AI-Assisted Information Retrieval for Network Operations Centres

This project aims to make it easier for people to access valuable information stored in complex company databases—without needing to know technical coding languages like SQL. Today, many employees and customers can’t easily search these databases because doing so requires specialized skills, which creates delays and extra training costs. Our research will explore ways to use advanced artificial intelligence, specifically Large Language Models (LLMs), to let users simply type questions in everyday language and receive accurate answers directly from the database. We will focus on improving both the accuracy and reliability of this “Text-to-SQL” technology so that it works consistently in real-world settings. For the partner organization, this means faster access to information, more efficient workflows, and the ability for a wider range of staff and customers to make data-driven decisions. In the long run, this project can help the company save time, lower costs, and create tools that can benefit many other industries facing similar challenges.

Faculty Supervisor:

Shervin Shirmohammadi

Student:

Partner:

Ciena Corporation (Ottawa, ON)

Discipline:

Engineering

Sector:

Information and cultural industries; Manufacturing; Professional, scientific and technical services

University:

University of Ottawa

Program: