AI-Optimized Real-Time Decision Systems for Digital Advertising

Abstract

Real-time bidding architectures powering programmatic advertising face simultaneous demands across latency, privacy, and decision quality that no single prior system has addressed within a unified engineering framework. The deprecation of third-party cookies, platform-level tracking restrictions, and evolving data protection regulation under GDPR have fundamentally altered the identity infrastructure that behavioral targeting depends upon, while exchange-imposed rigorous deadlines continue to constrain every component of the serving pipeline. Four concrete contributions are presented: a sub-50 ms AI inference pipeline built on distributed edge caching and SLO-aware gradient-boosted scoring; a federated identity framework achieving privacy-compliant personalization through rotating session tokens and cohort-based identifiers; a hybrid multi-agent reinforcement learning and large language model bidding optimizer delivering substantial revenue improvement over rule-based baselines; and a systematic experimental evaluation framework reporting latency, throughput, and CTR prediction accuracy synthesized from peer-reviewed production-scale benchmarks. End-to-end P95 latency remains within the exchange deadline at production DSP throughput, CTR prediction AUC reaches 0.776 for gradient-boosted models, and coordinated multi-agent RL bidding achieves 19,501 CNY platform revenue versus 5,347 CNY for hand-crafted rules. Zero-knowledge verification mechanisms address the measurement attribution gap introduced by identifier deprecation, while legally grounded privacy design satisfies GDPR requirements as system properties rather than post-hoc compliance overlays.