Enterprise RAG, GraphRAG & Knowledge Systems
Evidence-grounded knowledge systems using retrieval, graphs, taxonomies and domain context.
Image · Enterprise RAG, GraphRAG & Knowledge SystemsEnterprise knowledge systems combine retrieval-augmented generation with metadata, taxonomies, ontologies and knowledge graphs so responses can be grounded in authorised information and traced to evidence.
Matchpoint approaches AI as an operating capability with accountable owners, explicit decision gates, measurable acceptance criteria, documented architecture and a practical path from discovery to production.
Enterprise knowledge work often depends on relationships across documents, entities, events and concepts. We design retrieval around the questions users need to answer, the evidence they must see, their access rights and the structure of the underlying domain.
The information architecture covers source authority, ingestion, chunking, metadata, taxonomies, ontologies, entity resolution and knowledge graphs. Basic RAG, structured retrieval and GraphRAG are evaluated according to the relationship complexity and evidence requirements of each task rather than treated as interchangeable patterns.
Evaluation separates retrieval from answer generation. We test source coverage, relevance, ranking, relationship traversal, groundedness, citation accuracy, permission handling, latency and cost using a question set drawn from real work. Production traces feed an improvement backlog for content, retrieval and product design.
How we deliver enterprise rag, graphrag & knowledge systems
- Knowledge-source and access design
- Chunking, metadata, taxonomy and ontology
- RAG and GraphRAG architecture
- Citation, relevance and retrieval evaluation
Enterprise RAG, GraphRAG & Knowledge Systems — frequently asked questions
GraphRAG is useful when answers depend on relationships across entities, documents, events or concepts that basic similarity retrieval cannot represent reliably.
Evaluation covers retrieval relevance and coverage, answer groundedness, citation accuracy, permission handling, latency, cost and performance on the real questions users ask.
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