A news summary based solely on the provided article body: Nearly every organization now has access to frontier AI models from OpenAI, Anthropic, Google, and others, yet outcomes remain uneven because even the most powerful model can only work with the information it receives. In high-stakes, regulated environments, getting that information right, structured, verified, and auditable is a complex engineering challenge. Current frontier models offer context windows of a million tokens or more, but complex financial workflows require ratings, research, financials, ownership data, and regulatory filings across dozens of entities, causing context to fill fast. The real engineering challenge encompasses retrieval pipelines, evaluation frameworks, governance infrastructure, and structured domain knowledge. Andrej Karpathy, an OpenAI co-founder, offered an analogy: if the LLM is the CPU, the context window is RAM, and context engineering is the operating system. At Moody’s, this infrastructure is called connected intelligence, built on four subsystems: an ingestion and normalization layer that transforms raw multimodal data into clean, structured inputs with temporal and provenance metadata, and a retrieval architecture.