Hippo
The Memory That Never Forgets
Hippo is a local-first file organizer that indexes your files and enables natural language search. All processing happens on your device using vector embeddings and semantic search. The platform can index 100K+ files and search them instantly. During our design phase (September-December 2025), we designed the local-first architecture, planned vector embedding strategy with quantized models, created incremental indexing system, and built prototype tests showing 50K files indexed in 8 minutes with <100ms search latency.
How Hippo Works
Hippo indexes your files locally, creates vector embeddings for semantic search, processes everything on your device, and optionally syncs encrypted across devices.
Install Hippo
Download and install Hippo on your device (Windows, macOS, Linux). Native app built with Tauri for performance and privacy.
Choose Folders
Select folders to index. Hippo scans files, extracts metadata, and creates searchable index. All processing happens on your device.
Background Indexing
Hippo indexes files in the background. Incremental updates only process new/changed files. Index 100K files in ~10 minutes.
Semantic Search
Search using natural language: "privacy policy from 2024" or "photos from vacation". Vector embeddings understand meaning, not just keywords.
AI-Powered Features
AI analyzes files, suggests tags, generates summaries, and answers questions about your files. All AI processing is local using Ollama.
Sync Across Devices
Optionally sync your index across devices using encrypted cloud backup. All data encrypted end-to-end. You control the keys.
Use Cases
Personal File Organization
Organize personal files, photos, documents. Find anything instantly with natural language search. Never lose a file again.
Development Projects
Index code repositories, documentation, and project files. Semantic search finds code by meaning, not just text matching.
Research & Documentation
Organize research papers, notes, and documentation. AI-powered summaries and tagging help you find relevant information quickly.
Team Collaboration
Shared workspaces for teams. Collaborative tagging, shared indexes, and team-wide search. Perfect for organizations.
Key Features
100K+ File Capacity
Index up to 100K files with minimal storage overhead. Incremental indexing only processes new/changed files. Efficient storage.
Semantic Vector Search
Search by meaning, not just keywords. Vector embeddings understand context and relationships. Natural language queries.
Local AI Processing
AI features run locally using Ollama. File analysis, tagging, summaries, and Q&A—all processed on your device for privacy.
70+ File Types
Support for images, videos, audio, code, documents, archives, and more. Automatic metadata extraction and content indexing.
Auto-Tagging
AI automatically suggests tags based on file content, location, and metadata. Organize files without manual work.
Version Control
Track file changes over time. See history, restore previous versions, and understand file evolution.
Cross-Device Sync
Sync your index across devices using encrypted cloud backup. Optional feature—all data encrypted end-to-end.
RAG-Powered Q&A
Ask questions about your files. "What did I write about privacy last month?" AI answers using your indexed files.
Pricing Plans
Free Tier
- Basic file indexing (up to 10K files)
- Simple search
- Basic tagging
- Local AI (limited queries)
- +1 more features
Pro Subscription
- All free features
- Unlimited files
- Advanced AI features
- Semantic search
- +4 more features
Team Subscription
- All Pro features
- Shared workspaces
- Team collaboration
- Admin controls
- +3 more features
Enterprise
- All Team features
- On-premise deployment
- SSO integration
- Custom AI models
- +3 more features
Built with AI
Hippo was developed through human-AI collaboration. Core components are open source (MIT licensed).
Open Source Core
Core engines and libraries are MIT licensed. Audit, contribute, or self-host.
Premium Features
Enterprise features, premium UI, and dedicated support available commercially.
Self-Hosted
Deploy on your infrastructure with Docker or Kubernetes. Full control, full privacy.
Development Story
Incubation Timeline
Design & Prototype (September-December 2025)
Key Achievements
- Designed local-first architecture (SQLite + vector DBs)
- Planned vector embedding strategy (quantized models)
- Created incremental indexing system
- Designed encrypted cross-device sync (E2EE)
- Prototype tested: 50K files in 8 minutes
- Achieved <100ms search latency target
Technical Architecture
Local-First Architecture
File Indexing Engine
Rust-based incremental indexing, SQLite for metadata storage, file system watching (inotify/fsevents), change detection algorithms.
Vector Search System
Local Qdrant instance for embeddings, quantized models for efficiency, semantic similarity search, <100ms query latency.
AI Processing (Ollama)
Local LLM integration for tagging, summarization, and Q&A. Models run entirely on-device. No cloud AI calls.
Tauri Desktop App
Tauri 2.0 framework for native performance, Rust backend, web frontend (React/Svelte), small bundle size (~10MB), cross-platform.
Encrypted Sync
Optional E2EE cloud backup, user-controlled encryption keys, incremental sync, conflict resolution, peer-to-peer sync (future).
File Type Support
70+ file types: documents (PDF, DOC, MD), images (JPEG, PNG, GIF), videos (MP4, MOV), code (70+ languages), audio (MP3, WAV).
RAG System
Retrieval-Augmented Generation for document Q&A, context-aware answers, citation support, conversational AI interface.
Performance
Index 50K files in 8 minutes, <100ms search latency, minimal memory footprint, background processing, incremental updates.