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The AI-Powered Modern Data Platform for E-Commerce is a cutting-edge solution that bridges the gap between complex data infrastructure and business users. This project combines a robust modern data stack with advanced AI capabilities, enabling natural language analytics over an enterprise-grade E-Commerce Data Warehouse. By leveraging Artificial Intelligence, Large Language Models, and Retrieval-Augmented Generation (RAG), this platform empowers business users to ask questions in natural language and receive actionable insights.
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Really nice concept and clean app design. The marketplace and community-focused experience look very thoughtfully put together. Great work! 🚀
Feed The Flock — community platform for trading homegrown/homemade goods and sustainable-living education.
AI Assistant Using Your Business Knowledge Base — RAG on Your Documents
THE PROBLEM
Q&A bots break down when knowledge lives in documents: a 100-page manual has no "questions" to match, it can't fit into a prompt, and generic chatbots hallucinate instead of admitting what they don't know.
THE SOLUTION
A RAG (Retrieval-Augmented Generation) knowledge base: documents are split into meaningful chunks, embedded into a vector index, and the assistant answers from the right sections — by meaning, not keywords.
Any format as-is: PDF, DOCX, TXT, Markdown — 100+ pages is fine
Answers grounded in YOUR documents — it says "I don't have that information" rather than inventing
Source references — every answer shows which document and section it came from
Runs on your infrastructure — documents never leave your control
One command to re-index after updating documents — documented, no programmer needed
LIVE DEMO
Ask it about my products, pricing, deployment and support — it answers from 6 real documents with sources:
https://kb-assistant-demo.dvc-ai.workers.dev
The 'I don't have that information' line is the part most RAG builds skip, and it's the one that matters. How do you set the cutoff? On mine, a fixed similarity threshold broke once I filtered results by user role. Scores shifted and it refused questions it could answer.
AI Data Assistant Inside Tableau
I built an AI-powered chatbot for Tableau that allows users to interact with their existing dashboards and data through natural-language questions.
Instead of manually navigating through multiple Tableau workbooks to find a specific metric or insight, users can ask the assistant what they need and receive relevant information from the available Tableau data and dashboards.
The solution adds a conversational AI layer directly to the existing BI workflow, helping users explore their data and find the right dashboard or report without changing how their Tableau environment is structured.
Can this system work outside Tableau? I think a client of mine would appreciate this