Product
environments that
teach AI
what people value.

Why

I came to AI research through product, growth, marketing, and creative experimentation.

For twenty years, my work has been finding cracks in systems, forming hypotheses, testing them against reality, and using behavior to make the system better.

Model training, environment design, and Reinforcement Learning feel like the same craft with the difficulty turned up.

The thing being improved is no longer just a funnel, campaign, or product surface. It is behavior.

Thesis

The future of AI will not be shaped by bigger models alone. It will be shaped by better environments, better feedback loops, better definitions of success, and better judgment about what humans actually value.

Products are not just apps. They are environments where people reveal value through edits, rejections, retries, purchases, saves, corrections, shares, and moments of trust.

Those signals become the raw material for evals, reward surfaces, and product learning loops with meaningful human value.

What we build

AI Matey builds real products as training environments for human value.

Each product is designed around a shared reward: the overlap between what helps the customer, what strengthens the business, and what gives an AI system a clearer signal about useful behavior.

The goal is not better AI in the abstract. The goal is AI that learns what useful means in the real world.

Experiments

• SmilePlease — AI portraits for likeness, trust, and identity preservation.
• Foodways — culturally grounded recipe and food-history RAG and deep research loops.
• Appree — communication rewriting without factual drift.
• GoatScreams API — subjective audio generation for timing, humor, and vibe.

WIP

• Slay the Sprint — a roguelike PM deckbuilder for RL training.
• IntroGym — a multi-agent, human+agent training environment.
• LinkedinCRO — intelligent career and aspiration signals for LinkedIn optimization.