Blab Business
Rocket Close Deploys AI-Powered Supercharger to Streamline Title Operations
Rocket Close, the Detroit‑based title agency and appraisal arm of Rocket Companies, has unveiled Supercharger, an agentic artificial‑intelligence platform that promises to cut months of manual title research into minutes.
The launch, announced in a 2026 AWS Machine Learning blog post, follows a partnership with Amazon Web Services (AWS) that leverages Strands Agents—an open‑source SDK for building autonomous agents around large language models (LLMs). Supercharger tackles a long‑standing bottleneck in the mortgage industry: title examiners must comb through scattered state guides, county rules and multiple databases to verify property records, a task that can consume hours of manual labor and scales poorly as loan volumes climb.
Supercharger is powered by Anthropic’s Claude model accessed through Amazon Bedrock, which supplies a unified API for several foundation models. When a user submits a query, the agent consults a Bedrock Knowledge Base filled with state‑specific examination checklists and company policies. It then calls a set of Model Context Protocol (MCP) tools that pull order‑specific data from Rocket Close’s internal Atlas Web API. The LLM synthesizes the information and streams a natural‑language response back to the user via a WebSocket connection.
Key technical features include:
Knowledge‑base integration – The agent searches a Bedrock Knowledge Base that contains state‑specific title examination checklists and company policies. Tool‑calling architecture – Each data source is exposed as an MCP tool, allowing the agent to select the appropriate function dynamically. Security and compliance – Amazon Bedrock Guardrails and row‑level data entitlements prevent accidental exposure of sensitive customer data. All conversations are logged with audit trails to satisfy regulatory requirements. Performance optimizations – By retrieving all necessary order data in a single call before LLM synthesis, the team reduced the number of LLM invocations, achieving a three‑fold latency improvement and lower operating costs.
According to the AWS blog, the solution delivers “question‑answering ability with real‑time insights about orders within existing workflows.” Rocket Close’s Vice President of Data Science, Bryan Bedard, said the tool “has saved thousands of calls and emails per month to our contact center, giving us greater scale and a better client experience.”
Operational impact figures reported by Rocket Close include:
A 30 % reduction in inbound calls and emails to the contact center. Improved state‑exam accuracy through real‑time guidance. Enhanced client satisfaction via automated routine tasks and on‑demand communication drafting. Consistent workflow execution across teams, reducing cognitive load.
The team also highlighted several lessons learned during development:
Efficient data retrieval is critical; a single‑call approach to order data minimizes LLM usage. Maintaining a clear separation of concerns between Strands Agents and MCP tools yields a flexible architecture. WebSocket‑based streaming provides immediate user feedback, improving perceived performance. Descriptive tool naming and coherent docstrings help the agent reason effectively. * Offloading security enforcement to session attributes keeps business logic clean.
Looking ahead, Rocket Close plans to extend Supercharger to support loan‑specific queries for bankers and to create fast‑start templates that enable other domain teams to build similar agentic solutions.
The initiative illustrates how mortgage‑industry firms can leverage agentic AI to transform knowledge‑intensive processes. By combining a domain‑specific knowledge base, LLMs, and a modular tool‑calling framework, Rocket Close has achieved measurable efficiency gains while maintaining compliance and security.
Supercharger’s launch comes at a time when the mortgage sector continues to digitize title and settlement functions. As loan volumes grow, tools that reduce manual research and streamline compliance checks are likely to become increasingly valuable.
For more technical details, Rocket Close references the Strands Agents documentation and the Amazon Bedrock marketing page. The company encourages other firms to adopt a similar approach, emphasizing clear business requirements, collaboration between technology and business teams, and iterative refinement based on real‑world usage.
The launch, announced in a 2026 AWS Machine Learning blog post, follows a partnership with Amazon Web Services (AWS) that leverages Strands Agents—an open‑source SDK for building autonomous agents around large language models (LLMs). Supercharger tackles a long‑standing bottleneck in the mortgage industry: title examiners must comb through scattered state guides, county rules and multiple databases to verify property records, a task that can consume hours of manual labor and scales poorly as loan volumes climb.
Supercharger is powered by Anthropic’s Claude model accessed through Amazon Bedrock, which supplies a unified API for several foundation models. When a user submits a query, the agent consults a Bedrock Knowledge Base filled with state‑specific examination checklists and company policies. It then calls a set of Model Context Protocol (MCP) tools that pull order‑specific data from Rocket Close’s internal Atlas Web API. The LLM synthesizes the information and streams a natural‑language response back to the user via a WebSocket connection.
Key technical features include:
Knowledge‑base integration – The agent searches a Bedrock Knowledge Base that contains state‑specific title examination checklists and company policies. Tool‑calling architecture – Each data source is exposed as an MCP tool, allowing the agent to select the appropriate function dynamically. Security and compliance – Amazon Bedrock Guardrails and row‑level data entitlements prevent accidental exposure of sensitive customer data. All conversations are logged with audit trails to satisfy regulatory requirements. Performance optimizations – By retrieving all necessary order data in a single call before LLM synthesis, the team reduced the number of LLM invocations, achieving a three‑fold latency improvement and lower operating costs.
According to the AWS blog, the solution delivers “question‑answering ability with real‑time insights about orders within existing workflows.” Rocket Close’s Vice President of Data Science, Bryan Bedard, said the tool “has saved thousands of calls and emails per month to our contact center, giving us greater scale and a better client experience.”
Operational impact figures reported by Rocket Close include:
A 30 % reduction in inbound calls and emails to the contact center. Improved state‑exam accuracy through real‑time guidance. Enhanced client satisfaction via automated routine tasks and on‑demand communication drafting. Consistent workflow execution across teams, reducing cognitive load.
The team also highlighted several lessons learned during development:
Efficient data retrieval is critical; a single‑call approach to order data minimizes LLM usage. Maintaining a clear separation of concerns between Strands Agents and MCP tools yields a flexible architecture. WebSocket‑based streaming provides immediate user feedback, improving perceived performance. Descriptive tool naming and coherent docstrings help the agent reason effectively. * Offloading security enforcement to session attributes keeps business logic clean.
Looking ahead, Rocket Close plans to extend Supercharger to support loan‑specific queries for bankers and to create fast‑start templates that enable other domain teams to build similar agentic solutions.
The initiative illustrates how mortgage‑industry firms can leverage agentic AI to transform knowledge‑intensive processes. By combining a domain‑specific knowledge base, LLMs, and a modular tool‑calling framework, Rocket Close has achieved measurable efficiency gains while maintaining compliance and security.
Supercharger’s launch comes at a time when the mortgage sector continues to digitize title and settlement functions. As loan volumes grow, tools that reduce manual research and streamline compliance checks are likely to become increasingly valuable.
For more technical details, Rocket Close references the Strands Agents documentation and the Amazon Bedrock marketing page. The company encourages other firms to adopt a similar approach, emphasizing clear business requirements, collaboration between technology and business teams, and iterative refinement based on real‑world usage.
