Projects that
moved the needle.

Launching AI-powered Marketing Agents required manually reviewing and categorizing large volumes of client files — a 20-hour process that created a bottleneck before any customer value was delivered.

Designed and built a custom GPT that ingests client files at scale, recommends the appropriate agent association, and generates agent-ready content summaries — including translating visual assets into structured text for faster processing.

Setup time dropped from ~20 hours to 8–10. That compression reduced internal delivery cost and cut customer lead time — accelerating time-to-value on a flagship AI product for every new deployment.

No visibility into where internal effort actually went — which meant no defensible basis for PS pricing, no data for scope conversations, and no way to identify where cost was quietly accumulating month over month.

Conducted an internal audit to define what needed to be measured and why. Designed a tracking framework from scratch, calibrated for accuracy, and built the measurement system that captured effort at enough granularity to surface real trends.

Identified hidden effort sinks growing month-over-month. Provided the data foundation that defended scope in client conversations and justified price increases that previously lacked any supporting evidence.

A major enterprise customer needed more than platform access — they needed a way to embed AI-driven insight into an active product innovation cycle across a complex, multi-vertical organization.

Oversaw full platform configuration, then designed the integration model: a custom training program with best practices, workflow guidance, and a repeatable usage structure for incorporating AI into weekly product development across 7 verticals.

60-person rollout that wasn't a one-time training — it launched an ongoing operational rhythm. Became the internal template for what enterprise AI-first customer enablement looks like at scale.