Bring accuracy, explainability, and trust to enterprise AI

Datavid’s GraphRAG services connect large language models (LLMs) with your enterprise knowledge graph - delivering AI that’s explainable, factual, and grounded in your own data.

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GRAPHRAG

Challenges we solve

Hallucinations and unverifiable AI outputs
LLMs that fail on domain-specific or regulatory queries
Fragmented content and disconnected knowledge bases
 
Prototypes that can’t scale into secure, governed AI-systems

Our approach  

We combine knowledge graphs, vector retrieval, and generative AI into a single, explainable workflow - powered by Datavid Rover.
Our approach ensures that AI doesn’t just generate answers. It shows exactly where those answers came from.

Graph-grounded retrieval: enrich LLM prompts with entities, relationships, and metadata from your graph
Context orchestration: dynamically feed the LLM only verified, relevant data
Multi-model integration: OpenAI, Anthropic, Bedrock, or your private LLM
Agentic workflows: automate reasoning, summarization, and validation steps
Semantic enrichment: transform unstructured content into connected knowledge
Explainable AI: trace every answer back to its factual origin
Federated governance: ISO 27001-aligned security and auditability
Rapid delivery: production-ready results in 6-8 weeks via lean, expert-led teams

The GraphRAG reference architecture

Our GraphRAG architecture integrates your existing data and document systems into a semantic knowledge graph that acts as a factual backbone for AI.

By linking your knowledge graph to an LLM through Datavid Rover, every model output becomes traceable, contextual, and verifiable - reducing hallucinations while enhancing insight depth.
 

Ready to build a trustworthy AI foundation?
Explore how GraphRAG powers explainable AI across industries.

Datavid -GraphRAG Architecture

Our GraphRAG capabilities

GRAPHRag

Knowledge & semantic foundation

Ontologies & taxonomies for your industry
KGs design & build (Neo4j, RDF, MarkLogic)
Entity, fact, and relationships enrichment

GraphRAG pipeline engineering

Hybrid retrieval with embeddings AND graphs
Dynamic prompt orchestration with semantics
Rover citation, provenance, & LLM integration

Governance, compliance & explainability

Fully traceable, citation-backed AI outputs
Policy-led access & metadata governance
FAIR semantics with drift, bias, & quality checks

Insights, analytics & agentic workflows

Conversational assistants & semantic search
Summarization, exploration, & reasoning
Rover insight dashboards & monitoring

GraphRAG in action

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Life Sciences

Roche - Graph-grounded clinical knowledge platform enabling explainable AI for research and regulatory teams.

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Agriscience

Syngenta - Cognitive search and discovery integrating 50M+ R&D documents powered by GraphRAG pipelines. 

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Publishing

ACS - LLM- based semantic assistant delivering factual answers from 33M+ scientific articles.

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Standards & compliance

BSI - AI-driven compliance insight engine with traceable, ontology-based reasoning.
 

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Why Datavid?

 
Datavid is one of the few global partners capable of delivering true GraphRAG - where semantic precision, knowledge graphs, and enterprise-grade AI engineering come together in a single, governed architecture.
 
Our teams combine deep semantic expertise with proven delivery frameworks to turn your complex knowledge environment into a trustworthy, explainable intelligence.
 
What makes Datavid different?
 

Deep expertise

in knowledge graphs, ontologies, and semantic enrichment

Proven accelerators through Datavid Rover

for faster, production-ready GraphRAG delivery
 

ISO 27001 & Cyber Essential Plus certified delivery

for secure, regulated deployments
 

Seamless integration

across Graph databases, data lakes and LLM and Agentic AI providers

Trusted by global leaders

in Life sciences, Baking & Finance, Public sector, Publishing, Agriscience, and Standards
 

130+ experienced data and AI professionals

with strong domain and semantic backgrounds
 

How Datavid compares

Feature

Traditional Service Providers

 Datavid

GraphRAG architecture

 

half tick

Vector-first RAG with optional graph add-ons

 

Frame 184-1

Native GraphRAG: entity-centric knowledge graph + ontology-driven reasoning + LLM grounding

Retrieval depth

 

close 1

Single-hop semantic similarity on document chunks

 

Frame 184-1

Multi-hop graph traversal (entities, relationships, constraints) + supporting evidence

Explainability & provenance

 

close 1

Document-level citations or none

 

Frame 184-1

Claim-level citations, entity lineage, relationship paths, and source documents

Semantic & ontology engineering

 

half tick

Minimal metadata tagging or schema-on-read

 

Frame 184-1

First-class capability: domain ontologies, taxonomies, controlled vocabularies, semantic alignment

Enterprise platform integration

 

half tick

Partial integration, often requiring data duplication

 

Frame 184-1

In-place integration across Neo4j, MarkLogic, Databricks, Elastic, and cloud LLMs

FAIR & metadata governance

 

half tick

Not explicitly supported

 

Frame 184-1

Designed-in: metadata harmonisation, persistent identifiers, reuse by design

Access control & auditability

 

half tick

Application-level controls only

 

Federated RBAC, permission-aware retrieval, full audit trails

Security & compliance delivery

 

half tick

Security posture varies by project

 

Delivered under ISO 27001 & Cyber Essentials Plus

Agentic workflows

 

Experimental agents without validation layers

 

Governed agents for reasoning, validation, orchestration, and rejection

Time to enterprise value

 

half tick

3–6 month PoCs with limited production readiness

 

6–8 weeks using reusable accelerators and reference architectures

Regulated-industry experience

 

General-purpose, consumer-oriented focus

 

Proven delivery in Pharma, Life sciences, Standards & Regulated Publishing

 

Datavid’s GraphRAG framework turned our generative AI prototypes into an enterprise-grade intelligence layer. Every insight is now sourced, verifiable, and audit ready.

Head of Data Strategy, Global Pharmaceutical Client

Your Questions. Answered.

What is GraphRAG?

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.

How is GraphRAG different from traditional RAG?

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.

Datavid specializes in building the semantic foundation that makes this possible.

Can GraphRAG integrate with our existing data and content platforms?

Yes. GraphRAG is designed to integrate in-place with existing enterprise data and content platforms, without requiring rip-and-replace migrations.

We routinely integrate GraphRAG with platforms such as MarkLogic, Neo4j, GraphDB, Neptune, Databricks, and Elastic, as well as all leading cloud LLM providers.

Datavid Rover acts as the semantic and governance layer, providing reusable ingestion, entity enrichment, ontology alignment, and access-controlled retrieval. GraphRAG queries are grounded using subgraphs, metadata, and source documents drawn directly from your existing systems, ensuring answers are explainable, permission-aware, and auditable.

This approach accelerates GraphRAG deployment while preserving your current data architecture and governance investments.

Integration supports hybrid retrieval (graph + vector + keyword), RBAC enforcement at query time, and source-level citation for every generated response.

Is GraphRAG suitable for regulated and high-compliance environments?

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.

How long does a GraphRAG project take with Datavid?

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.

Do we need a knowledge graph before starting?

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.

Ready to build your graph-grounded AI fabric?

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