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README.md

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-title: README
-emoji: 🏃
+title: Duohub
+emoji: ⚡
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 colorTo: purple
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-Edit this `README.md` markdown file to author your organization card.
+# Duohub - Ultra-fast Graph RAG for Voice AI
+
+Duohub provides blazing fast graph RAG services specifically designed for voice AI and other low-latency applications, delivering context in under 50ms.
+
+## Key Features
+
+- **Graph & Vector RAG**: Choose between semantic similarity search with reranking or deep query resolution with graph traversals
+- **Custom Ontologies**: Pre-trained ontology models for different domains, with options for custom ontologies
+- **Advanced Processing**: Built-in coreference resolution, fact extraction, and entity resolution
+- **Global Scale**: Data replicated across multiple regions for consistent low-latency performance
+- **Simple Integration**: Start querying your knowledge base with just three lines of code
+
+## Quick Start
+
+```python
+from duohub import Duohub
+client = Duohub(api_key="your_api_key")
+response = client.query(query="Your question here", memoryID="your_memory_id")
+
+
+## Why Duohub? ⭐
+
+- 🚄 **Lightning-Fast**: Delivers query responses in under 50ms, making it ideal for real-time voice AI applications
+- 🎯 **High Precision**: Graph-based memory ensures accurate and contextually relevant responses
+- 🔌 **Easy Integration**: Get started with just 3 lines of code - no complex setup or infrastructure needed
+- 🌍 **Global Ready**: Data replicated across 3 locations by default for consistent low-latency performance
+- 🎛️ **Flexible Options**: Choose between vector or graph RAG based on your needs
+- 🛠️ **Built-in Processing**: Includes coreference resolution, fact extraction, and entity resolution out of the box
+- 🏢 **Enterprise Grade**: Supports on-premise deployment, custom ontologies, and dedicated support