technology company with a print services business. A knowledge graph instead of plain vector search, open-weight models inside the company's own perimeter, no data egress. Relevance feedback tunes retrieval. Customer assistant, agent assist, sales, document drafting

Challenge
A large technology company: knowledge scattered across contracts, tickets, specifications, guides and scopes. People spend hours finding answers, support and sales answer differently, documents are written from scratch every time. Plain vector RAG confuses similar entities and loses the links between documents, and cloud models are out: customer data cannot leave the perimeter
What we did
Built a self-hosted knowledge platform on GraphRAG: entities, documents and the relations between them stored as a knowledge graph, hybrid retrieval with reranking, every answer grounded and cited. Runs only on open-weight models on the client's servers, no external APIs. A feedback loop: relevance ratings tune retrieval and ranking without retraining the model. On that layer: a customer-facing assistant, agent assist for support, a sales copilot, enterprise search and template-based document drafting from guides and scopes, with versioned drafts
Result
Cited answers for customers, support and sales from one knowledge layer, inside the company's own perimeter. Enterprise search in seconds instead of hours. Documents are drafted from templates and refined in versions, not written from scratch
CustomerassistantAgent assistSales copilotDocumentdraftingOpen-weightmodelsRelevancefeedbackKnowledgegraph · GraphRAG
Self-hosted, no data egress. A graph, not only vectors. Answers with sources
0external APIs: models inside the perimeter
4assistants: customers, support, sales, documents
secondsinstead of hours to find an answer

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