Muoro Built an AI System to Cut Denials by 60%

Our client, a healthcare technology firm, needed a secure and scalable platform to automate insurance denial handling. The core goal was to process complex EDI 835 files, analyze denial trends using AI, and deliver actionable insights through an accessible web portal.

Their ideal solution required:

• Automation to parse raw EDI 835 files

• A structured database to store and track denial data

• AI assistance to analyze trends and suggest actions

• A secure, domain-hosted web interface for hospitals

But they lacked the in-house backend expertise to build an intelligent system that could scale reliably and ensure compliance.

The mission was to reduce manual work and speed up denial resolution using automation and AI.

The platform needed to:

• Extract structured data from raw claim files

• Enable hospitals to upload and track denial statuses

• Provide AI-driven suggestions to resolve denials faster

• Offer secure access with real-time dashboards and deployment on a custom domain

To build the platform, the client had to overcome five key hurdles:

Manual parsing of healthcare claim files was time-consuming and error-prone.

Hospitals had no way to monitor denial statuses or access resolution history in one place.

Admins couldn’t easily track what was uploaded or view analytics without technical support.

The platform needed secure hosting, domain mapping, and authentication in a production environment.

The system lacked file-level visibility after upload, impacting trust and accountability.

Muoro deployed a team of backend engineers with deep expertise in file parsing, data architecture, and secure infrastructure.

We built a custom parser to extract structured data from EDI 835 files and store it in a scalable format.

• Automated EDI 835 parsing

• Structured output mapped to DynamoDB

• Logging and error capture for unmatched data fields

To store and manage large-scale denial records, we:

• Set up AWS DynamoDB for fast, reliable, and scalable data storage

• Enabled search and query capabilities across denial records

We integrated ChatGPT to analyze denial trends, offering:

• Natural language summaries of file content

• Suggested resolution strategies based on denial patterns

• Reduced manual analysis load for hospital staff

Our engineers ensured the solution was secure and production-ready:

• Deployed backend using AWS EC2, S3, and serverless architecture

• Configured domain mapping for direct hospital access

• Integrated user authentication and role-based permissions

• Built monitoring tools to track uploaded file data in production

The stack was optimized for scalability, speed, and intelligent automation.

1. Node.js – Backend logic, file handling, and API development

2. AWS DynamoDB – Fast, scalable NoSQL storage for denial records

3. ChatGPT API – AI integration for claim file insights and trend analysis

1. AWS EC2 & S3 – Hosting backend and static assets

2. Serverless Architecture – Minimal maintenance, high availability

3. Custom Domain Mapping – Secure, institution-specific access

The platform delivered meaningful results across operational and technical metrics.

AI summaries and structured data helped staff act quickly and reduce processing time.

Automated parsing replaced repetitive data entry and cleanup.

Hospital admins now have dashboards showing upload history, denial patterns, and live status.

Post-upload tracking ensures transparency and accountability across institutions.

Fully managed infrastructure supports long-term growth and secure multi-tenant access.

The client now operates:

• A domain-hosted portal for healthcare institutions

• A backend that processes complex EDI files with ease

• AI support to guide resolution strategies

• A real-time database for tracking denial resolution history

If your team handles large datasets like EDI files or claims records and needs intelligent automation, Muoro provides backend engineers with healthcare domain expertise to deliver real-time, scalable solutions.

Let’s talk.