Most GenAI failures aren't model problems — they're data pipeline problems in disguise. We spent months debugging a GenAI feature producing outputs our product team called confidently wrong. The model wasn't the issue. Two upstream systems were preprocessing the same field differently, and the resulting embeddings were subtly misaligned in ways that only surfaced under specific query patterns. The model was doing exactly what it was supposed to with inputs that were quietly broken. That experience reshaped how we think about GenAI reliability. The failure points teams blame on models are almost always upstream: inconsistent preprocessing, stale embeddings, retrieval pipelines built for analytics latency instead of inference latency, and absent data contracts between producers and consumers. In this session we define what AI-ready data actually means in practice — freshness SLAs, preprocessing consistency guarantees, validated embeddings with drift detection, and retrieval latency budgets that fit inside a user-facing inference window. We walk through a purpose-built AWS architecture: Glue Data Catalog for governed metadata, DynamoDB and ElastiCache for low-latency feature serving, Kinesis for real-time context injection, and Bedrock Knowledge Bases with pgvector on Aurora for RAG pipelines. We close with a practical pipeline audit checklist attendees can take back and apply to their own infrastructure the following week.