𝐀𝐈 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 & 𝐃𝐚𝐭𝐚 𝐐𝐮𝐞𝐫𝐲 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦

I built an AI-powered research intelligence platform that helps analysts and business teams query complex datasets through a single conversational interface.

𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦

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.

𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧

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.

𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 & 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 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.

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐕𝐚𝐥𝐮𝐞

The platform helps teams analyze data faster, reduce manual SQL work, improve trust in AI outputs, and uncover insights across multiple data sources.

𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤: 𝐑𝐞𝐚𝐜𝐭 • 𝐓𝐲𝐩𝐞𝐒𝐜𝐫𝐢𝐩𝐭 • 𝐏𝐲𝐭𝐡𝐨𝐧 • 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 • 𝐑𝐀𝐆 • 𝐕𝐞𝐜𝐭𝐨𝐫 𝐒𝐞𝐚𝐫𝐜𝐡 • 𝐋𝐋𝐌 𝐀𝐠𝐞𝐧𝐭𝐬 • 𝐒𝐐𝐋 • 𝐀𝐏𝐈𝐬 • 𝐄𝐯𝐚𝐥 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬

I finally had the time to finish the prototype for an idea I had for Google Calendar. A lot of us have a weekly meeting that could have been an email. But cancelling it is more work than just showing up. You write the update, send it, then go back and cancel the meeting, and everyone gets two emails. So I added one button to the event. It turns into Calendar’s own email dialog with your agenda already in it, and sending it cancels that week’s meeting. Built with React and Motion.