Systems that had to survive contact with production.

A category team had a working prototype that derived useful signals from user clickstream data, and no way to trust it unattended. The engagement was the distance between those two states: making the pipeline dependable, defining which signals actually influenced a buying decision and discarding the rest, and giving the team a path from an idea to something running in front of users without a rebuild each time.

The measurable result was a shorter path from idea to launch. The durable result was that the system kept working after we left.

A state government needed to verify application documents at a volume that manual review could not absorb. We implemented partially automated verification using OCR and learning-based models — partially being the operative word. The system does the work leading up to a decision and routes the ambiguous cases to a person, because the cost of a confident wrong answer on a citizen's application is not symmetric with the cost of a slower one.

Verification time fell by roughly two thirds. The design constraint that mattered more was that a human stayed accountable for every rejection.

A static intake form was doing a poor job of capturing the context a trip designer actually needs. We replaced it with a conversational lead capture agent that gathers trip context naturally, turns it into a qualified lead, and brings in a human at the right moment.

The broader workflow layer joined lead handling, follow-ups, and internal operations coordination — systems that each worked independently but not together. Most of the engineering was at the seams: retries, handoff state, and making every transition clear to the person receiving it.

Built a workflow automation stack for GTM teams whose outbound activity had become repetitive but could not safely become generic. The system brought account context, buyer signals, and conversation history into the work of finding, qualifying, and following up with prospects.

Rather than automate the appearance of outreach, it made the next conversation more informed: which accounts deserved attention, what had already been said, and when a human should take over. That gave teams a more consistent operating rhythm and a clearer path from outbound activity to conversion.

Engaged with a large global enterprise's Asia, Middle East, and Africa Agentic AI program as principal advisor, running six concurrent production workstreams across pricing, competitive intelligence, media, sales, finance, and brand operations.

The work was not a single assistant with a broad remit. Each workflow had to fit its own source systems, decision rights, and review conditions while still becoming part of a coherent regional program.

A five-agent creative pipeline — router, product, creative, campaign, and publisher — had accumulated an 800-line router prompt, three-to-four-second latency, and state failures between agents. We refactored it to one root agent with modular skills.

Product, creative, campaign, and publishing capabilities became skills discovered and activated on demand: a new capability is now a SKILL.md file, not another agent boundary to coordinate. The result was a simpler system that ships more reliably.

Built an AI system that classifies ad performance, predicts performance six and twenty-four hours ahead, and automatically pauses underperforming ads while scaling winners. Objective-aware KPI stacks — ROAS and profit for sales, CPL for leads, CTR/CPC for traffic, and CPM/reach for awareness — drive classification at every stage.

The automation maps those predictions to discrete actions instead of a generic score: create a variation and launch, scale a winner, flag an uncertain case for review, or pause a loser before spend is wasted.