I audit how ecommerce teams use AI, direct the creative strategy behind their Meta ads, and build the system that carries customer language, feedback and ad results into the next brief.
Most teams do not need another AI tool. They need a system that remembers the strategy, the feedback and the performance data they already paid for.
The same feedback gets repeated every week.
Winning ads live in folders instead of the next brief.
Quality still depends on one senior person's memory.
You already paid for that learning.
Customer language, rejected claims, creative feedback and ad results should follow the idea into the next brief. If they do not, you pay for the same lesson again.
Pick the place where work currently breaks: the AI setup, the Meta plan, or the system connecting feedback and performance to what gets made next.
I map where AI is already used, which instructions and decisions disappear between sessions, where claims or approvals can fail, and which workflow should be fixed first.
I read Meta performance at angle, hook and offer level, decide what to repeat, retire or test next, and turn that into the creative plan your team can actually make.
I build a Creative Brain around your own customer language, winning patterns, approved claims, playbooks and performance data. Then I connect it to how your team actually works.
The model is the replaceable part. The build decides what it reads, what it is allowed to say, how the work is checked, and how ad results change the next brief.
Ads, reviews, surveys, comments, claims and past decisions.
What good looks like, what cannot ship and what the brand will not say.
Briefs, hooks, angles and scripts built from relevant context. It does not begin with a giant prompt.
Claims, compliance, grading and human approval before work gets through.
What happened in market changes what the system recommends next.
Across DTC brands, the same problems kept showing up: customer knowledge was hard to use, feedback disappeared between sessions, and ad results never reached the next brief. These are the systems I built in response.
Reviews, surveys and comments stored by theme, objection and buying context, so they become ad angles instead of another research document.
Approved claims, banned phrases, copy rules and grading criteria checked before a draft reaches the team.
Performance follows the concept, so winning angles return for a reason and ideas that failed do not get suggested again.
I knew the brand inside out. I made the content and wrote the strategy. Every new chat still forgot the corrections I had just made.
Every draft started from scratch. The feedback, performance data and brand rules had nowhere to go. I built the system so the next brief could use what the last campaign taught us.
If AI makes the same mistake twice, your feedback died in the chat.
The best fit is an ecommerce brand with an internal marketing or creative team and enough creative volume for repeated mistakes to become expensive.
Tell me what your team is trying to change. If one of the three routes fits, I will tell you where I would start.
You will speak with me.
