The Bottleneck in Title Insurance

Navigating the labyrinth of property rights and real estate regulations is a high-stakes, manual grind. For Rocket Close, a Detroit-based title insurance and asset valuation firm, the process of verifying property records across disparate state systems was a persistent operational bottleneck. Reviewers were forced to toggle between multiple databases and cross-reference regional tax requirements manually, consuming significant time from skilled staff. To solve this, the company partnered with AWS to build an AI agent solution called Supercharger, designed to bridge the gap between fragmented internal data and the need for rapid, accurate decision-making.

Architecting for Efficiency with MCP

Rather than forcing a single, monolithic prompt to handle every edge case, Rocket Close adopted a modular approach. They utilized Strands Agents, an open-source SDK from AWS, to manage the planning, tool-calling, and reasoning loops of the agent, powered by Anthropic’s Claude models. The breakthrough came from integrating the Model Context Protocol (MCP), which allows the agent to treat various data sources as standardized, independent tools. By decoupling the data retrieval from the LLM’s reasoning, the agent only pulls the specific information required for a task, rather than scanning entire databases. This structure ensures that as data sources or models evolve, the core system remains flexible and maintainable.

The Shift from Manual Search to Intelligent Assistance

Supercharger fundamentally changed how the operations team interacts with complex order data. By centralizing fragmented information, the agent acts as a conversational partner that guides staff through the title search workflow. The system integrates six core functions, ranging from conversation analytics to state-specific regulatory checklists. Unlike traditional keyword-based systems, the agent uses natural language processing to grasp the intent behind multi-turn dialogues, providing actionable insights in real-time. To maintain security and compliance, the team implemented Amazon Bedrock Guardrails, which filters inputs and outputs while maintaining a strict audit trail of all interactions for regulatory oversight.

Quantifiable Gains in Performance

This architectural shift yielded immediate, measurable results. By optimizing the agent to perform targeted data lookups via MCP, Rocket Close reduced the number of unnecessary LLM calls, which in turn improved system latency by 3x. Bryan Bedard, VP of Data Science at Rocket Close, noted that the integration of the agent with external chat interfaces successfully reduced monthly customer service inquiries by 30%. The transition from manual data entry to an agent-assisted model has allowed the operations team to move away from repetitive tasks and focus on high-value analysis, proving that agentic automation is most effective when domain-specific knowledge is treated as a modular tool rather than a static prompt.

For teams looking to replicate this, the path forward is to stop treating LLMs as general-purpose engines and start defining your data sources as callable functions. By focusing on goal-oriented prompts rather than rigid, step-by-step instructions, you allow the model to navigate complex workflows with greater autonomy and precision.