An AI agent that investigates real business questions using a real SQLite database and a real MCP server — evidence-grounded reports, not chatbot guesses. Week 8 of a 90-day AI Engineering roadmap.
An AI agent that investigates a real business question against a real SQLite database — through a real MCP server — and writes an evidence-grounded report instead of a chatbot guess. Eighth deliverable of a 90-day AI Engineering roadmap (Phase 1: Foundation, Week 8).
- Overview - Features - Demo - Installation - How to Run - Example Question - Architecture - Why a Real Database + Real MCP Server - Folder Structure - Future Improvements - Roadmap Context - Author - License
AI Business Agent (UI codename Ledger) takes a plain-English business question — "Why did our revenue decrease this month?" — and actually investigates it: classifies the question, plans which business data it needs, queries a real SQLite database (optionally through a real MCP server), computes grounded findings in plain Python, optionally gathers external research, and writes a structured report. If the data doesn't support a conclusion, it says so instead of guessing.
This is Repo 8 of 10+ in a structured 90-day AI Engineering roadmap.
- 🧭 Question Classification — maps a business question to a category (sales, customers, operations, finance, inventory, marketing, general) and the data it needs - 🗃️ Real SQLite Business Database — customers, products, orders, sales tables, generated from realistic synthetic data (Pakistani regions: Punjab, Sindh, KPK, Balochistan) - 🔌 Real MCP Server — mcpserver.py exposes 7 business-data tools over the actual Model Context Protocol (official mcp SDK), connectable from Claude Desktop, Claude Code, or any MCP client — not just this app - 🧠 Deterministic Planning — pl
From the project README.
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