I've been fascinated by AI and systems logic for a long time. My background is rooted in mathematics and data analytics, but my path into AI and automation actually started somewhere pretty unexpected: I used to own a dog training business.
Training a dog means breaking complex behavior down into small, modular pieces. You pair simple signals, layer steps on top of each other, and set clear boundaries around reinforcement so the outcome is predictable. Designing autonomous AI agents works almost exactly the same way. You're teaching a system to handle complex tasks by chaining together modular skills and setting strict guardrails around what it's allowed to do.
Professionally, I'm driven by what I call productive efficiency. If I catch myself doing a manual, repetitive task two or three times, my instinct is to figure out how to automate it, whether that means writing APEX code, configuring an integration pipeline, or just building the tool myself. When something in my own life didn't work the way I needed it to, I didn't wait around for someone else to fix it. I re-engineered it and built my own app instead.
That instinct is the throughline in my career: taking broken, noisy data environments, from ERPs to Salesforce CPQ workflows to agentic chatbots that cut support volume and saved $1M a year, and turning them into clean, reliable systems people can actually trust.