The MTR Operator's Field Guide to Building an AI-Powered Business

Six blind spots that quietly break a growing MTR operation — before an operator ever sees them coming.

This free guide will help you:

See exactly where your operation sits on the AI maturity curve — and what's actually above you.
Understand the difference between GenAI and agentic AI, and why it changes what you should expect from any tool you use.
Run the 60-second diagnostic operators use to score their own operation across leads, availability, follow-up, and reporting.
Spot the six places MTR operations quietly break — before growth exposes them for you.

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What Operators Have To Say About Us...

"It's basically just organizing your day and it's operating while you're sleeping. I love that."

— Conference attendee, MTR operator

"We had six VAs when we started. We have one now."

— MTR operator, conference attendee

"You guys are way out ahead of everybody, it sounds like."

— Conference attendee, co-host operator

About the founders

JR Pagdanganan is the co-founder and CEO of KeyFlex AI — ex-Google, a chemistry degree, and a background in UX research. He builds AI the way a scientist runs an experiment: isolate variables, test hypotheses, validate with data. That rigor is what shaped KeyFlex AI's modular agent architecture — the reason the system stays fast, accurate, and cheap to run, instead of dumping everything into one massive prompt.

KeyFlex AI is built on top of two years of real mid-term rental operating data — every lead, every lease, every insurance placement — because you can't design a system for a business you don't understand.

"We had the vision and we executed. We didn't learn AI from a course. We built it — on our own business, with our own data."

Camille Lim Yan is co-founder and COO of KeyFlex AI — a former Senior TPM at Amazon Prime Video Sports who went on to lead GenAI initiatives at T-Mobile, building 15+ custom GPTs that added more than $20M in enterprise value. She's also held product and operations roles at Leafly and Boeing, and holds an MBA from the University of Washington.

At Amazon, Camille managed engineering teams — writing precise requirements, breaking complex systems into pieces, and shipping reliably at scale. That's the same skill set behind building and orchestrating a network of AI agents. The engineers and the AI work for her.

"Neither of us codes. We don't need to. The engineers and the AI work for us."

See Where Your Operation Actually Stands.