The platform
that outperforms
Technology that breaks through conventional barriers and empowers your team to drive a higher standard of revenue cycle performance.
Leading the way to an autonomous revenue cycle
For too long, RCM leaders have been stuck in a cycle of reactive management – always playing catch-up and manually creating and reviewing data. Auxo provides critical insights, highlights root causes and bottlenecks, and recommends effective strategies in seconds.
AI-native technology
AI is built into Auxo – not on top of it – to enable long-term capabilities.
Agentic AI
Automates routine tasks like claim status follow-up, appeal letter generation, and coding triage.
Generative AI
Helps your teams identify trends, guide work, and recommend next steps.
solutions is built on a single platform.
See Auxo in Action
Real results
- 33% reduction in A/R days
- 38% reduction in A/R > 90 days
- 5.5% increase in cash collections
Frequently asked questions
What is an Autonomous Revenue Cycle?
An autonomous revenue cycle is an advanced, AI-driven approach that uses AI, machine learning, and agentic technology. It plans, prioritizes, and executes work with minimal human intervention, using AI to continuously adapt based on payer behavior, outcomes, and system signals. We’re evolving from people managing workflows to technology managing workflows and people managing strategy.
How does Auxo use Generative AI in revenue cycle?
In RCM, generative AI can produce faster documentation, clearer interpretation of payer rules, or reduced roadblocks between people and the information they need to do their jobs well. In other words, generative AI improves how work gets done, but not how work moves. Auxo’s AI Advisor utilizes generative capabilities to automate three specific functions:
- Identify trends and anomalies (what is happening?)
- Determine root cause (why is it happening?)
- Develop AR strategies (what should I do?)
By offering curated prompts, Auxo delivers essential insights, identifies underlying bottlenecks, and suggests tactical improvements instantly—eliminating the hours of manual analysis typically required.
How does Revology use Agentic AI in revenue cycle?
Agentic AI can autonomously make decisions and execute complex end-to-end processes. This isn’t AI that only responds to prompts, it is built to pursue defined goals. It can plan, sequence actions, interact with multiple systems, and adjust according to outcomes — all with limited intervention.
What is AI-Native Technology?
AI-Native technology is AI that is built into the product from the beginning versus added later. AI is embedded at the data, workflow, and decision layers, not bolted on as a feature. We didn’t add AI to the platform — we built the platform around AI.
What is Revology's Approach to AI in revenue cycle?
Revology integrates specialized revenue cycle expertise into a robust insurance management platform. By leveraging AI-enabled intelligence, we automate essential back-office processes to achieve a high-performing autonomous revenue cycle across the enterprise.
How Should RCM leaders think about adopting and implementing AI?
AI in revenue cycle is no longer a futuristic concept – it’s a strategic imperative. Payers have already adopted and are using AI, particularly for denials. Healthcare organizations that adopt adaptive, agentic AI systems as urgency grows will be better positioned to keep pace of – and get ahead of payers.
Will AI replace revenue cycle staff?
No. The goal of AI in revenue cycle management is to clarify where expertise – human expertise – matters most, shifting effort from repetitive tasks to high-value work that drives individual and organizational growth
Interested in Next Generation Technology for RCM?
Request a DemoAuxo for Insurance Follow Up
Our powerful Insurance Follow-Up solution helps you collect more cash by making sure the right claim is worked by the right person at the right time and in the right way.
We transformed our solution into an AI-powered autonomous engine that thinks, learns, and acts like your best RCM expert.