LLM Business Intelligence System
Ask business questions in plain language and get answers grounded in your own data — not a generic chatbot.
Overview
The LLM Business Intelligence System is an engineering project exploring how large language models can sit on top of real business data — sales figures, operational metrics, structured records — and answer questions in natural language, instead of requiring users to build reports or write queries themselves.
The problem
Business intelligence tools are powerful but require someone who knows how to build the right dashboard or query. Most day-to-day questions — how last week compared to the week before, which product line is underperforming — still route through a person who knows the tooling, creating a bottleneck.
Our approach
The system translates a natural-language question into a structured query against a business's own data sources, then uses the language model to turn the structured result back into a clear, grounded answer, with the underlying data available for verification rather than hidden behind the model's response. Access controls determine which data a given user's questions are allowed to touch.
Key capabilities
What the system actually does.
Natural-Language Querying
Business users ask questions in plain language instead of building queries or dashboards.
Grounded Answers
Responses are generated from real structured data, not the model's own unverified assumptions.
Query Transparency
The underlying data and logic behind an answer stays visible and checkable.
Role-Based Data Access
Access controls determine what data a user's questions are allowed to touch.
How it works
System workflow.
Natural-Language Question
Query Translation
Structured Data Retrieval
Grounded Answer Generation
Transparent Result
Natural-Language Question
Query Translation
Structured Data Retrieval
Grounded Answer Generation
Transparent Result
Technology
Built with purpose-chosen tools.
System experience
A user types a question the way they'd ask a colleague — no query syntax, no dashboard to build — and gets back a direct answer alongside the data it was built from, so the answer can be checked rather than taken on faith.
Why it matters
Putting business data behind a natural-language interface removes the bottleneck of routing every question through whoever knows the reporting tool, without hiding the underlying numbers behind an opaque answer.