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Case Study

RiskCorrect

Helping RiskCorrect Streamline Claims Management Through AI-Powered Workflow Automation

The Client

RiskCorrect is a data-driven workers’ compensation and claims management platform that helps employers reduce administrative complexity, improve claim oversight, and make more informed decisions across the claim lifecycle.

By centralizing claim data, documentation, and operational workflows into a more connected system, RiskCorrect helps organizations reduce costs, improve consistency, and create better outcomes for both employers and employees.

 

 

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Business-Execution

Summary

TYPE OF PRODUCT

AI-Powered Claims Management Platform

BUSINESS VERTICAL

Insurance, Risk Management & Workers’ Compensation

TECH USED

Amazon Bedrock, Claude, AWS Lambda, AWS Fargate, Amazon S3, Aurora PostgreSQL, CloudWatch, X-Ray

SERVICE
Full-service Development Team—

Solution Architecture, Delivery Management, AI/Data Engineering, DevOps Engineering, Quality Assurance

DESCRIPTION

RevStar implemented a GenAI-powered claims summary automation framework on AWS designed to streamline claims workflows, reduce manual documentation effort, and improve operational consistency across internal and client-facing reporting.

RESULTS

Enabled scalable AI-powered claims processing workflows that reduced manual summary effort, improved reporting consistency, and supported measurable cost-saving opportunities for RiskCorrect and its clients.

We’ve reduced about 20 hours of labor a week while significantly improving the accuracy of our data. For our clients, we’re seeing realistic 15–20% decreases in premiums. You’re talking about millions of dollars in savings.
Tobin Robeck CEO, RiskCorrect

The Challenge

RiskCorrect’s growing claims operations required a faster and more scalable way to manage claim documentation, operational workflows, and client reporting across both Workers’ Compensation and non-Workers’ Compensation claims.

Internal teams were spending significant time reviewing claim notes, claim fields, and lengthy email threads in order to manually create claim summaries. The process created operational bottlenecks, inconsistent reporting quality, and unnecessary administrative overhead as claim volumes increased.

At the same time, RiskCorrect needed a more intelligent and standardized way to support both internal claim strategists and external client-facing reporting workflows without increasing operational headcount.

The Problem

RiskCorrect needed a scalable and intelligent way to automate claim summary generation, reduce manual administrative effort, and improve consistency across internal and client-facing claims workflows.

The Solution

We modernized RiskCorrect’s claims operations through a secure AWS-native GenAI framework designed to automate claim summary generation, improve reporting consistency, and streamline operational workflows at scale.

The solution leveraged Amazon Bedrock and serverless AWS infrastructure to process structured and unstructured claims data, including claim notes, operational records, and long-form email threads. By automating summary generation workflows, RiskCorrect reduced manual documentation effort while improving the speed, consistency, and reliability of operational reporting across the platform.

Designed with scalable cloud-native architecture and human-in-the-loop validation workflows, the platform established a flexible foundation for future AI-driven automation, intelligent claims processing, and operational decision support capabilities.

 

What We Built

To support RiskCorrect’s operational modernization goals, we built a secure AWS-native GenAI claims automation framework powered by Amazon Bedrock, serverless infrastructure, and AI-driven summary generation workflows.

To meet project requirements, we deployed a scalable event-driven architecture using Amazon Bedrock, AWS Lambda, AWS Fargate, Amazon S3, and Aurora PostgreSQL. The solution introduced AI-powered claims summary generation, automated workflow orchestration, and intelligent processing capabilities designed to reduce manual documentation effort, improve reporting consistency, and support scalable operational growth across the platform.

The Team

We have a growing cost-effective hybrid team, with onshore Product Managers and developers from our build center in Colombia. Operating three parallel teams for different products and support.
Two people collaborating at a whiteboard covered with sketches and sticky notes, planning a project layout.

Results

RevStar helped RiskCorrect significantly reduce manual claims documentation effort while improving operational consistency, reporting accuracy, and workflow efficiency across the platform. By implementing scalable GenAI-powered automation workflows, RiskCorrect established a stronger operational foundation capable of supporting faster claims processing, measurable client savings, and long-term AI-driven growth.

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