AI-powered validations streamline claim intake by detecting missing or invalid HL7 data upfront, eliminating manual Excel workflows and reducing errors. By leveraging OCR and automation, data is captured directly from diverse formats, applies configurable rules, and auto-corrects common issues, reducing manual intervention and improving accuracy. With bulk claim actions, and payer-specific flags, the portal ensures consistent formatting, faster review cycles and cleaner claims.

AI instantly connects to payer systems to verify insurance benefits at the point of appointment creation, eliminating delays and reducing denials caused by eligibility errors. Through automation and real-time data integration, benefit checks run seamlessly in the background, applying configurable rules to ensure coverage details are accurate upfront — resulting in fewer claim reworks and stronger financial clarity.

When insurance information is missing, inactive, or outdated, AI automatically searches payer databases to uncover active coverage. Automated discovery workflows reduce manual effort, speed the identification of valid coverage, and ensure eligibility data flows directly into claims processing, helping providers to reduce missed reimbursement opportunities, improve first-pass claim rates, and capture revenue that might otherwise go uncollected.

AI automatically derives CPT and ICD-10 codes using payer- and provider-specific rules, eliminating manual entry errors and improving compliance. With agentic automation, coding tasks are executed autonomously, applying rules, validations, and edits without constant human oversight, which streamlines workflows and reduces backlogs. Combined with real-time validations, modifier management, and batch claim processing, it delivers greater coding accuracy and processes higher volumes of claims per hour.

AI ensures every service provided is accurately captured and billed by reconciling clinical data against billing rules. By combining automation with machine learning, the system continuously identifies patterns in charge capture, flags potential gaps, and applies compliance-centric edits without manual intervention. This prevents revenue leakage, improves accuracy over time, and helps organizations safeguard revenue integrity while streamlining reimbursement.

AI streamlines the end-to-end appeals process by automating creation, collation, and submission of appeal packets, while predictive analytics identify the best strategies for success. With real-time tracking and automated workflows, it improves accuracy, speeds up resolution, and reduces manual effort.

This AI-powered solution leverages advanced models and agentic automation to autonomously handle claim status inquiries, EOB requests, payment confirmations, and prior-authorization checks. The system delivers natural, human-like conversations and continuously improves through machine learning, reducing the burden of manual calls and freeing staff to focus on higher-value work.

AI automates the processing of correspondence received into the lockbox, separating claim-related and non-claim communications and converting paper EOBs into electronic 835s. With OCR, rule-based validation, and agentic automation that autonomously routes, validates, and allocates work, the system ensures 100% capture of all payments and denials while cutting processing time and achieving greater accuracy. This reduces manual effort, accelerates reconciliation, and improves revenue integrity.