diff --git a/modules/ROOT/content-nav.adoc b/modules/ROOT/content-nav.adoc
index 0db201c1c..618680abb 100644
--- a/modules/ROOT/content-nav.adoc
+++ b/modules/ROOT/content-nav.adoc
@@ -100,6 +100,8 @@ Generic Start
 
 * xref:billing.adoc[Billing]
 
+* xref:graphrag.adoc[GraphRAG with Aura]
+
 * Connecting applications
 ** xref:connecting-applications/overview.adoc[Drivers and libraries]
 ////
diff --git a/modules/ROOT/pages/graphrag.adoc b/modules/ROOT/pages/graphrag.adoc
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+++ b/modules/ROOT/pages/graphrag.adoc
@@ -0,0 +1,67 @@
+[[aura]]
+= graphRAG
+:description: RAG with aura
+
+. Graph RAG Concept: Uses a graph database to structure and retrieve relevant information for LLMs, enhancing retrieval-augmented generation (RAG).
+. Graph Representation for Time & Space: Graphs naturally capture relationships, making them ideal for modeling events, locations, and sequences.
+. Neo4j & RAG: Graph databases like Neo4j simplify data retrieval by storing relationships directly, avoiding expensive joins.
+. Data Ingestion & Indexing: Extracting and structuring data efficiently (e.g., from articles) improves search and retrieval performance.
+. Hybrid Search: Combines keyword and semantic search to improve accuracy and handle misspellings or variations in queries.
+. Summarization & Distillation: Reduces noise so the LLM can focus on high-level reasoning, minimizing hallucinations.
+. Agentic Approaches: LLMs can dynamically select tools (e.g., Cypher queries) to improve decision-making and retrieval accuracy.
+. Prompt Engineering: Structured prompt templates ensure consistency and better responses from the LLM.
+. Knowledge Graphs & Entity Linking: Using triples (subject-predicate-object) helps structure data effectively for retrieval and reasoning.
+. Emil’s Demo: Showcased how Neo4j can automatically create a graph from articles, demonstrating knowledge extraction and structuring.
+
+== Introduction
+
+* Aura is simplifying Graph RAG – Making it easier to use.
+* A wrapper for the code → The user interacts through the UI instead of coding everything to do with GraphRAG Agents themselves.
+* “You know Cypher → Now you can create tools → Now you can create questions.”
+
+Graph RAG answers complex questions but is hard to implement today.
+Reduces the need for deep Cypher and technical knowledge.
+Many want to build agents, but it’s not trivial.
+In Aura:
+A low-code/no-code experience.
+Grounded in a knowledge graph (ensuring reliable answers).
+
+== Neo4j Agent Overview
+
+An Agent consist of
+
+* Choice – The agent makes decisions.
+* Tools – Functions the agent can use.
+* Flow Router – Determines how tasks are handled.
+* Components – Different parts that make up the agent system.
+
+== Goal of the Agent
+
+* Answer complex questions and take actions.
+* Grounded in knowledge: Answers are based on a knowledge graph.
+
+== How the UI Works
+
+* Users can specify functions.
+* The agent has tools/functions it can use (e.g., Cypher queries).
+* The LLM determines access to these tools:
+* The Agent selects 1 or 2 tools to answer the question.
+* Chooses the best Gemini models for the approach.
+
+Example: Movie Agent
+
+Movie Graph DB in Aura – Create a tool like "Find Movies by Actor."
+
+Input: Actor name → Queries: ACTOR ACTS IN / ACTOR IN MOVIE.
+
+Example: "Which movies does Keanu Reeves act in?" → The agent selects the relevant tools and queries the data.
+
+== Publishing the agent:
+Developers send questions to endpoints.
+
+== Industry-specific use cases:
+
+* Security
+* Supply Chain Graph
+* Customer 360
+* How can I answer tough questions?
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