Detroit-based title agency Rocket Close deployed an agentic AI system called Supercharger to automate research-heavy tasks and accelerate order processing. According to a June 12, 2026 AWS blog post, the solution centralizes jurisdictional knowledge to reduce manual search time for title examiners and improve overall workflow throughput.
Technical architecture and deployment
The system runs on Strands Agents, an open-source agent SDK, paired with Anthropic Claude via Amazon Bedrock. Amazon Bedrock Knowledge Bases ingest state guides, county rules, and internal company policies to support title-examination tasks. To meet compliance requirements in regulated financial services, the system applies row-level data entitlements that restrict access to sensitive customer information. It also logs all conversations with full audit trails, a security integration that serves as a core competency for professionals following an AI Learning Path for Operations Managers.
Prior performance metrics
This deployment marks Rocket Close's second major engagement with AWS for operational improvements. An April 2026 AWS blog detailed a proof of concept that used Amazon Textract and Bedrock to process 2,000 abstract document packages daily. That initial project reduced processing time from 30 minutes to under two minutes per package while maintaining 90 percent accuracy. Supercharger addresses a different layer of this workflow, focusing on agentic research and decision support rather than raw document extraction.
Observable design patterns
The architecture decouples the agent SDK from the model runtime, using knowledge bases to encode jurisdictional rules such as state-specific title standards. Row-level entitlements and audit-logged conversations reflect compliance requirements common in financial services workflows. This design provides a clear blueprint for teams seeking AI Agents & Automation solutions in regulated environments. However, the AWS blog does not publish numeric throughput, error-rate, or cost-reduction benchmarks for Supercharger.
Why this matters for operations
Operations leaders should monitor whether production metrics emerge for Supercharger and how knowledge-base maintenance scales as new jurisdictions are added. The deployment demonstrates a concrete method for applying agentic AI to retrieval-heavy, compliance-bound workflows without exposing sensitive customer data. Tracking the maturation of open-source agent SDKs like Strands Agents will help operations teams evaluate competing frameworks for future workflow automation.
Your membership also unlocks:
