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-# 📄 PageIndex
+# [📄 PageIndex](https://pageindex.ai)
 
 Are you frustrated with vector database retrieval accuracy for long professional documents? Traditional vector-based RAG relies on semantic *similarity* rather than true *relevance*. But **similarity ≠ relevance** — what we truly need in retrieval is **relevance**, and that requires **reasoning**. When working with professional documents that demand domain expertise and multi-step reasoning, similarity search often falls short.
 
-🧠 **Reasoning-based RAG** offers a better alternative: enabling LLMs to *think* and *reason* their way to the most relevant document sections. Inspired by AlphaGo, we use *tree search* to perform structured document retrieval. 
+🧠 **[Reasoning-based RAG](https://pageindex.ai)** offers a better alternative: enabling LLMs to *think* and *reason* their way to the most relevant document sections. Inspired by AlphaGo, we use *tree search* to perform structured document retrieval. 
 
 **[PageIndex](https://vectify.ai/pageindex)** is a *document indexing system* that builds *search tree structures* from long documents, making them ready for reasoning-based RAG.  It has been used to develop a RAG system that achieved 98.7% accuracy on [FinanceBench](https://vectify.ai/blog/Mafin2.5), demonstrating state-of-the-art performance in document analysis.
 
-Self-host it with this open-source repo, or try our ☁️ [Cloud service](https://dash.pageindex.ai/) - no setup required.
+#### 🚀 Deployment Options  
+- 🛠️ Self-host: run it yourself from this open-source repo
+- **[☁️ Cloud service](https://dash.pageindex.ai/)**: try instantly with our [🖥️ Dashboard](https://dash.pageindex.ai/) or [🔌 API](https://docs.vectify.ai/quickstart) *(no setup required)*  
 
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