AI system case study
An AI workflow for on-brand ABM personalization.
A marketer enters the account, audience, and campaign requirements. The workflow returns personalized, brand-aligned assets in every required size.
The problem
Account-level personalization was a production bottleneck.
Every account needed enough variation to be relevant, while every asset still had to preserve the correct logo, typography, layout, and dimensions. Producing each version manually limited how quickly a campaign could move.
What I built
The marketer sets the strategy. The workflow handles production.
I built a workflow where a marketer supplies the account, vertical, persona, and ad requirements. AI creates the visual variation. Python applies the approved brand elements and outputs every required size. Those assets feed an ABM content engine that also personalizes email, LinkedIn, and display campaigns using account intelligence.
How it runs
From campaign input to a complete asset set.
- Direction. The marketer supplies the strategy, audience, and account context.
- Variation. AI creates imagery based on those approved inputs.
- Assembly. Code applies the precise logo, typography, text, and layout.
- Production. The workflow produces the complete set of campaign assets in the required sizes.
What I learned
I used AI for variation and code for the parts that had to be exact. The form keeps strategy with the marketer. The workflow handles repetitive production while preserving the brand rules.