Summary

A third-party billing agency supporting emergency medical services providers was straining under the rising volume and complexity of Electronic Data Interchange (EDI) 835 transactions, processing roughly 85,000 payments each month. Manual handling created bottlenecks because even simple cases required human review, while complex payer-specific rules slowed the entire operation. Marlabs designed and implemented an AI-powered automation pipeline that intelligently ingests, verifies, and posts payments while flagging exceptions for human intervention. The result was a more scalable, accurate, and efficient revenue cycle operation, with the majority of payment posting automated and posting errors sharply reduced.

Challenge

The client, a third-party billing agency serving emergency medical services providers, faced growing operational pressure from the increasing volume and complexity of EDI 835 transactions. Every month the agency processed about 85,000 payments, and the lack of scalable automation made it difficult to keep pace with daily demand while maintaining accuracy across varying payer-specific rules. Manual processing bottlenecked the work: even simple cases required human review, and complex payer logic slowed efficiency across the board. The absence of a framework that could handle both standard and payer-specific scenarios left staff absorbing an unsustainable manual burden and created a persistent drag on denial resolution, reconciliation, and financial reporting. The client needed a scalable, AI-powered automation framework that could reduce manual workload, increase processing speed, and support both standard and payer-specific logic without sacrificing compliance.

Solution

Marlabs delivered an AI-driven solution that automated the daily ingestion, verification, and posting of EDI 835 payment files. The platform used large language models to intelligently apply payer-specific rules, including those for Medicare, and to route exceptions for manual resolution, enhancing scalability while preserving compliance and accuracy. The work was structured across four phases.

Discovery and process mapping

Marlabs began by mapping the client's current EDI workflows and pain points to identify where automation could deliver the most value. The team analyzed transaction volume, took inventory of the payer rules that governed processing, and documented the manual tasks that were consuming staff time. This groundwork established a clear picture of which scenarios were routine and which demanded specialized logic, setting the direction for the rest of the build.

Data ingestion and storage automation

Next, Marlabs automated the ingestion of daily EDI 835 files from the client's internal systems into cloud-based storage. The team configured secure file transfers and cloud object storage, then normalized the incoming data so it was consistent and ready for automated processing. This phase removed the manual handoffs that had slowed the front end of the workflow and created a reliable foundation for verification and posting.

AI-driven verification and payment posting

With clean data flowing in, the team deployed large language models to cross-check data accuracy and automate the posting of clean transactions. The AI applied payer-specific rules, classified payments, and tracked reconciliation so that straightforward cases moved through without human touch. This is where the bulk of the manual effort was eliminated, allowing staff to focus on the cases that genuinely needed judgment.

Exception management and monitoring

Finally, Marlabs implemented logic to flag exceptions for manual processing with clear explanations of why each case was routed for review. The team enabled real-time monitoring, feedback loops, resolution suggestions, and dashboards and alerts so the client had continuous visibility into exceptions and resolution metrics. This closed the loop between automation and human oversight, ensuring that edge cases were handled quickly and that the system kept improving over time.

Results

Marlabs' solution enabled the client to move beyond unsustainable manual processes and embrace intelligent automation for EDI 835 transactions. The initiative freed up staff by automating, conservatively, 85 percent of payment posting processes, and it reduced posting errors by 95 percent while improving reconciliation across systems. Denial resolution accelerated as turnaround time for resolving payer-specific issues shortened, and real-time tracking of exceptions and resolution metrics gave the client operational visibility it had never had. By reducing manual workload, accelerating denial resolution, and improving payment accuracy, the client positioned itself for long-term efficiency in revenue cycle operations. The project also established a scalable framework for handling increasing transaction volume and laid the groundwork for expanding AI use across similar transactional workflows, all within a governed approach aligned to payer-specific compliance requirements.

Impact

Marlabs' AI-powered automation delivered measurable gains across the client's revenue cycle operations:

• Automated, conservatively, 85 percent of payment posting processes, freeing staff from repetitive manual work.

• Reduced posting errors by 95 percent and improved reconciliation across systems.

• Shortened turnaround time for resolving payer-specific denial issues.

• Established a scalable framework for handling increasing transaction volume.

• Enabled real-time tracking of exceptions and resolution metrics for greater operational visibility.

• Applied AI within a governed framework aligned with payer-specific compliance requirements.