Knowledge graph foundation
We design and build (or connect to) the knowledge graph GraphRAG retrieves over, because the graph is what makes it work.
GraphRAG Development
GraphRAG Development
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
Give AI answers that follow real relationships. Noseberry builds GraphRAG: retrieval-augmented generation grounded in a knowledge graph, so your AI can reason over connected facts, answer multi-step questions and cite grounded sources. More accurate and explainable than plain vector RAG, with evaluation and human review built in.
Book a free GraphRAG sessionDefinition
What is GraphRAG development?
GraphRAG development is the build of retrieval-augmented generation that retrieves over a knowledge graph rather than only a store of text chunks. Because the graph captures entities and their relationships, retrieval can traverse connections and assemble grounded, connected context, so the AI reasons over related facts, answers multi-step questions, and cites its sources. It needs a knowledge graph underneath, so we build the two together, and we add evaluation, guardrails and human review so accuracy is measured and consequential answers are checked.
Key takeaways
- GraphRAG is retrieval-augmented generation grounded in a knowledge graph, not just a pile of text chunks.
- It lets AI follow relationships and reason over connected facts, which plain vector RAG struggles to do.
- It improves accuracy and explainability for questions that span many entities and multi-step reasoning.
- It needs a knowledge graph underneath, so the two are built together, with human review of answers.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
Scope
What GraphRAG development includes
The graph, the retrieval, the grounded generation, and the evaluation that proves it works.
Graph-aware retrieval
We retrieve connected subgraphs and relationships relevant to a question, not just similar text passages.
Hybrid retrieval
We combine graph traversal with vector and keyword retrieval, so you get both relationships and semantic recall.
Grounded generation
We feed the retrieved graph context to the model so answers are grounded, traceable and cite their sources.
Evaluation and guardrails
We build evaluation sets and guardrails so accuracy is measured, and a person reviews answers where it matters.
Integration and serving
We expose GraphRAG through APIs into your product or assistant, with monitoring for quality and cost.
Who this is for
Built for teams who need AI answers to be right and explainable
Product and AI teams whose users ask connected, multi-step questions and who need grounded, traceable answers, not a plausible-sounding guess.
Signs you need it
- Your RAG answers are shallow or miss connections across documents and entities.
- Users ask multi-step questions that span many related facts.
- You need answers that are explainable and can cite grounded sources.
- You already have or plan a knowledge graph and want AI to use it.
- Accuracy and traceability matter more than a quick demo.
How we work
Graph, retrieve, generate, evaluate
1
Ground in a graph
We build or connect the knowledge graph GraphRAG will retrieve over.
2
Design retrieval
We combine graph traversal with vector and keyword retrieval for the use case.
3
Wire generation
We feed graph context to the model for grounded, cited answers.
4
Evaluate
We measure accuracy with evaluation sets and add guardrails and human review.
5
Serve and monitor
We integrate it into your product and monitor quality and cost.
Why Noseberry
Why choose Noseberry for GraphRAG
Graph plus retrieval, together
We build the knowledge graph and the retrieval layer as one system, not bolted together.
Accuracy you can trace
Answers grounded in connected facts, with citations and evaluation, not a confident guess.
Human-in-the-loop
For consequential answers, a person reviews. AI supports the decision; it does not replace judgment.
Proven at scale
2M+ lives touched, 15+ Fortune 500 clients, 250+ solutions across 20+ countries.
Frequently Asked Questions
GraphRAG is retrieval-augmented generation that retrieves over a knowledge graph, so the AI can follow relationships and reason across connected facts, rather than only matching similar text chunks. It grounds answers in structured, connected context.
Standard RAG retrieves text passages by similarity, which can miss how facts relate. GraphRAG adds a knowledge graph, so retrieval can traverse relationships and assemble connected context, improving accuracy and explainability for multi-entity, multi-step questions.
Yes. GraphRAG retrieves over a knowledge graph, so a graph is required. We build it as part of the engagement, or connect to one you already have.
It reduces it by grounding answers in structured, connected context and enabling citations, but no approach removes it entirely. We add evaluation, guardrails and human review so accuracy is measured and consequential answers are checked.
When questions span many related entities, need multi-step reasoning, or must be explainable and traceable. For simple lookups, plain RAG may be enough, and we will tell you honestly.
Yes. We expose it through APIs into your assistant or application, with monitoring for quality and cost, and human-in-the-loop where decisions matter.
Make AI answers accurate and explainable
Book a free session and we will design GraphRAG for your use case.
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