Why I Built VaultDocs AI: Local-First Contract Risk Screening with Open Models

Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle 💸

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

VaultDocs AI is a local-first, privacy-guaranteed contract risk screening assistant tailored for freelancers, contractors, and boutique agency owners.

Freelancers frequently sign Master Services Agreements (MSAs), Statements of Work (SOWs), and Non-Disclosure Agreements (NDAs) loaded with unfavorable clauses—such as unlimited indemnification, intellectual property handovers, oppressive payment terms (Net 90+), and unilateral termination rights. Hiring a legal professional for every contract review is cost-prohibitive for independent workers. However, uploading confidential client contracts to commercial cloud LLMs (like standard online AI chat tools) directly violates client non-disclosure agreements (NDAs) and risks exposing sensitive commercial data to server logs and third-party AI training pipelines.

VaultDocs AI solves this by running 100% on the user's local machine with zero cloud connectivity. No contract text, vector embeddings, or prompts ever leave local memory.

Parses PDF contracts into page-boundary chunks.

Uses a fast in-memory hybrid ranker (BM25 + legal keyword boost) and dense FAISS vector index to retrieve risk-prone sections in under 5 milliseconds.

Performs local open-weight LLM screening using llama3.2:1b, gemma:2b, or qwen2.5:3b via Ollama.

Validates model citations directly against extracted PDF text to eliminate hallucinations.

Renders a professional, interactive 3-column legal review workstation with customizable risk scoring (0–100) and downloadable Markdown audit reports.

YouTube Walkthrough: https://youtu.be/pede-5xhahY

The complete project is open-source and up on GitHub:

https://github.com/RiyaDhami13/vaultdocs-ai

I wanted to build something practical that relies entirely on open-source AI tools running locally: