24h Claude Hackathon

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RechercheAuto

Aman Priyanshu

Supriti Vijay

Recherche-Auto, harnessed through the advanced prowess of the state-of-the-art Language Learning Model (LLM) - Claude, is revolutionizing the domain of web-based news research by streamlining the organization and encoding of vast datasets into bespoke knowledge graphs. We further streamline the process by automating knowledge aggregation using search results and preparing concise, informational summaries, significantly enhancing the efficiency and effectiveness of data acquisition and analysis. Our implementation includes Retrievable Automated Generation (RAG) systems, which bolster our platform's ability to deliver targeted information discovery, ensuring that search outcomes are precisely aligned with the user's unique informational needs.

In addition to RAGs, Recherche-Auto employs LLMs for automatic error correction, particularly for search queries that result in dead ends. This feature dynamically suggests alternative queries or modifies the existing ones to circumvent informational deadlocks, thus maintaining the momentum of research without manual intervention.

From a market research and economics perspective, Recherche-Auto is poised to automate and economize the extraction of valuable insights from web-based sources. By minimizing the time and resources needed for comprehensive market analysis, it offers businesses a competitive edge in data-driven decision-making, potentially saving billions in research expenditures while fostering a more informed, agile, and adaptive corporate strategy. We aim to revolutionize the global market research industry that generates more than $118.8 billion in annual revenue.

Recherche-Auto transforms web news research by using Claude to generate targeted queries, organizing and encoding data into custom knowledge graphs.

"RechercheAuto team on 24h Claude Hackathon Hackathon"

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