// what it does

Fully On-Device RAG

Complete RAG pipeline running locally — no cloud, no API calls, no data leaves the device.

Gemma Small Language Model

Google's Gemma SLM optimized for mobile inference with low memory footprint.

ObjectBox Vector Storage

High-performance embedded vector database for on-device similarity search.

Zero Network Dependency

Works in airplane mode, offline environments, and restricted networks.

Document Upload & Processing

Upload PDFs and text files, automatically chunked and embedded on-device.

// how it gets used

01

Upload and Query

Open app

Document processed and embedded on-device

Get answer with source references — all offline

01

Open app

02

Upload PDF or text document

03

Document processed and embedded on-device

04

Ask a question in natural language

05

Get answer with source references — all offline

Upload PDF or text document

Ask a question in natural language

🧠

Phoenix

Ready when you are

Hey! I'm Phoenix — I know Titas's work, projects, and experience. Ask me anything — from distributed systems to production RAG, or what it's like building at Tesco and VMware.