Builder.Media

Introduction

Shipping at the speed of AI

Four weeks doesn't leave room for the usual handoff between design, dev, and analytics—so instead of treating them as separate phases, AI was integral to building these foundations from the ground up with each other in mind.

Figma AI was crucial for rapid ideation, moving from concept to hi-fi prototype within days. Claude became a standing partner across the entire pipeline, from interrogating funnel friction in week 1 to deploying production code in week 3. Gemini helped solve misaligned tags, improper UTM attribution across sites, and diagnosed non-compliance within Google Search Console.

With a hi-fidelity mockup approved for deployment by the end of week 1, design stopped being the bottleneck, and execution became the next constraint to solve for.

Building in production

Integrating with a legacy WordPress environment

That speed only matters if it can effectively merge with an actual codebase.

Building backend functionality, CSS styling, and JavaScript interactions within WordPress, Claude was fundamental to accelerating implementation. Rather than starting each conversation from scratch, I created a persistent project tailored to our dev environment, including skills, requirements, guidelines, and objectives to reduce hallucination.

This was the core driver that hit deployment by week 4.

Measuring the outcome

GA4, UTM attribution, and custom event tracking

Shipping fast meant nothing if the team couldn't see the impact.

While tracking had technically existed across these properties for years, misaligned tags had left months of data uncollected, and the team had lost visibility into the exact metrics they sell advertisers.

Rebuilding GA4 tracking with proper UTM attribution, going the extra step to integrate with their CRM provided rich context into user behavior. It was no longer just where traffic came from, but which subscribers remained active across properties, unlocking signal for the team, rather than just direct engagement.

Post-mission debrief

Measuring outcomes

In summary, this project fundamentally changed how AI fit into my process.

While the speed gains were valuable, the bigger shift came from the comprehensive look at design, implementation, and measurement that AI-native workflows position you to work from. I started asking—how will this affect the core product, how long will it take to build, and what will we be able to measure—during discovery, then bringing all three answers into the same room as the design.

That shift not only hit deadlines, but it changed the conversation I could have with stakeholders—from "here's the design" to "here's the design, here's what it costs to ship, and here's how we'll know it worked."