Rocket Close, a Detroit-based title agency within Rocket Companies, faced bottlenecks in title operations as mortgage demand grew. Title examiners spent hours navigating disparate systems, state guides, and county requirements to verify data and understand local regulations. To address this, Rocket Close collaborated with AWS to build Supercharger, an agentic AI solution designed to optimize title workflows and reduce friction in the homebuying process (AWS Machine Learning Blog).
Supercharger combines Strands Agents, large language models via Amazon Bedrock, knowledge bases, and Model Context Protocol tools to centralize title and closing knowledge and automate research-heavy tasks. The solution provides conversational analytics, state-level title examination assistance, API-based integration, guardrails for compliance, comprehensive logging, and unified data access across multiple systems (AWS Machine Learning Blog). For commerce and lending practitioners, this architecture demonstrates how agentic AI can reduce manual research time, improve decision accuracy, and scale operations without proportional headcount growth.
The business impact was substantial: Supercharger reduced incoming calls and emails to the contact center by 30% through its question-answering capability, improved state exam accuracy through real-time order insights, and achieved 3x latency improvements through architectural refinement and better prompting (AWS Machine Learning Blog). The project highlights key lessons around efficient data retrieval, separation of concerns between agents and tools, WebSocket-based streaming for perceived performance, and the importance of designing solutions that leverage agent intelligence rather than constraining it.