Traditional ETL Ingestion Frameworks Are Failing the Requirements of Modern AI Architecture

Traditional ETL pipelines are causing production AI models to fail because they are architecturally blind to semantic meaning. Classical data frameworks flatten text, strip layouts, and use fixed character chunking that cuts off critical context, leading directly to model hallucinations. To scale enterprise AI, data engineers must build layout-aware ingestion pipelines using parent-child chunking, continuous change data capture for real-time vector database synchronization, and deep metadata inj...