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ā¢ Apr 14
Most businesses lose customers because they reply too late.
AI chatbots can solve this by replying instantly and working 24/7.
š This is exactly what Iām building right now.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
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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.
ā¢ 17h
Can this system work outside Tableau? I think a client of mine would appreciate this
AI Data Assistant Inside Power BI
I built an AI-powered chatbot directly inside Power BI to help users interact with their existing dashboards and data through natural-language questions.
Instead of manually opening multiple dashboards to find a specific metric, users can ask the assistant a business question and receive a data-grounded answer along with the relevant report/dashboard source.
The assistant works across multiple Power BI dashboards and can provide context from the available reports, making it easier to find insights without changing the existing BI environment.
Key capabilities:
- Ask questions directly inside Power BI
- Connect with existing Power BI data
- Search across dashboards and reports
- Get relevant metrics and insights
- Identify the source report/dashboard
- Answer data-driven business questions
- Work within existing BI infrastructure
- Add a conversational layer to Power BI
AI Assistant Using Your Business Knowledge Base ā 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 ā by meaning, not keywords.
Any format as-is: PDF, DOCX, TXT, Markdown ā 100+ pages is fine
Answers grounded in YOUR documents ā it says "I don't have that information" rather than inventing
Source references ā every answer shows which document and section it came from
Runs on your infrastructure ā documents never leave your control
One command to re-index after updating documents ā documented, no programmer needed
LIVE DEMO
Ask it about my products, pricing, deployment and support ā 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.