Kirimgeray Kirimli

Moving from basic prompt calls to enterprise-grade AI content generation requires treating the workflow as a rigorous MLOps engineering pipeline rather than a simple API wrapper. Drawing from real-world deployments across media companies and enterprise data environments, this session explores the full production lifecycle of autonomous content engines. We cover multi-stage data ingestion, low-latency fact-checking guardrails, lightweight evaluator models for brand-voice alignment, and automated confidence scoring that dynamically routes low-certainty outputs to Human-in-the-Loop (HITL) review queues—enabling high-throughput content generation with strict corporate governance.

Standard RAG architectures rely on naive vector chunking, which strips away vital structural, temporal, and relational context—leading to hallucinations when agents query vast media archives or complex enterprise data. This technical deep-dive reveals how to engineer unified Graph-Vector-Relational ingestion pipelines capable of processing multi-billion-record datasets at scale. We detail the architecture behind real-time Change Data Capture (CDC) to maintain transactional consistency across vector stores and knowledge graphs, design sub-second query orchestration layers, and demonstrate how to turn deep historical archives into deterministic, hallucination-free memory systems for AI agents.