Otis Elevator

AI Framework to Automate Reverse Engineering of the Service Interface of Embedded Elevator System

This project presents an AI-powered framework for the autonomous reverse engineering of proprietary Otis elevator service interface protocols, extensible to competing platforms. Using a stimulus-response methodology on an elevator simulator, the framework monitors serial communications, maps state transitions with protocol messages, and generates structured documentation by clustering diagnostic symbols into their signal state groups. Through a structured data collection process, engineers can feed elevator data into the system, eliminating manual protocol decoding. The framework enables querying of the elevator controller for specific signal states, bypassing the diagnostic tool entirely.