News & Press Releases

MD Clarity Unveils Major Expansion of RevFind with Agentic AI to Optimize the Revenue of Provider Organizations

Published on 
September 30, 2026

MD Clarity, a leading provider of revenue optimization software, today announced the launch of a major expansion of its flagship RevFind product’s agentic AI capabilities. The expansion supercharges provider organizations’ ability to automatically detect underpayments and denials, uncover root causes, and surface and prioritize recovery opportunities, facilitating faster and more complete reimbursement. 

Underpayments and denials lead to significant revenue loss and create a substantial workload for provider organizations, with revenue teams often waiting on manual reports and data pulls before they can take action. RevFind addresses these challenges with agentic AI that gives teams continuous, self-service access to sophisticated audits of revenue data, paired with a faster way to make decisions from that data. Teams can ask questions in plain English and get answers, analyses, and visualizations directly from their data. The agent works on demand or in the background, monitoring new developments and flagging revenue uplift opportunities as they surface.

RevFind enables provider organizations to:

  • Uncover trends and root causes behind underpayments and denials
  • Ask questions in plain English and get answers and visualizations in seconds
  • Validate findings with visibility into the underlying methodologies
  • Automate dashboards and recurring payer performance reports
  • Prioritize opportunities and accelerate recovery across teams

“Finding an underpayment is only part of the challenge. Revenue cycle and finance teams still spend too much time figuring out what’s driving it and what to do next,” said Dan Freeman, Chief Executive Officer at MD Clarity. “RevFind brings agentic AI into that process, empowering teams to automate the investigation of revenue trends, easily identify root causes, and prioritize the opportunities that matter most.”

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