What challenges did Rocket Close face before AI implementation?

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.

What was the impact of the AI solution?

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.

Source

Evidence: Low35/100

Rocket Close Supercharger: agentic title examination assistance with Amazon Bedrock Knowledge Bases

Use case typeAI agentsUpdated Jun 12, 2026

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

+200%latency

Strategic outcomes

Cost efficiencyReduced manual research and cognitive loadCustomer experience & trustEnhanced client satisfactionRisk & complianceImproved security and auditability

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Incoming calls and emails to contact center: 30% decrease

AWS Machine Learning BlogJun 12, 2026Blog postExplicit claimLow evidence strength

reducing the number of incoming calls and emails to the contact center by 30%

Normalized claim

Latency: 200% increase

AWS Machine Learning BlogJun 12, 2026Blog postInferred claimLow evidence strength

achieving 3x latency improvements

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Rocket Close
Provider: AWS
Maturity: Unknown
Linked source: AWS Machine Learning Blog

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

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.

Technologies

Amazon Bedrock Knowledge Bases Amazon Bedrock Guardrails Strands Agents SDK Model Context Protocol Amazon S3

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

Evidence: Low35/100Evidence strength