I build AI systems that think like teams, act like operators.

I build agentic AI systems — software where autonomous agents read through messy human information, reason about it, and carry the work all the way to action. I come at it from an unusual angle: I study how people actually decide — reward, motivation, bias, at the neural level — and design systems with those realities built in. Builder first; the science is the lens I build through.

My main system, NovaCRM, is a live AI-native CRM where six specialized agents turn scattered email and Slack into a structured pipeline — each agent with one bounded, verifiable job, and a person kept in the loop for the moments that matter. It grew out of Executive Mind Matrix, where three agents with competing cognitive biases argue a decision before routing it. Around them I’ve built a knowledge system that self-heals before it pages me, a model that scores video by predicted brain response (success criteria committed to git before the results), and a behavioral-finance tool that catches institutional rotation in covariance before it shows up in price.

The thread through all of it: the bottleneck is rarely the model — it’s the system around it, and the people it’s for. I care about the gap between good thinking and executed action, because I’ve watched capable people drown in operational overhead while their best ideas never ship. So I’m putting real agentic systems into production and learning in the open — headed toward an early-stage team where building the system and understanding the people it serves are the same job.