GraphRAG services for accurate, explainable, and trusted enterprise AI 

GraphRAG (Graph Retrieval-Augmented Generation) connects large language models to a governed knowledge graph, enabling the AI to reason over structured entities, relationships, and metadata—not just raw text.

This results in factual, contextual, and fully traceable answers grounded in your enterprise knowledge.

Traditional RAG uses flat text embeddings, which often leads to hallucinations and shallow context.

GraphRAG enriches retrieval with semantic structure, ontologies, and graph context, allowing the model to understand domain concepts and provide explainable, evidence-backed answers.

The result is fewer hallucinations and answers your team can verify.

A GraphRAG pipeline usually works across five stages:

1. Content is ingested and enriched: Documents, databases, and APIs are brought into the system, where entities, relationships, and metadata are extracted.
2. The knowledge graph is built: Extracted facts are mapped to a domain ontology and stored in a graph database.
3. Evidence is retrieved with a hybrid search: Graph traversal, vector search, and keyword search are used together to surface relevant entities and supporting evidence.
4. The prompt is orchestrated: Retrieved context is assembled into a structured, permission-aware prompt.
5. The answer is generated with provenance: The model produces an answer with citations, entity lineage, and auditable source documents.

Yes. GraphRAG can integrate with existing enterprise data and content platforms without rip-and-replace migrations, including MarkLogic, Neo4j, GraphDB, Neptune, Databricks, Elastic, and leading cloud LLM providers.

Datavid Rover acts as the semantic and governance layer, supporting ingestion, entity enrichment, ontology alignment, access-controlled retrieval, hybrid search, RBAC enforcement, and source-level citations.

Queries are grounded in subgraphs, metadata, and source documents from your existing systems, so answers remain explainable, permission-aware, and auditable.

Absolutely.

Datavid designs GraphRAG architectures with auditability, data lineage, access control, and FAIR principles built in.

Our delivery is ISO 27001 and Cyber Essentials Plus certified, and we have a proven track record with life sciences, standards organizations, government, and scientific publishing.

GraphRAG depends on the quality of the underlying graph. If your data lacks structure, ontology, or metadata, you must build that foundation first. It also requires more upfront engineering than basic RAG, though Datavid’s accelerators reduce that effort. For high-volume, low-precision use cases, simpler retrieval may be enough.

Most enterprises see a working, explainable GraphRAG pipeline in 6–8 weeks, thanks to reusable semantic accelerators and lean, senior-led delivery teams.

Our pilots are structured to deliver business value early while ensuring long-term scalability.

Not necessarily.

Datavid can help you design or modernize your ontology and knowledge graph or enrich your existing content into a semantic layer as part of the GraphRAG engagement.