Case Study: Scaling Volunteers
The Problem
Mouse was expanding from 300 to 3,000 students and needed to manage a rapidly growing volunteer base while maintaining quality and saving time.
Adam Davis, CEO of Mouse, and his team built AI workflows to categorize volunteers and match them to classrooms using an evolving prompt library.
The Solution
Mismatch rates dropped, the team grew confident with AI, and Mouse was ready to expand programming at scale.
The Result
Adam and the Mouse Story
Mouse had a good problem: their AI Design League was working, students were engaged, and the mission was resonating. But what started as a pilot for 300 students suddenly needed to serve 3,000. Volunteer support, once managing a group of 15, now required coordination across more than 300 people.
This kind of scale doesn’t just strain systems, it tests them. Matching professionals to classrooms became a puzzle with too many pieces and too little time and that’s where Adam Davis, CEO of Mouse, stepped in.
Under Adam’s leadership, Mouse didn’t just bolt on a tech fix, they built a flexible, AI-enhanced system that could adapt where AI categorized volunteers by expertise, matched them with class needs, and even learned to account for real-world constraints like scheduling and availability.
The result was an organizational shift where staff learned how to prompt and adapt AI tools. Confidence replaced hesitation and Mouse moved from surviving growth to designing for it.
Building this system enabled Mouse to give the team better tools that keep the mission human and the operations humane.