Research and Implementation of LLM based Autonomous Agent Based on IoT Big Data Environment

Geotab will provide the foundational platform and infrastructure, data, and expertise for the project. This includes access to their IoT big data environment on Google Cloud Platform, containing telematics data from 4.5M+ connected vehicles globally. They will offer mentorship and support through their AI Platform team, share historical data and documentation for training the large language model agent, and enable the intern to utilize various tools and resources to conduct analysis and construct solutions.
Geotab possesses a wealth of telematics data gathered from over 4.5 million devices globally, which presents untapped potential for AI-driven improvements in safety, sustainability, and operational efficiency for its customers. However, manual identification of critical events—such as safety incidents, customer dissatisfaction signals, or computational inefficiencies—is time-consuming, reactive, and prone to delays. The reliance on human intervention for analysis compromises real-time responsiveness and scalability, with suboptimal resource usage and consumption.
This project addresses the challenge of developing an autonomous Large Language Model (LLM) agent to provide actionable insights to both Geotab developers and customers, enabling rapid responses and proactive problem-solving.
The project solution enhances Geotab’s product quality, improves driver safety, reduces computation cost, and strengthens customer relationships.

Faculty Supervisor:

Shurui Zhou

Student:

Partner:

Geotab Inc

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

University:

University of Toronto

Program: