diff --git a/README.md b/README.md
index 4285eaa..ab38426 100644
--- a/README.md
+++ b/README.md
@@ -30,7 +30,7 @@
 <summary><h2>📢 Recent Updates</h2></summary>
 
  **🔥 New Releases:**
-- [**PageIndex Chat**](https://chat.pageindex.ai): The first human-like document-analysis agent platform built for professional long documents. Could also be integrated via the [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart) (beta).
+- [**PageIndex Chat**](https://chat.pageindex.ai): The first human-like document-analysis agent platform built for professional long documents. It can also be integrated via the [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart) (beta).
 <!-- - [**PageIndex Chat API**](https://docs.pageindex.ai/quickstart): An API that brings PageIndex’s advanced long-document intelligence directly into your applications and workflows. -->
 <!-- - [PageIndex MCP](https://pageindex.ai/mcp): Bring PageIndex into Claude, Cursor, or any MCP-enabled agent. Chat with long PDFs in a reasoning-based, human-like way. -->
  
@@ -39,15 +39,15 @@
 <!-- - [Do We Still Need OCR?](https://pageindex.ai/blog/do-we-need-ocr): Explores how vision-based, reasoning-native RAG challenges the traditional OCR pipeline, and why the future of document AI might be *vectorless* and *vision-based*. -->
 
  **🧪 Cookbooks:**
-- [Vectorless RAG](https://docs.pageindex.ai/cookbook/vectorless-rag-pageindex): A minimal, hands-on example of reasoning-based RAG using **PageIndex** — no vectors, no chunking, and human-like retrieval.
-- [Vision-based Vectorless RAG](https://docs.pageindex.ai/cookbook/vision-rag-pageindex): Experience OCR-free document understanding through PageIndex’s visual retrieval workflow that retrieves and reasons directly over PDF page images.
+- [Vectorless RAG](https://docs.pageindex.ai/cookbook/vectorless-rag-pageindex): A minimal, hands-on example of reasoning-based RAG using PageIndex — no vectors, no chunking, and human-like retrieval.
+- [Vision-based vectorless RAG](https://docs.pageindex.ai/cookbook/vision-rag-pageindex): Experience OCR-free document understanding through PageIndex’s visual retrieval workflow that retrieves and reasons directly over PDF page images.
 </details>
 
 # 📑 Introduction to PageIndex
 
 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.
 
-Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/pageindex)** — a **_vectorless_**, **reasoning-based RAG** system that builds a *hierarchical tree index* for long documents and *reasons* over that index for *retrieval*. It simulates how **human experts** navigate and extract knowledge from complex documents through **tree search**, enabling LLMs to *think* and *reason* their way to the most relevant document sections. It performs retrieval in two steps:
+Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/pageindex)** — a **_vectorless_**, **reasoning-based RAG** system that builds a *hierarchical tree index* for long documents and *reasons* over that index for *retrieval*. It simulates how **human experts** navigate and extract knowledge from complex documents through **tree search**, enabling LLMs to *think* and *reason* their way to the most relevant document sections. PageIndex performs retrieval in two steps:
 
 1. Generate a "Table-of-Contents" **tree structure index** of documents
 2. Perform reasoning-based retrieval through **tree search**
@@ -59,7 +59,7 @@ Inspired by AlphaGo, we propose **[PageIndex](https://vectify.ai/pageindex)** 
 ### 🧩 Features 
 
 Compared to traditional *vector-based RAG*, **PageIndex** features:
-- **No Vector DB**: Uses document structure and LLM reasoning for retrieval, instead of vector search.
+- **No Vector DB**: Uses document structure and LLM reasoning for retrieval, instead of vector similarity search.
 - **No Chunking**: Documents are organized into natural sections, not artificial chunks.
 - **Human-like Retrieval**: Simulates how human experts navigate and extract knowledge from complex documents.
 - **Better Explainability and Traceability**: Retrieval is based on reasoning — traceable and interpretable, with page and section references. No more opaque, approximate vector search ("vibe retrieval").
@@ -68,7 +68,7 @@ PageIndex powers a reasoning-based RAG system that achieved [98.7% accuracy](htt
 
 ### 📍 Explore PageIndex
 
-Please see a detailed introduction of the [PageIndex framework](https://pageindex.ai/blog/pageindex-intro). Check out this GitHub repo for open-source implementations, and our [cookbook](https://docs.pageindex.ai/cookbook) and [tutorials](https://docs.pageindex.ai/tutorials) for more examples. The PageIndex service is available as a ChatGPT-style [chat platform](https://chat.pageindex.ai), or could be integrated via [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart).
+Please see a detailed introduction of the [PageIndex framework](https://pageindex.ai/blog/pageindex-intro). Check out our [GitHub repo](https://github.com/VectifyAI/PageIndex) for open-source code, and [cookbooks](https://docs.pageindex.ai/cookbook) and [tutorials](https://docs.pageindex.ai/tutorials) for additional usage guides and examples. The PageIndex service is available as a ChatGPT-style [chat platform](https://chat.pageindex.ai), or could be integrated via [MCP](https://pageindex.ai/mcp) or [API](https://docs.pageindex.ai/quickstart).
 
 ### ⚙️ Deployment Options
 - 🛠️ Self-host — run locally with this open-source repo.
@@ -76,8 +76,8 @@ Please see a detailed introduction of the [PageIndex framework](https://pageinde
 
 ### 🧪 Quick Hands-on
 
-- Try the [_**Vectorless RAG Notebook**_](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/pageindex_RAG_simple.ipynb) — a *minimal*, hands-on example of reasoning-based RAG using **PageIndex**.
-- Experiment with the [*Vision-based Vectorless RAG*](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/vision_RAG_pageindex.ipynb) — no OCR; a minimal, reasoning-native RAG pipeline that works directly over page images.
+- Try the [_**Vectorless RAG Notebook**_](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/pageindex_RAG_simple.ipynb) — a *minimal*, hands-on example of reasoning-based RAG using PageIndex.
+- Experiment with the [*Vision-based vectorless RAG*](https://github.com/VectifyAI/PageIndex/blob/main/cookbook/vision_RAG_pageindex.ipynb) — no OCR; a minimal, reasoning-native RAG pipeline that works directly over page images.
   
 <div align="center">
   <a href="https://colab.research.google.com/github/VectifyAI/PageIndex/blob/main/cookbook/pageindex_RAG_simple.ipynb" target="_blank" rel="noopener">
@@ -171,13 +171,13 @@ You can customize the processing with additional optional arguments:
 <details>
 <summary><strong>Markdown support</strong></summary>
 <br>
-We also provide a markdown support for PageIndex. You can use the `-md_path` flag to generate a tree structure for a markdown file.
+We also provide markdown support for PageIndex. You can use the `-md_path` flag to generate a tree structure for a markdown file.
 
 ```bash
 python3 run_pageindex.py --md_path /path/to/your/document.md
 ```
 
-> Notice: in this function, we use "#" to determine node heading and their levels. For example, "##" is level 2, "###" is level 3, etc. Make sure your markdown file is formatted correctly. If your Markdown file was converted from a PDF or HTML, we don’t recommend using this function, since most existing conversion tools cannot preserve the original hierarchy. Instead, use our [PageIndex OCR](https://pageindex.ai/blog/ocr), which is designed to preserve the original hierarchy, to convert the PDF to a markdown file and then use this function.
+> Note: in this function, we use "#" to determine node heading and their levels. For example, "##" is level 2, "###" is level 3, etc. Make sure your markdown file is formatted correctly. If your Markdown file was converted from a PDF or HTML, we don’t recommend using this function, since most existing conversion tools cannot preserve the original hierarchy. Instead, use our [PageIndex OCR](https://pageindex.ai/blog/ocr), which is designed to preserve the original hierarchy, to convert the PDF to a markdown file and then use this function.
 </details>
 
 ---
@@ -197,7 +197,7 @@ To address this, we introduced PageIndex OCR — the first long-context OCR mode
 
 --- -->
 
-# 📈 Case Study: Leading Finance QA Benchmark
+# 📈 Case Study: PageIndex Leads Finance QA Benchmark
 
 [Mafin 2.5](https://vectify.ai/mafin) is a reasoning-based RAG system for financial document analysis, powered by **PageIndex**. It achieved a state-of-the-art [**98.7% accuracy**](https://vectify.ai/blog/Mafin2.5) on the [FinanceBench](https://arxiv.org/abs/2311.11944) benchmark — significantly outperforming traditional vector-based RAG systems.