Full.ad
Fully autonomous end-to-end advertising generation and optimization powered by multi-agent LLM orchestration, synthetic audience simulation, and reinforcement learning
Zero-touch optimization without human intervention
Automated classification using transformer-based zero-shot labeling
Bayesian meta-model aggregates simulation runs per variant
System Architecture
Core Infrastructure
Microservice architecture on Docker and Kubernetes (GKE). Managed through Kubeflow Pipelines with PromptOps versioning, Model Registry for LoRA/QLoRA adapters, and Reinforcement Engine for policy optimization.
Data Fabric
Vector Database (Pinecone/Weaviate) for multimodal embeddings. BigQuery + DuckDB for high-speed analytics. Google Cloud Storage/AWS S3 for ad assets.
Multimodal Ingestion
Asynchronous ETL with Airbyte + dbt Core from Meta Ads, Google Ads, TikTok APIs. Scraping via Apify + BrightData. Real-time trend streams from X, TikTok, Google Trends. Audio/video transcribed with Whisper + CLAP.
Knowledge Graph
Neo4j graph with nodes for Keywords, Creatives, Personas, Channels, Metrics. OpenAI text-embedding-3-large + CLIP ViT-L/14 with LoRA emotional fine-tune. Ontology Manager auto-expands structures.
Language Generation
GPT-5 fine-tuned for copywriting, sentiment, compliance. LangChain StructuredOutputParser for deterministic JSON outputs. Guardrails AI enforces brand safety and content regulation.
Visual & Audio Generation
Images: Stable Diffusion XL Turbo + ControlNet. Video: RunwayML Gen-3 with CLIP-guided temporal composition. Audio: ElevenLabs multilingual TTS with cloned brand voice profiles.
Synthetic Audience Simulation
LLM agents simulate psychographic profiles. Mesa ABM framework with utility functions: Reward = CTR_predicted + (Engagement × EmotionalResonance) - CognitiveLoad. Bayesian meta-model aggregates 10k+ runs.
Predictive Models
Hybrid DeepFM + Transformer encoder for CTR prediction. Trained on streaming batches via Petastorm + PyTorch Lightning. Processes structured data and embeddings.
Reinforcement Learning
Policy optimization through Proximal Policy Optimization (PPO). Rewards from synthetic simulation + live metrics. Distributed training on Ray Tune clusters. Automatic prompt parameter adjustment.
Campaign Deployment
Integrations with Meta, Google, LinkedIn, TikTok APIs. OAuth2 token rotation + rate-limit handling. Multi-Armed Bandit budget allocator for dynamic spend optimization.
Monitoring & Governance
Prometheus + Grafana dashboards. ElasticSearch + OpenTelemetry tracing. Evidently AI for drift analysis. Zero-shot toxicity detection. AES-256 encrypted storage with IAM permissions.
Continuous Learning
AutoML retraining with MLflow triggered by drift or 5%+ improvement. Genetic Prompt Mutation Engine evolves prompts. Concept Drift Detector triggers regeneration. Memory compression via vector store pruning.
Tech Stack
Orchestration
Backend
Model Serving
Storage
Data Streaming
ML Framework
Frontend
Observability
Infrastructure
Innovation Highlights
Synthetic A/B Testing
Removes ad spend during ideation phase by simulating audience behavior before deployment
Cross-Modal Reinforcement
Real-time adjustment of copy, visual, and audio synergy through unified feedback loops
Genetic Prompt Mutation
Autonomous evolution of ad concepts through mutation and selection of high-performing prompts
Knowledge Graph Synthesis
Zero-shot campaign creation for new products leveraging semantic relationships
Self-Healing Campaigns
Automatic rerouting of publication flows when ad network APIs fail
Zero-Touch Optimization
Human oversight optional with continuous performance-driven evolution
Interested in learning more about Full Ad?
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