Every output becomes a new file.
- 01Resize the layout for every placement.
- 02Rewrite copy without shared constraints.
- 03Re-check spacing, contrast, and brand rules.
- 04Repeat the same review across the set.
Explore controlled, on-brand ad variations from one master template. This case study demonstrates AI-assisted content routes and design checks while keeping human review in the loop.
Demonstration boundary: The interactive studio uses fixed synthetic data. It does not generate or publish files, and this case study does not claim a measured production output benchmark.
Creative teams need more formats, more messages, and more audience variations without rebuilding every composition or letting the visual system drift.
Interactive demo · synthetic campaign data · no files are generated or published. Pick a master, a format, an audience and a message route; the design checks re-score on every change.
Turn one approved system into a useful range of launch-ready directions.
Start building →A product interpretation grounded in the published positioning and technology list. These stages demonstrate how the idea could work; they are not audited implementation details.
Identify editable regions, hierarchy, components, and constraints.
Figma Plugin APIAdapt the headline and support copy for audience and objective.
OpenAI GPT-5Check contrast, spacing, hierarchy, overflow, and brand tokens.
TypeScriptCompare directions, refine the selected output, and keep human approval.
shadcn/uiThe stack behind the plugin, and the job each part does.
Typed creative rules, state, and editor interactions.
Template context and design-tool integration.
Published model reference for content generation.
Interface foundations for the product controls.
Tools like Mojo start as a product question: what should stay fixed, and what should be free to vary. Bring us the template and the rules.