An LLM-powered analyst tool that ingests business data and surfaces insights, summaries, and strategic recommendations in plain language — no SQL required.
Demo — AI Business Analyst in action
Business data is only useful if decision-makers can access it. Most people can't write SQL or read a Jupyter notebook — so insights stay locked up with data teams.
Built an LLM-powered layer on top of business data that lets anyone ask questions in plain English and get back structured, accurate, actionable answers.
A tool that democratizes data analysis — making business intelligence accessible to non-technical stakeholders without sacrificing accuracy.
The AI Business Analyst bridges the gap between raw business data and the people who need to act on it. Using a retrieval-augmented generation (RAG) architecture, it grounds LLM responses in real business data — so answers are accurate, not hallucinated.
Users can ask questions like "What was our best performing product last quarter?" or "Which customer segment has the highest churn rate?" and get back a clear, well-reasoned answer with the supporting data.
Add more detail about your implementation here — what data sources it connects to, what LLM you used, how you handled grounding and accuracy.
RAG pipeline — Business data is embedded and stored in a vector database; queries retrieve relevant context before LLM generation.
LLM integration — Add your model here (GPT-4, Claude, Llama, etc.) with a system prompt tuned for business analysis.
Structured output — Responses are formatted with a summary, supporting data, and a recommended action.