Project Brief: Automating Vulnerability Insights for Better Public Outcomes
Public sector teams face mounting pressure to identify and support vulnerable individuals in real time—often with limited data, siloed systems, and stretched resources. Traditional approaches rely heavily on manual review and reactive contact strategies, which delay critical help for those who need it most.
In response, we worked with a major public sector body to design and deploy a data-AI solution that automatically surfaces indicators of vulnerability, enabling proactive engagement and intelligent triage. Our approach transformed frontline service delivery, improving reach, equity, and efficiency across key population groups.
Project Description
AI-Powered Vulnerability Detection and Customer Contact Automation
This project aimed to modernize how a public sector organization identifies and responds to individuals at risk—across housing, social care, health, and financial services. The challenge: too many missed opportunities for early intervention due to fragmented data and manual case identification.
We built and deployed a machine learning-driven insight engine that analyses customer records, service interactions, and contextual indicators to detect vulnerability signals—such as changes in behaviour, payment patterns, or service disengagement. These insights are prioritized and routed to frontline teams through tailored dashboards and automated contact triggers.
Our solution not only improved the speed and accuracy of identifying at-risk individuals—it also enabled personalized, timely outreach at scale. By aligning AI intelligence with human judgment, the organization significantly reduced response times, improved service uptake, and ensured support reached those who needed it most, when they needed it most.