TrustBase

Inspiration

In today’s digital world, users routinely agree to privacy policies, terms of service agreements, and cookie disclosures without fully understanding what they are accepting. These documents are often long, complex, and written in dense legal language that discourages careful reading. We created TrustBase to address this transparency gap. Our goal is to make online policies more accessible and understandable, empowering users to quickly see how their data is collected, stored, shared, and protected.

What It Does

TrustBase aggregates and analyzes information from privacy policies, terms of service documents, cookie notices, and even related news stories. Instead of forcing users to read thousands of words of legal text, the platform extracts key insights and presents them in a structured, easy-to-read format. This allows users to quickly understand critical details such as data collection practices, third-party data sharing, tracking technologies, and user rights.

How We Built It

We built TrustBase using Python for backend processing and Flask as the web framework to handle routing and API integration. The frontend was developed with HTML, CSS, and JavaScript to create a clean and interactive interface. For AI-powered text analysis and summarization, we integrated Google Gemini (1.5–2.0 Flash models). Our system follows a structured pipeline: first, we collect or scrape policy text; next, we use regular expressions to extract relevant sections; then we convert the extracted content into structured JSON based on a defined schema; and finally, we render the structured data into formatted HTML for display. This regex-to-JSON-to-HTML workflow allows us to transform complex legal documents into organized summaries.

Challenges We Ran Into

One of the biggest challenges was managing AI output formatting. Gemini responses sometimes mixed HTML and JSON, which made parsing and validation difficult. We also encountered API key configuration and authentication issues during integration. Handling large privacy policy documents presented token limit and performance challenges, requiring optimization and chunking strategies. Additionally, maintaining consistent JSON schema outputs required careful prompt engineering and validation logic to ensure reliable structure.

Accomplishments We’re Proud Of

We are proud of successfully completing the project end-to-end and integrating Google Gemini into a functional, real-world application. Building a reliable regex-to-JSON-to-HTML transformation pipeline was a major technical milestone. We also strengthened our understanding of backend and frontend integration, structured data processing, and API debugging. Most importantly, we turned a complex idea into a working product.

What We Learned

Throughout this project, we learned how to design and validate a JSON schema, engineer prompts for structured AI outputs, and build a dependable data transformation pipeline. We gained hands-on experience debugging API integrations and managing large text inputs efficiently. The process also deepened our understanding of data structuring, error handling, and full-stack application development.

What’s Next for TrustBase

Our next step is to develop a Chrome extension that allows users to instantly analyze privacy policies directly from their browser. We also plan to improve AI output consistency, introduce a privacy transparency scoring system, enhance the user interface with clearer data visualizations, and expand coverage to support more websites and platforms.