Typical building directories are commonly seen on signages where people walk pass by it barely noticing. RamNav is an advance, responsive, and complete directory which enables people to locate a specific room in the building through speech recognition and give the information they need. The directory system caters speech recognition feature used for performing query and output the room details for queries such as: floor level, nearby room names, maplike image, and a predefined QR code that the user can scan using their phones to show a copy of query result in their mobile devices. The main tools used by the researchers for this project are the Microsoft Speech Services for the Speech-to-Text API, Python programming using PyCharm IDE, and Tkinter as the Python GUI library. RamNav has been exceptional in acquiring and processing the speech of the user even on different environment situations and met all the proponents desired output. It attained an accuracy rate of 98.12% overall for its speech recognition features. Meanwhile, a keyword recognition rate of 97.05% on 518 total keywords for the reliability objective.