Rocket Close Supercharger: agentic title examination assistance with Amazon Bedrock Knowledge Bases
Rocket Close is a Detroit-based title agency and appraisal management company that faced bottlenecks in state-specific title examinations because teams had to manually research county, probate, and tax ID requirements across fragmented systems. To address the workflow, Rocket Close built Supercharger, an agentic AI solution that centralizes title and closing knowledge, guides operations teams through order processing in natural language, and surfaces order-specific context from internal systems.
- Organization
- Rocket Close
- Industry
- Insurance
- Location
- United States
- Published
- June 2026
Reported outcomes
−30%
incoming calls and emails to contact centerOther quantified impact
Strategic outcomes
Primary read
Use case focus
Showing 3 of 3
- 1AI agents
- 2Workflow automation
- 3Customer service agent
- State-specific title examinations required manual research across disparate systems.
- Teams had difficulty scaling throughput while maintaining accuracy.
- Title examiners had to search multiple systems, state guides, and county requirements.
- Rocket Close built an agentic solution called Supercharger using Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, Strands Agents SDK, and Model Context Protocol tools.
- The domain-specific agent queries policies and procedures from the knowledge base, retrieves order information through MCP tools, applies guardrails for security and compliance, and streams responses back through an authenticated WebSocket workflow.
- The architecture was designed to centralize knowledge, reduce manual research, maintain audit trails, and support future expansion to additional teams.
- The company reduced incoming calls and emails to the contact center by 30%.
- The solution improved state exam accuracy and reduced research and cognitive load for decisions.
- Rocket Close enhanced client satisfaction by automating routine tasks and drafting communications.
- Architectural and prompting refinements achieved 3x latency improvements and reduced costs.
Architecture
An authenticated chat workflow uses JWT and Istio to establish a WebSocket connection, invokes a Strands Agent, queries Amazon Bedrock Knowledge Bases for policies and procedures, uses MCP tools to fetch order information from the Atlas Web API, and streams synthesized responses back to the UI. Amazon Bedrock Guardrails and row-level entitlements help enforce security and compliance, while conversations are logged for audit trails.
Sources & evidence1
AI-generated summary. Verify important details with the linked sources before relying on this case.
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