Autonomous RevOps AI Agent

The Challenge

In complex marketing organizations, weekly strategic reporting often requires hours of manual SQL extraction and spreadsheet manipulation. This creates a bottleneck, forcing RevOps and Marketing leaders to be reactive rather than proactive when optimizing budgets and identifying channel cannibalization.

The Solution

I engineered an "Always-On" intelligence layer. Instead of a simple chatbot, this system is an autonomous CLI tool that runs distinct reporting modules (Executive Briefs, Deep Dives, and Rebudgeting Scenarios). It autonomously extracts raw campaign data, calculates efficiency frontiers, and generates 5 distinct rebudgeting scenarios (e.g., "How to cut spend by 20% while maintaining pipeline").

Architecture & Design

To ensure scalability and security, the application is built on a strict, modular architecture:

• Separation of Concerns: Each module is split into four layers: queries.py (Pure SQL) → analyzer.py (Pandas data structuring) → strategist.py (LLM API Calls) → report.py (HTML Generation).
• LLM as a Reasoning Engine: Rather than feeding raw text to an AI, the system uses Python to do the heavy mathematical computation first. The LLM is then fed highly structured JSON to perform forensic analysis and prioritize actions.
• Enterprise Security: Utilizes Application Default Credentials and secure API gateways, ensuring zero data leakage.