Building Supercharger: How Rocket Close optimized title operations with agentic AI

Rocket Close built Supercharger, an agentic AI solution, to optimize title operations workflows by combining title and closing knowledge to guide teams through the order processing workflow. The solution uses Strands Agents, large language models (LLMs), Amazon Bedrock, Amazon Bedrock Knowledge Bases, and Model Context Protocol (MCP) tools to centralize knowledge and automate research-heavy tasks. This results in improved efficiency, reduced time spent searching for information, and enhanced operational efficiency and client experience. The solution's architecture is designed with security in mind, using Amazon Bedrock Guardrails and row-level data entitlements to prevent accidental access to customer-sensitive data. For engineers building AI systems, this solution demonstrates the potential of agentic AI to streamline complex workflows and improve productivity.

⚡ Key Takeaways

• The Supercharger solution uses Strands Agents, an open-source agent harness SDK by AWS, to build agents using the Anthropic Claude Large Language Model (LLM) through Amazon Bedrock.
• The solution integrates with Rocket Close operational databases containing order information, standard procedures, and policies for state-level title exams.
• Conversation Analytics enables natural language processing that understands context and intent across multi-turn conversations.
• The solution's API-based integration connects with existing systems to maintain data consistency and avoid data duplication.
• The use of Amazon Bedrock Guardrails and row-level data entitlements provides intelligent access controls and prevents accidental access to customer-sensitive data.

The Supercharger solution demonstrates the potential of agentic AI to streamline complex workflows and improve productivity in the title operations industry. By leveraging large language models and conversational intelligence, engineers can build solutions that improve efficiency, reduce errors, and enhance client experience.

✅ Practical Steps

1. Use Strands Agents to build agents using large language models (LLMs) through Amazon Bedrock.
2. Integrate with operational databases containing order information, standard procedures, and policies for state-level title exams.
3. Implement Conversation Analytics to enable natural language processing that understands context and intent across multi-turn conversations.

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