Rocket Close, a Detroit-based title agency within Rocket Companies, partnered with AWS to develop Supercharger, an agentic AI solution designed to streamline title operations and lending workflows (AWS Machine Learning Blog). Title examiners previously spent hours navigating multiple systems, state guides, and county requirements to verify data from disparate sources. Supercharger combines title and closing knowledge with natural language interaction to guide teams through order processing, centralizing knowledge and automating research-heavy tasks (AWS Machine Learning Blog).
The solution is powered by Strands Agents—an open source agent harness SDK—combined with Amazon Bedrock, Knowledge Bases, and Model Context Protocol (MCP) tools that dynamically select and invoke data sources based on user queries (AWS Machine Learning Blog). Supercharger delivered immediate operational gains: contact center calls and emails dropped by 30%, state exam accuracy improved through real-time order insights, and the system achieved 3x latency improvements through architectural refinement (AWS Machine Learning Blog). For commerce and lending practitioners, this demonstrates how agentic AI can transform knowledge-intensive, multi-system workflows into streamlined, natural-language-driven processes that reduce manual research and scale operations without proportional headcount growth.
Key architectural lessons emerged around efficient data retrieval, WebSocket-based streaming for perceived performance, and letting agents orchestrate dynamically rather than constraining them with deterministic steps (AWS Machine Learning Blog). The future roadmap includes expansion for bankers to address loan-specific questions and fast-start templates for other domain teams, signaling a broader shift toward agentic automation in mortgage and lending operations.