GraphRAG introduces a new trust layer for generative AI in the Graphwise platform, making enterprise answers explainable, auditable, and context-rich.
For many organizations, the biggest barrier to generative AI isn’t the model, it’s trust. Teams struggle to justify AI-driven decisions when they can’t see where an answer came from, how it was produced, or whether it respects internal policies and regulations.
With GraphRAG, we’re introducing the intelligence layer of the Graphwise platform: a governed, graph-powered retrieval-augmented generation engine designed specifically for enterprises that need explainability, compliance, and scale.
GraphRAG combines Large Language Models (LLMs) with a Semantic Layer built on knowledge graphs to deliver context-rich, verifiable, and audit-ready answers. It’s where your institutional knowledge, graph data, and AI workflows meet.
What is GraphRAG?
GraphRAG is the core GenAI engine in the Graphwise platform. It goes beyond traditional vector-only RAG by unifying:
- Semantic reasoning over your knowledge graph
- Hybrid retrieval across graph, vector, and full-text search
- Multi-hop question answering for complex, relational queries
Where standard RAG stops at similarity search, GraphRAG understands entities, relationships, and semantics. It transforms fragmented documents, taxonomies, and domain models into actionable answers that can be traced, explained, and audited.
Our vision is simple:
To be the enterprise-standard engine for trusted generative AI, turning complex institutional knowledge into reliable, multi-hop answers for mission-critical decisions.
Why enterprises need a trusted GenAI layer
Most enterprise AI teams face the same challenges:
- Opaque pipelines — It is hard to see how an answer was produced or which sources were used
- Hallucinations and inconsistency — Models improvise when context is missing or ambiguous
- Regulatory pressure — This applies especially in finance, healthcare, ESG, and the public sector
- Siloed knowledge — Structured and unstructured data live in different systems with no shared semantic layer
GraphRAG addresses these challenges by design:
- Transparent retrieval and reasoning — Each step of the pipeline can be inspected
- Source-level provenance — Answers are backed by explicit documents and graph entities
- Semantic grounding — The knowledge graph acts as a source of truth, reducing hallucinations
- Regulatory-friendly traceability — It is built for environments where you must justify why an answer is correct, not just what it is
What makes GraphRAG different
GraphRAG builds on years of experience with GraphDB, PoolParty, and semantic technologies in regulated industries. At its core, the product is shaped around three pillars.
Trust and explainability
GraphRAG turns the traditional “black-box” RAG pipeline into a transparent, auditable system:
- Explainable answers — Users can see what was retrieved and why it was used
- Source citations and provenance — Answers are linked back to specific documents and graph entities
- “Explain this answer” views — This breaks down how the system interpreted the question, expanded concepts, and selected context
- Regulatory-ready traceability — Retrieval, reasoning, and guardrails can be inspected for audits and reviews
This is essential for teams working under strict oversight, where AI output must withstand internal and external scrutiny.
Hybrid retrieval and knowledge-graph grounding
GraphRAG isn’t tied to a single retrieval method. It combines:
- Graph-based retrieval via the knowledge graph (GraphDB and ontologies)
- Vector search in your chosen vector store
- Full-text search (FTS) for keyword-driven discovery
On top of that, GraphRAG uses knowledge-model-driven input processing to understand user intent. This means that it:
- Detects and enriches concepts from your taxonomy/ontology
- Expands queries with related entities and terms
- Builds a graph representation of the question to resolve ambiguity
This makes a big difference for real-world questions that are short, vague, or rely heavily on domain-specific language.
Because the system retrieves from a graph of entities and relationships — not just isolated chunks — GraphRAG is particularly strong on multi-hop questions (“how does X impact Y across Z?”) and complex context.
Enterprise flexibility and vendor-agnostic design
GraphRAG is engineered to fit into existing enterprise stacks instead of locking you into one vendor:
- Any major LLM — OpenAI, Claude, Azure AI, Amazon Bedrock, or your own hosted models
- Any vector store — OpenSearch, Elastic, and other enterprise-grade vector backends
- Any enterprise IdP — Integration with standard identity providers via OAuth2/OIDC
Embedding models and vector stores are abstracted behind clear interfaces, so you can switch providers, update models, or scale infrastructure without rewriting your application.
GraphRAG core capabilities
Version 1.0 focuses on delivering a solid foundation for trusted conversational experiences over your knowledge graph and content.
- Secure, authenticated access
- Keycloak-based authentication and authorization
- Separation of users, services, and GraphRAG pipelines
- Conversational experience with short-term memory
- Multi-turn chat with context preserved within each conversation
- AI-generated follow-up questions to help users go deeper
- Concept descriptions based on your SKOS-style taxonomies
- Explainability and provenance out of the box
- A dedicated explainability panel that shows what tools were called and what they returned
- Document panel with source URL, title, and description
- Basic provenance listing which sources contributed to the answer
- Hybrid retrieval foundation
- Integration with GraphDB and external vector stores like Elastic/OpenSearch
- Support for combining structured graph context with unstructured documents
- Enterprise-grade guardrails
- Input and output guardrailing integrated in the workflow
- Safety and policy checks wired directly into the agent orchestration
- Modern APIs and streaming
- Synchronous and asynchronous querying
- Server-sent events (SSE) for streaming answers, explainability messages, sources, follow-ups, concepts, and guardrail signals
In short: GraphRAG v1 gives you a governed, explainable conversational layer over your knowledge graph and content, ready to be embedded into applications, portals, and internal tools.
Tested on real-world, high-stakes scenarios
To shape GraphRAG, we evaluated the product on a diverse set of demanding scenarios, including:
- Financial regulation and monetary policy — Policy documents, regulatory texts, interconnected financial concepts
- Healthcare and clinical knowledge — Clinical pathways, medical guidelines, interactions, and care protocols
- International development and ESG — Project documents, sustainability reports, regulatory frameworks, and impact narratives
- Semantic technology and internal knowledge hubs — Deeply structured ontologies and knowledge graphs connected to technical documentation
These use cases push the system across multiple dimensions: precision, reasoning depth, traceability, and robustness in domain-specific language. They’re exactly the environments where “just another chatbot” isn’t enough.
Getting started with GraphRAG
GraphRAG is available as a core component of the Graphwise platform and integrates natively with:
- GraphDB for semantic graph storage and reasoning
- PoolParty and your knowledge models (taxonomies, ontologies)
- Your existing vector infrastructure and LLM providers
If you’re building or scaling enterprise AI initiatives and need answers that are explainable, compliant, and grounded in your knowledge graph, GraphRAG is designed for you.
We’d be happy to walk you through the first release, discuss your data landscape, and explore how GraphRAG can become the trust layer for your generative AI applications.
