The creation of large language models is like the advent of electricity, new and paradigm-shifting, but not well understood in terms of how it can be used. To identify use cases for this technology, it's helpful to start with an existing problem and consider how generative AI can solve it. The long and labor-intensive process of underwriting loans and mortgages is a prime example, where LLMs can speed up the process to half the time it would take manually. An implementation using a Compound AI approach combines many different AI components, data sources, and integrations to power mission-critical applications. A digital assistant called the "AI underwriter" works alongside human analysts throughout the underwriting workflow, automating labor-intensive aspects of underwriting and augmenting their capabilities. The copilot acts as connective tissue between scattered loan information and the analyst's decision-making process, handling tasks such as document processing, extracting critical data points, analyzing relationships, answering specific queries, and populating standardized sections of credit memos. By automating these labor-intensive aspects of underwriting, the copilot reduces the workload of analysts, allowing them to focus on higher-value risk assessment and decision-making. The implementation is technically implemented through a Compound AI approach, which requires a customized end-to-end solution with advanced AI models, data sources, and integrations. The power of the AI underwriter lies in its ability to combine specific details from multiple information sources to paint a nuanced picture of each deal. Deploying Compound AI has traditionally required a large team of experts, but AI orchestration frameworks and tools have changed that, making it easier for organizations to build quickly and within budget.