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CASE STUDY

HIPAA-Compliant Agentic AI Platform for Automated Claims Intelligence Using AWS Bedrock

 
 

Mactores built a secure, HIPAA-compliant Agentic AI platform for KlearTrust using AWS Bedrock to automate medical document processing, accelerate claim intelligence, and improve audit readiness.

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About_Customer_KlearTrust

About 
The Customer

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KlearTrust is a healthcare analytics company that provides advanced insights for payers, providers, and employers. Their platform helps organizations reduce costs, improve care quality, and streamline claims analysis with data-driven intelligence while maintaining strict privacy and regulatory compliance.

Customer_Situation_KlearTrust

 

Customer Situation

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KlearTrust needed to accelerate how clinical documents, claims, and supporting evidence were analyzed across complex healthcare workflows. Manual review was slow, error-prone, and difficult to scale for high-volume customers. They required automated extraction of medical entities, summarization, and structured claim insights while ensuring complete HIPAA compliance. The environment included PHI-sensitive workloads requiring strict encryption, strong access controls, and detailed audit trails. 

KlearTrust also needed seamless integration with existing APIs, analytics pipelines, and identity providers. Any Agentic AI approach requires accuracy, safety, explainability, and predictable performance. Their challenge was to build an autonomous, highly governed AI framework capable of handling medical documents while maintaining trust, transparency, and operational assurance.

Our Approach

Mactores designed a modular Agentic AI architecture using AWS Bedrock for reasoning, extraction, and summarization. Lambda, Fargate, and Step Functions powered agents for retrieval, validation, and structured output generation. Comprehend Medical and OpenSearch enhanced entity detection and contextual retrieval. A HIPAA-aligned security model enforced private VPC endpoints, mTLS, granular IAM, short-lived credentials, and KMS encryption. Responsible AI layers added guardrails, PHI protections, output validation, and full explainability. CloudWatch, X-Ray, and OpenSearch delivered actionable observability.



Business Outcomes

KlearTrust reduced manual claim review time by over 60% while improving accuracy and operational consistency. Automated extraction and reasoning accelerated the delivery of high-quality insights for payers and employers. Claims teams gained reliable, explainable outputs with an audit-ready structure and documentation. The platform improved scalability, reduced operational effort, and enhanced customer trust through compliant and transparent AI.

Technical Outcomes

The system achieved low-latency inference using Bedrock models through secure VPC endpoints. Lambda, Fargate, and Step Functions provided scalable, fault-tolerant orchestration. IAM boundaries enforced zero-trust access, while KMS, mTLS, and encrypted storage protected PHI. Validation layers ensured accuracy, and OpenSearch-driven retrieval improved contextual reasoning. Unified observability enabled rapid debugging, drift monitoring, and ongoing optimization.
Getting_Started_KlearTrust

Getting 
Started

Mactores initiated discovery workshops to understand KlearTrust’s workflows, compliance needs, and integration points. Model benchmarking determined accuracy, cost, and latency across Bedrock options. Architectural design established secure connectivity, IAM controls, retrieval patterns, and validation logic. A pilot using synthetic PHI validated safety and performance. Mactores delivered production-ready IaC, documentation, governance controls, and training to support long-term adoption and continuous improvement.

 

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