Aviation workflow review
Corrie Mays can support selected aviation projects with 20 years of experience as a Marine Corps aviator, Naval Flight Officer, and Blue Angel.
About Gabe
Main Character is Gabe Mays's applied AI product lab: a place to build, learn, and work on selected projects with people who care about the details.
Systems-minded builder.
I build AI products, modernize existing software, and use the work to understand how AI is changing product development.
Before technology, I served as a Captain in the United States Marine Corps with deployments to Iraq and Afghanistan. That experience shaped how I think about leadership, operations, communication, and the importance of tools that work under pressure.
After the military, I moved into technology and product leadership, eventually leading product teams at GoDaddy while building startups and side projects across SaaS, automation, developer tools, and AI systems.
Main Character is my product lab for building small, testable AI products around specific workflows. Some work starts in personal tools. Some becomes infrastructure, audit tooling, consumer software, or frontline operational software.
Building is also how I learn. Working directly with current models, coding tools, and product constraints gives me a faster feedback loop than waiting for market summaries after the fact. That signal informs what I build, which collaborations I take seriously, and my own AI investing.
Domain judgment supports the build.
Main Character is founder-led. When a project depends on specialized knowledge, I pair product and engineering work with credible review from people who understand the work.
Project-specific review
For other domains, the right reviewer should match the workflow: operators, analysts, maintainers, or experts who understand the environment and constraints.
How I think
The operating kernel
Across the work, the same product questions keep showing up: what should the model know, what should the person decide, what should the software remember, and what should happen next?
The model changes quickly. The durable work is the product layer around it: context, memory, evaluation, interfaces, and the judgment to know where AI belongs.
Build real products
Learn from current tools
Keep durable context
Evaluate model behavior
Use AI in the build
Reuse what holds up
How I build
Build first
Real product work gives better signal than watching the market from the outside.
Keep it useful
Good AI work still needs clear interfaces, durable context, and a reason for people to come back.
Stay honest
The useful lessons are often where models fail, where workflows resist automation, and where users ignore the obvious demo.
Outside work.
I am originally from California and now live on Cape Cod, Massachusetts, with my wife of 18 years and our two children, Maverick and Samantha.
Have a concrete AI problem worth building around?
Send the product, workflow, or modernization problem with enough context for Gabe to tell whether there is a serious build.