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README.md

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+# EmbedNeural
+
+*On-device multimodal embedding model enabling instant, private NPU-powered visual search.*
+
+---
+
+## Model Description
+
+**EmbedNeural** is the world’s first multimodal embedding model purpose-built for **Qualcomm Hexagon NPU** devices. It enables **instant, private, battery-efficient** natural-language image search directly on laptops, phones, XR, and edge devices — with no cloud and no uploads.
+
+The model continuously indexes local images using NPU acceleration, turning unorganized photo folders into a fully searchable visual database that runs entirely on-device.
+
+---
+
+## Key Features
+
+### ⚡ NPU-accelerated multimodal embeddings  
+Optimized for Qualcomm NPUs to deliver sub-second search and dramatically lower power consumption.
+
+### 🔍 Natural-language visual search  
+Query thousands of images instantly using everyday language (e.g., “green bedroom aesthetic”, “cat wearing sunglasses”).
+
+### 🔒 100% local and private  
+All computation stays on-device. No cloud. No upload. No tracking. 
+
+### 🔋 Ultra-low power  
+Continuous background indexing uses ~10× less power than CPU/GPU methods, enabling true always-on search.
+
+---
+
+## Why It Matters
+
+People save thousands of images — memes, screenshots, design inspo, photos — but struggle to find them when needed. Cloud solutions compromise privacy; CPU/GPU search drains battery. 
+
+EmbedNeural removes these tradeoffs by combining:  
+- **Instant retrieval** (~0.03s across thousands of images)  
+- **Continuous local indexing**  
+- **Zero data upload**  
+- **NPU-optimized efficiency for daily use**  
+
+This makes visual search something you can actually use **every day**, not just when plugged in. 
+
+---
+
+## Use Cases
+
+- **Personal image libraries:** Rediscover memes, screenshots, and old photos instantly.  
+- **Creative workflows:** Search moodboards and visual references with natural language.  
+- **Edge & embedded systems:** Efficient multimodal search for mobile, XR, IoT, and automotive.
+
+---
+
+## Performance Highlights
+
+- Sub-second search even across large image libraries
+- ~10× lower power consumption vs CPU/GPU search
+- Stable always-on indexing without thermal or battery issues