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

Imagine a personal financial assistant that runs completely offline, analyzing your spending patterns and generating budget recommendations—all within your web browser. In this article, we'll create an offline, Retrieval-Augmented Generation (RAG) assistant using LiteRT's LM Web API and the lightweight Gemma 4 on-device language model. By caching the model weights in the browser's Cache Storage, this application ensures complete privacy, eliminates token costs, and delivers genuine offline functionality.

To build this innovative tool, you'll need the following prerequisites:

- LiteRT LM Web API

- Gemma 4 E2B model

- Angular 22

- TailwindCSS

- Node 24

These components form the foundation of our privacy-first personal finance assistant. The Angular and TailwindCSS frameworks handle the user interface and styling, while LiteRT LM Web API and the on-device model power the AI capabilities. Utilize npm to install the necessary dependencies, including @litert-lm/core, TailwindCSS, PostCSS, JSON repair, Dexie for IndexedDB storage, and development tools like angular-eslint, husky, lint-staged, serve, and commitlint.

The application's architecture focuses on processing transactions entirely on-device to maintain financial data privacy within the browser. It operates in three phases: model and data storage, arithmetic grounding, and conversation management. During model and data storage, the app downloads and caches the Gemma 4 E2B model weights in the browser's Cache Storage for offline access. Users can log expenses, which are then securely stored in IndexedDB.

To mitigate hallucinations in small language models, the assistant aggregates expenses into monthly totals, daily totals, and expense-by-category breakdowns. These precomputed values are supplied to the Gemma WebGPU engine as ground truth alongside user queries. Conversation management prevents exceeding browser token limits by resetting the conversation after three turns while retaining a client-side memory of the last two queries. This ensures conversational continuity while keeping memory usage within on-device limits.

The article also covers source code organization, including an IndexedDB database for tracking expenses offline and the AI integration process. By caching the Gemma 4 weights in Cache Storage, the application can run 100% offline, offering total privacy and eliminating token costs. Download the 2GB Gemma 4 E2B model once, and the assistant will function entirely offline.