ProofPilot AI — Real-Time AI Reliability Lab Inspiration
AI-generated answers can sound convincing even when they contain unsupported or incorrect claims. For students, developers, researchers, and businesses, trusting an answer without checking its evidence can lead to poor decisions.
I built ProofPilot AI to make AI reliability easier to inspect. Instead of treating an AI-generated response as automatically trustworthy, the project breaks it into individual claims and compares them against provided evidence.
What it does
ProofPilot AI is a web-based AI reliability analysis tool that helps users examine AI-generated answers and understand where their claims stand.
Users can submit a question, an AI-generated answer, and supporting evidence as text or accessible URLs. The application analyzes the claims and classifies them as:
Supported: The available evidence supports the claim. Contradicted: The available evidence conflicts with the claim. Insufficient Evidence: The available evidence is not enough to determine whether the claim is supported.
The application also provides reliability metrics, analysis history, and downloadable reports to make results easier to review.
How I built it
I used Python and Flask for the backend, HTML, CSS, and JavaScript for the interactive frontend, and SQLite for storing analysis history. Gunicorn serves the application, and Render hosts the live demonstration.
The architecture is designed to run without a paid AI API or GPU. It uses evidence matching and claim-analysis logic, with explicit verdicts and limitations rather than pretending that every result is guaranteed to be correct.
Challenges and lessons learned
One of the main challenges was designing a useful reliability workflow without presenting heuristic evidence matching as definitive fact-checking. Another was connecting the analysis results to meaningful history and metrics while keeping the application lightweight and deployable on free hosting.
This project reinforced an important lesson: AI reliability is not just about producing an answer. It is also about examining evidence, communicating uncertainty, and making the verification process transparent.
What's next
Future improvements include stronger semantic claim verification, independent source corroboration, benchmark-based evaluation, and more comprehensive explanations of conflicting evidence.
Vision
ProofPilot AI aims to make AI-generated information easier to question, inspect, and evaluate—one claim at a time.
Live demo: https://proofpilot-ai-gve8.onrender.com
Source code: https://github.com/Sreevalli20/devpost
Log in or sign up for Devpost to join the conversation.