Recursive AI for Ad Banner Generation

Online advertising campaigns must be tailored to different platforms and user behaviors while strictly adhering to brand guidelines. Traditionally, this process requires skilled professionals who invest significant time and effort to achieve optimal results.

While some existing tools—like design automation platforms or creative optimization suites—streamline banner creation, they typically rely on templates and generalized insights rather than leveraging an advertiser’s own first-party data (e.g., actual click-through rates or conversion metrics). As a result, creative decisions often rely on the intuition and experience of designers and marketers rather than being clearly rationalized through data-backed insights.

Recursive AI for Ad Banner Generation enables the semi-automatic creation of ad banners that are fully aligned with your brand’s visual identity and seamlessly incorporate brand-specific assets (e.g., logos, product images). It further optimizes banners based on AI-predicted performance to maximize key advertising metrics such as click-through rate (CTR), conversion rate (CVR), and cost per action (CPA). This AI-driven approach results in an average 60% reduction in time spent by designers and marketers on ad creation.

The system integrates two AI models: the Image Generation Model and the Banner Optimization Model.

Image Generation Model

• High-Quality Image Generation: Leverages state-of-the-art image diffusion technology to generate realistic images from text descriptions.
• Visual Identity Customization: Automatically applies style-specific adjustments to elements like lighting, contrast, and color, ensuring alignment with your brand’s visual identity.
• Brand Asset Integration: Incorporates brand-specific assets (e.g., logos, product images, or brand ambassador photos) into generated visuals.
• Multi-Language Support: Automatically translates prompts entered in languages other than English, ensuring seamless functionality across multiple languages.
• Image-to-Image Functionality: In addition to generating images from text, the model can modify elements within existing images—such as adding, removing, or changing accessories, clothing, hairstyles, etc.—based on user prompts.
• Fast Processing: Includes a custom request handler with asynchronous processing to efficiently manage simultaneous tasks from multiple users.
• Background Editing: Designed specifically for banner creation, the model can automatically change or remove image backgrounds when a specific trigger word is added to the prompt.

Banner Optimization Model

• Accurate Performance Prediction: Predicts banner performance across different audience segments with approximately 72% accuracy based on historical data from various digital channels.
• Design Optimization: Uses performance predictions to refine visual designs, maximizing key metrics such as click-through rate (CTR), conversion rate (CVR), and cost per action (CPA), powered by a custom multimodal LLM.
• Cost-Effective Deployment: Operates efficiently on standard CPU hardware, simplifying deployment and reducing operational costs.

Implementation