Developed a custom Generative AI platform that solves the "generic AI look" problem. By coupling Stable Diffusion models with a Retrieval-Augmented Generation (RAG) engine containing specific brand guidelines and assets, this solution allows marketing teams to generate near-final creative assets that are 100% brand-compliant.
Off-the-shelf models (Midjourney, DALL-E) frequently hallucinated colors or styles that violated strict corporate brand guidelines, requiring heavy manual retouching.
Early prototypes suffered from 30+ second generation times, causing friction for designers used to rapid iteration cycles.
Running dedicated GPU instances 24/7 for sporadic design workload resulted in excessive cloud bills with <10% utilization.
Implemented a Vector Store (OpenSearch) indexing thousands of approved brand assets. The retrieval step injects style context into the prompt before it hits the image generation model.
Deployed custom Stable Diffusion XL models on Amazon SageMaker Asynchronous Inference endpoints. This allows auto-scaling to zero when unused, optimizing cost.
Decoupled the user interface from the heavy compute backend using API Gateway and SQS. This ensures a responsive UI even during burst workloads.
REST Interface
Buffering
Request Processor
Async Inference
Vector Store
Generated Assets
Metadata
Reduced creative campaign turnaround time from 2 weeks to days by providing designers with high-quality starting points.
RAG implementation ensures generated colors and motifs align with brand guidelines 99% of the time, eliminating "blank page" syndrome.
Implementation of SageMaker Async Inference (Scale-to-Zero) drastically cut GPU costs compared to always-on instances.
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