Technical Details

Central to Clever’s operation is the Clever AI Agent—an umbrella of specialized sub-agents, each trained or fine-tuned for different tasks. These sub-agents communicate through standardized protocols and share relevant data via centralized stores or message queues.

Clever AI Agent Architecture

Sub-Agent Ecosystem:

• Design AI Agent: Focuses on analyzing brand identity (color palette, typography rules), applying design heuristics, and generating new layout variations.

• Performance Optimization AI Agent: Conducts real-time or near-real-time analysis on website and ad performance, leveraging machine learning models to propose optimal design changes or audience segments.

• Orchestrator AI Agent: Oversees resource allocation and manages scheduling conflicts or priority tasks across microservices.

• LLM-Based Copy Agent: Uses pre-trained large language models to generate and refine marketing copy, product descriptions, and ad headlines. Fine-tuned models can enforce brand- specific linguistic styles and terminologies.

Model Training and Maintenance

• Data Ingestion & Labeling: Images, brand style guides, and campaign performance metrics feed into respective databases. Labelling tools (e.g., Labelbox) might be used to improve segmentation models or train brand consistency models. Text data (e.g., brand guidelines, style references) continuously refines the LLM-based copy agent.

• Continuous Learning: For tasks like background removal or A/B test optimization, new data continuously refines Clever’s AI capabilities. LLM-based models can be fine-tuned or prompt-engineered to incorporate emerging brand rules.

• Versioning & Rollback: Model versions are tracked using MLOps platforms (e.g.,MLflow), enabling safe rollbacks in case a new model version underperforms.

Inference Workflow

• Edge Cases & Quality Checks: Confidence thresholds ensure that images or templates requiring human oversight are flagged. LLM responses are similarlymonitored for compliance with brand tone and style.o Caching & Reuse: Frequently used transformations (e.g., standard brand color sets or common ad copy phrases) are cached, reducing redundant computations.

• Performance Monitoring: GPU, CPU, and memory metrics are tracked to identify slow or failing inference sessions, triggering automated scaling or fallback mechanisms.

Security & Compliance

• Authentication & Authorization: API interactions with third-party services (e.g., Figma, Canva, Shopify, Facebook) leverage OAuth 2.0 or signature-based access.

• Data Protection: Sensitive brand data or user PII is encrypted at rest and in transit(TLS 1.2+). Role-based access controls (RBAC) ensure only authorized microservices can access specific resources.

• Compliance: Configurable data retention policies, GDPR compliance for user data, and logging audits ensure enterprise-grade security standards.

• LLM Governance: For text-generation tasks, any compliance or brand-safety checks (e.g., profanity filters, banned keywords) are enforced through prompt engineering or post-processing pipelines.