---
title: "GreyMatter — Dark Matter Scientific Discovery & Debate Synthesizer"
description: "GreyMatter is a RAG-powered scientific discovery platform built for the Claude Solv-A-Thon hackathon. It transforms decades of dark matter research literature into living debates: retrieving, comparing, and transparently arguing across experiments with citations and uncertainty quantification. Using hybrid retrieval (dense embeddings + BM25), Claude-powered debate synthesis, and interactive consensus graphs, it makes cutting-edge astrophysics research accessible to both experts and the public."
url: "https://meghrajjare.in/projects/greymatter/"
---

# GreyMatter

> Dark Matter Scientific Discovery & Debate Synthesizer

GreyMatter is a RAG-powered scientific discovery platform built for the Claude Solv-A-Thon hackathon. It transforms decades of dark matter research literature into living debates: retrieving, comparing, and transparently arguing across experiments with citations and uncertainty quantification. Using hybrid retrieval (dense embeddings + BM25), Claude-powered debate synthesis, and interactive consensus graphs, it makes cutting-edge astrophysics research accessible to both experts and the public.

- **Year:** 2025
- **Live demo:** [https://claudesolvathon.vercel.app](https://claudesolvathon.vercel.app)
- **Source code:** [https://github.com/Namit24/greymatter](https://github.com/Namit24/greymatter)
- **Tech stack:** Next.js 15, TypeScript, Tailwind CSS, Anthropic Claude AI, Google Gemini AI, Pinecone, LangChain, Prisma ORM, PostgreSQL, Better Auth, Vercel AI SDK, Groq, React Graph Vis, shadcn/ui, Framer Motion

## Highlights

- Hybrid RAG retrieval combining dense embeddings with BM25 keyword search, optional cross-encoder reranking, and MMR diversification to avoid duplicate results
- Claude-powered debate synthesis generating expert summaries with methods and caveats, layperson analogies, embedded glossaries, and argument maps (supports/refutes/uncertain)
- Interactive consensus and contradiction graph visualization using React Graph Vis, mapping support, conflict, and uncertainty relationships across research papers
- Uncertainty quantification scoring each claim on source weight (peer-reviewed > preprint), recency, agreement count, method quality, and statistical strength
- Voice-powered research interface with text-to-speech synthesis, enabling hands-free exploration of dark matter literature and debate maps