2.1 Core Components

Event Driven Microservices

• RabbitMQ serves as the backbone for task orchestration, enabling asynchronous communication between system components such as background removal, template adaptation, website optimization, and ad campaign management.

• Producers generate tasks (e.g., uploading images, initiating A/B tests, or creating campaigns), while consumers (workers) process these tasks in parallel.

• Monitoring & Logging: Each microservice publishes logs and metrics to centralized systems (e.g., ELK stack or Prometheus/Grafana) for system health monitoring and alerting.

AI-Powered Design and Marketing Customization

• Background Removal: Powered by advanced segmentation models (e.g., U-Net variants or integration with Remove.bg API) for accurate isolation of foreground objects. For high-volume operations, GPU clusters (e.g., AWS EC2 G-series instances) accelerate background removal tasks.

• Template Integration: Figma and Canva APIs enable direct manipulation of template layers, including automated replacement of placeholders, scaling, and alignment. A “Template AI Agent” can analyze existing brand styles (colors, fonts, etc.) and dynamically apply them to new designs.

• Dynamic Shopify Websites: Automatically generates Shopify websites with pre- defined themes optimized for e-commerce. Clever’s “Web Layout AI Agent” parses brand assets and recommended design patterns to produce a consistent look-and-feel.

• A/B Testing Automation: Implements test variations (e.g., different layouts, CTAs, or color schemes) and tracks user interactions via embedded scripts. A “Performance Optimization AI Agent” uses machine learning to evaluate results and recommend next steps for higher conversions.

• Facebook Ad Campaigns: Automates the creation, management, and monitoring of ad campaigns with performance insights. Clever’s “Ad Optimization AI Agent” adjusts parameters such as targeting, bidding, and creative variations based on real- time metrics.o LLM-Enhanced Copy Generation: Pre-trained language models (e.g., GPT-based or BERT-based solutions) produce ad copy, product descriptions, blog content, and text variants that align with brand guidelines.

Dynamic Scaling-Powered Design and Marketing Customization

• Containerized Workers using Docker and Kubernetes (K8s) scale up or down depending on the workload. Kubernetes Horizontal Pod Autoscalers (HPAs) monitor CPU, memory, or custom metrics (e.g., queue length) to manage scaling decisions.

• Parallel Task Processing: Tasks are processed in parallel, allowing thousands of designs and campaigns to be handled simultaneously.

• Cost Optimization: Spot instances or preemptible VMs can be used for non-time-critical tasks to reduce operational costs.

Cloud-Native Infrastructure

• Storage: AWS S3 or Google Cloud Storage is used to store uploaded assets, finalized designs, and campaign data.

• Compute: Serverless functions (e.g., AWS Lambda or Google Cloud Functions) handle lightweight tasks (e.g., image compression, data validation, logging), while GPU-enabled virtual machines process computationally intensive AI workflows.

• CI/CD Pipeline: Automated testing and deployment pipelines (e.g., GitHub Actions or Jenkins) ensure new features and updates are released with minimal downtime.