What it does
ProofPath turns scattered screenshots, messages, receipts, and documents into a clear, source-linked evidence story.
Users can start with a small evidence packet or upload their own evidence. ProofPath uses multimodal AI to extract events and claims, build a chronological timeline, connect each event to its source, distinguish Supported, Uncertain, and Missing evidence, identify contradictions, highlight missing proof, and generate a readable evidence report.
The core workflow is:
Evidence → Timeline → Evidence Status → Missing Proof → Clear Story
Who it's for
ProofPath is designed for people who need to organize evidence around an everyday dispute, such as an online purchase or service issue, and want a clearer view of what their uploaded evidence actually supports.
What makes it different
ProofPath does not simply summarize uploaded files. It keeps the analysis grounded in the submitted sources and makes uncertainty visible. A Supported event must have an explicit source and extracted supporting detail; ambiguous evidence is marked Uncertain; and claims without sufficient evidence are identified as Missing. Contradictions must also point back to the relevant submitted sources.
Important boundary
ProofPath is an evidence-organization and analysis tool. It does not make legal decisions, determine guilt, or provide legal advice. Human review is still needed to verify the AI's interpretation against the original evidence.
What I learned
Building ProofPath taught me how much planning improves AI-assisted development. I used the Devpost Learn Skill Pack to move from scope to PRD to technical specification before building, then implemented a small end-to-end workflow with structured AI output, source validation, and recovery states for unreadable files and AI/API failures.
Technology
ProofPath uses a lightweight HTML/CSS/JavaScript frontend, a Node.js server, and Gemini multimodal AI with structured JSON output. It uses no database for the proof of concept.
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