Go Completely Offline: Build a Privacy-First Personal Finance Assistant with LiteRT and Gemma 4

Imagine having a personal financial planner that analyzes your sensitive transaction history, flags spending anomalies, and suggests budget optimizations—all while running 100% offline, directly inside your web browser.

In this guide, we will build an offline Retrieval-Augmented Generation (RAG) assistant powered by the LiteRT LM Web API and the highly efficient, on-device Gemma 4 E2B small language model. By caching the model weights directly in the browser's Cache Storage, your application will achieve complete privacy, zero token costs, and genuine offline capability.

1. Prerequisites

LiteRT LM Web API

Gemma 4 E2B model