The text discusses the challenges of implementing a customer support bot that utilizes AI to analyze tickets and suggest responses. The bot struggles with using outdated information due to the overnight ETL jobs that result in stale data, leading to issues such as suggesting refunds for customers who have already received them. The complexity of managing various components and tools exacerbates these problems, highlighting the difficulty of productionizing AI systems. Chalk, a proposed solution, aims to address these issues by treating AI as infrastructure, integrating machine learning and language model features into a unified platform. Chalk enables real-time context injection, versioned feature classes, and reusable LLM outputs, simplifying the AI stack and ensuring data freshness. This approach allows for more reliable and traceable AI decision-making, promoting a seamless transition from experimentation to production without needing extensive code rewriting, ultimately making AI deployment akin to traditional software engineering.