Advancing AI Innovation Through Cross-Functional Collaboration (2)

Daman will work alongside the Public Sector Team as an Associate Software Developer.
Unlike traditional single-project placements, Daman will operate as part of a cross-functional technical cohort supporting a diverse portfolio of Government of Alberta initiatives. This flexible model allows the team to deploy the interns across multiple high-priority use cases, such as document classification, workforce optimization, or policy research, depending on immediate operational needs and the stage of the project lifecycle.
The innovation challenge centers on integrating three core technical pillars into public service workflows:
? Agentic AI: Developing autonomous workflows to handle complex decision-support tasks.
? Retrieval-Augmented Generation (RAG): Implementing intelligent search and interaction layers for large-scale government policy repositories.
? Classical Machine Learning: Building predictive models for forecasting staffing demands or service utilization.
Government of Alberta Initiatives include:
1. Wildfire Workforce Optimization: Interns will develop predictive models and agentic agents to optimize Duty Officer assignments. The goal is to reduce burnout risk and overtime costs through better decision support for wildfire leadership.
2. Research Assistant SWAT Team: Supporting the rollout of a production-ready AI product, the interns will join a dedicated team to streamline the deployment of RAG-based search and interaction layers across various GoA partners to accelerate program delivery.
3. Income Support Program Integrity: The cohort will deploy document intelligence and risk-scoring models to handle high application volumes. This initiative uses AI to rapidly flag potential fraud while fast-tracking low-risk applications to shorten decision timelines for vulnerable residents.
The anticipated economic and social benefits include significant cost savings through reduced manual administrative burdens and improved accuracy in resource planning. By modernizing information management and decision workflows, this project directly enhances the efficiency, transparency, and scalability of essential public services provided to Albertans.
Samara will work alongside the Public Sector Team as an Associate Business Solutions Consultant.
Project Description: Alberta’s Income Support program faces growing pressure as high application volumes and manual fraud reviews slow access to critical financial assistance for vulnerable residents. AltaML will partner with the GoA to deploy AI-enabled document intelligence and risk-scoring approaches, drawing on existing solution patterns to rapidly flag potential fraud while fast-tracking low-risk applications. The overall goal is to shorten decision timelines, improve program integrity, and ensure support reaches eligible Albertans faster and more efficiently.

Faculty Supervisor:

Carlos Cruz

Student:

Partner:

AltaML

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of Alberta

Program: