Problem
Modern students, researchers, analysts, and builders can get an AI answer in seconds, but it is difficult to tell what is actually supported by evidence. Long documents, conflicting sources, and generated claims make it easy to lose the original context or accept an answer that sounds confident but cannot be checked.
Solution
ProofPath is an evidence-first AI workspace for asking a question, collecting source material, and producing an answer whose important claims stay connected to the evidence behind them.
Instead of treating the model's response as the final authority, ProofPath separates generation from verification:
- The user asks a question or uploads source material.
- The system retrieves relevant passages and organizes them by source.
- AI drafts an answer from the retrieved evidence.
- Each important claim is linked back to the supporting passage.
- A verification layer flags unsupported or conflicting claims before the user accepts the result.
- The user can inspect the evidence trail and export the final result.
What makes it different
Most AI research tools optimize for getting an answer quickly. ProofPath makes provability part of the product experience.
The central interaction is not just "ask AI"; it is:
Claim → Evidence → Verification → Answer
The system is designed so that uncertainty is visible instead of being hidden behind fluent text.
Key Features
- Evidence-grounded AI answers
- Claim-to-source traceability
- Source and passage viewer
- Unsupported-claim detection
- Conflict/uncertainty indicators
- Research workspace for multiple sources
- Structured final answer with an evidence trail
- Exportable research record
AI Use
AI is used for retrieval-assisted synthesis, claim extraction, semantic comparison, and natural-language explanations. The verification layer is deliberately separated from free-form generation so that the product can challenge an answer instead of blindly repeating it.
Target Users
- Students researching assignments
- Developers and builders evaluating technical information
- Analysts comparing sources
- Researchers working with long documents
- Anyone who wants an AI answer they can inspect rather than simply trust
Technical Direction
ProofPath will be implemented as a full-stack web application with a modern React/Next.js interface, an API/backend layer, a document and evidence store, and an AI retrieval/synthesis pipeline. The architecture is designed so individual evidence records remain inspectable throughout the workflow.
Hackathon Build
The OFFGRID MVP focuses on one memorable workflow: ask a question, generate an answer, and open any important claim to see the evidence supporting it.
The project is being built as a functional prototype during the OFFGRID build period, with the initial version prioritizing a strong end-to-end verification experience over a large number of loosely connected features.
Future Direction
Future versions could add browser research, collaborative workspaces, citation quality scoring, domain-specific verification, reusable research graphs, and integrations with existing knowledge-management tools.
Built With
- next.js
- node.js
- openai
- postgresql
- rag
- react
- tailwind-css
- typescript
- vector-search
