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๐๐ ๐๐๐ฌ๐๐๐ซ๐๐ก ๐๐ง๐ญ๐๐ฅ๐ฅ๐ข๐ ๐๐ง๐๐ & ๐๐๐ญ๐ ๐๐ฎ๐๐ซ๐ฒ ๐๐ฅ๐๐ญ๐๐จ๐ซ๐ฆ
I built an AI-powered research intelligence platform that helps analysts and business teams query complex datasets through a single conversational interface.
๐๐ก๐ ๐๐ซ๐จ๐๐ฅ๐๐ฆ
Research teams often work across disconnected databases, reports, spreadsheets, dashboards, and documents. This slows analysis, makes cross-source searching difficult, and can reduce trust in AI-generated answers.
๐๐ก๐ ๐๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง
The platform combines RAG, AI agents, natural-language-to-SQL, structured and unstructured retrieval, source attribution, confidence scoring, and evaluation pipelines.
Users can ask a business question in natural language, and the system decides whether to search documents, query databases, run SQL, or combine multiple sources before returning an evidence-backed answer.
๐๐๐๐ฎ๐ซ๐๐๐ฒ & ๐๐ซ๐จ๐๐ฎ๐๐ญ ๐๐ฑ๐ฉ๐๐ซ๐ข๐๐ง๐๐
The system evaluates answer correctness, retrieval quality, SQL accuracy, source relevance, hallucination risk, and response performance. Low-confidence results are captured for review and continuous improvement.
The frontend is built with React + TypeScript and includes AI chat, dataset exploration, source references, analytics, evaluation dashboards, and real-time updates.
๐๐ฎ๐ฌ๐ข๐ง๐๐ฌ๐ฌ ๐๐๐ฅ๐ฎ๐
The platform helps teams analyze data faster, reduce manual SQL work, improve trust in AI outputs, and uncover insights across multiple data sources.
๐๐๐๐ก ๐๐ญ๐๐๐ค: ๐๐๐๐๐ญ โข ๐๐ฒ๐ฉ๐๐๐๐ซ๐ข๐ฉ๐ญ โข ๐๐ฒ๐ญ๐ก๐จ๐ง โข ๐๐จ๐ฌ๐ญ๐ ๐ซ๐๐๐๐ โข ๐๐๐ โข ๐๐๐๐ญ๐จ๐ซ ๐๐๐๐ซ๐๐ก โข ๐๐๐ ๐๐ ๐๐ง๐ญ๐ฌ โข ๐๐๐ โข ๐๐๐๐ฌ โข ๐๐ฏ๐๐ฅ ๐๐ข๐ฉ๐๐ฅ๐ข๐ง๐๐ฌ
โข Oct 14
Impressive work. Bringing RAG, natural-language-to-SQL, source attribution, confidence scoring, and evaluation into one research platform solves a very real problem for data-heavy teams. The focus on accuracy and evidence-backed answers makes this especially valuable.
The network for creativity
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