My brother and I, our first venture was Nayya, which was a platform that worked like TurboTax in that it was designed to assist Americans in choosing their employee benefits during open enrollment. During Nayya

s development, we worked with several major insurers, and consulted them as data science thought partners. We got really good at helping them adjudicate claims, making it harder for the provider to actually get paid. So when we exited out of that business, we started studying the revenue cycle market, and became enamored with the idea of taking everything we learned with insurers and flipping it on its head to help provider groups run a more thoughtful, data-driven and automated revenue cycle. And that’s where the idea for Adonis was born.

When we first started the company, I met with dozens and dozens of CFOs at mid-market healthcare roll-ups, and I think that was pretty eye-opening for where things are going. The average healthcare group is paying anywhere from 6%-8% of their topline on medical billing costs, both people and technology, but they are only collecting 87% to 90% of the revenue they deserve. And none of the CFOs that we met could tell me a good story about how they were leaking over 10% of their top line.

Putting our data engineering hats on, we realized that the average healthcare group has three or four different systems of record that houses part of the claims lifecycle: there are EHRs like Athena and eClinicalWorks, a practice management system that could be the same as the EHR, or could be a system like NextGen or Waystar, then there’s a clearinghouse like Change Healthcare, and finally the data the lives within all of the different payer portals. So what we began doing was coming in and stitching together all of this data and running benchmark analysis on top of it and anomaly detection, to try to give our customers a view into where things are breaking down, where they are leaking revenue, and giving them ideas on where they can improve their operations.

After doing that in a more consultative way, we decided to productize it into our first flagship product, Adonis Intelligence. Essentially, it integrates into all of these systems of record, centralizing the data stream and adding a monitoring and alerting layer for when things break down and there are changes in the way payers adjudicate claims. We’re not only doing that for each customer, but there are some network effects where every customer we add to the platform, we’re picking up changes to the rules faster.

In the broader tech or VC world, we’re likened to a Datadog for the healthcare revenue cycle– we’re like the first line of defense to detect blind spots that otherwise CFOs and revenue teams might miss. And even for the bigger, more sophisticated groups that already have PowerBI and Tableau installed, they still have to know what questions to ask of the data and how to manipulate it. What we bring to the table is we’re already crawling the data, understanding it, looking for trends and anomalies, and we’ll alert you on it. We’re the “check engine light” for the revenue cycle.

That was our first product. We were deploying it and helping our customers identify blind spots, then they would go back to the practice management system and update the scrubbing logic, or update the way they are submitting claims. We got feedback from these customers that they wanted us to go beyond giving us advice and alerts, they wanted us to automate the fixes, so that inspired our second product: Adonis Billing.

The thesis behind that product is that we can take everything we learned in building our Intelligence product, and apply it to the actual claims submission and denials management processes. At a high level, the scope of services done isn’t different from traditional practice management services, but we’re differentiated by bringing in automation and machine learning in processes so we can submit claims automatically without having a human touch the claim, while seeing a very high first pass acceptance rate.

That’s one example. Additionally, our algorithms detect issues like an insurer denying specific ICD/CPT code pairs more frequently, which can indicate a change in their contractual rules. We then alert the revenue cycle team to act accordingly, like including additional medical documentation or authorization requests for future claims. If a provider experiences multiple denials, we flag it to relevant teams to resolve any contractual issues. Also, we identify underpayments by insurers, assisting with the detection and automatic filing of appeals. Our role is to provide the first line of visibility into any abnormalities within the revenue cycle.

To deploy our platform, we need a core dataset comprising the provider roster, the contracts our customers have, and ideally at least 24 months of prior claims data. We also track every new claim and monitor the feedback from the insurer.

Mapping these different data streams, each with their unique models, is a significant challenge. We tackle this using ETL pipelines, data mapping, and internal tools leveraging machine learning to align our customer

s data with our required format, which speeds up deployment.

Provider contracts and fee schedules often come in the form of lengthy, disorganized PDFs. We utilize OCR technology to extract essential details from these documents. The data ingestion process isn

t glamorous, but it is essential to organizing the data appropriately. Through automation and establishing repeatable processes, we simplify the task of mapping the necessary data for our customers.

Many revenue cycle companies created between 2017 and 2020 were venture-backed and tailored their products specifically for the digital health space, anticipating continual growth and influenced by early players such as Headway, Headspace, and Cerebro. This sector saw a boost during the pandemic years, but it has since slowed down, and the total addressable market (TAM) is limited, with less than 2 dozen companies in digital health generating more than $50 million in revenue.

Digital health companies have technical teams capable of integrating with APIs from vendors like Stripe. However, our focus is more on mid-market provider groups that usually don

t have software engineers, BI setups, or any advanced technology beyond basic services, and are plagued by high denial rates without adequate support, aside from what service organizations like R1 provide.

Our expertise lies in serving this mid-market segment, which is willing to invest significantly in solving their revenue cycle problems, often potentially reclaiming mid-seven to eight figures in revenue. We offer solutions that deliver a strong ROI, like 1:4 or 1:5.

We do get a lot of reachout both from health systems and digital health providers, though. For instance, a large, venture-backed psychiatric group with over $200 million in revenue sought our services to handle advanced denial management after outgrowing their original solution. The market doesn

t have to be monopolistic. Similar to how Adyen offers a more enterprise-level service to businesses that have outgrown Stripe, there is room for multiple players to succeed in the revenue cycle management industry.

We evaluate three key metrics. First, we look at the net collection rate, which indicates the percentage of revenue they

re successfully collecting versus what they

re owed. There are complexities in determining what they deserve because of discrepancies between contracts and payer reimbursements, but the actual collection rate is clear-cut.

Second, we consider the cost to the customer to achieve that net collection rate, taking into account both technology and human resources. We analyze whether they

re spending a high percentage of their revenue on collections or if it

Lastly, the speed of payment has become more significant. We look at the accounts receivable turnover ratio, examining the duration it takes for customers to get paid, the number of denials they encounter, and overall, how quickly they

re receiving reimbursement from insurers.

Our Intelligence platform tracks these metrics, and during quarterly business reviews with our clients, we use this data to demonstrate the improvements and value Adonis has brought since their initial onboarding.

We tailor our sales and marketing approach depending on who we’re dealing with, taking into account the unique starting points and revenue cycle maturity of each customer. For example, two specialty clinics with private equity funding can show dramatically different levels of sophistication in their revenue cycle processes.

Our practice is to conduct an initial analysis for our clients and share insights based on our experience with similar healthcare organizations to outline potential improvements. We avoid promising specific net collection rates, recognizing that each business has its unique challenges, including things like contract or credentialing issues.

We aim to provide a return on investment for our clients within the first 12-24 months of using our software, which is priced within a range reflecting the anticipated improvements. In some cases, we have increased clients

 net collection rates by 8% to 9%. For clients already performing exceptionally well, even a modest 50-basis-point increase or a reduction of three full-time employee equivalents can make a significant difference in driving EBITDA.

I think there are three key components. First, the technology integration, which is primarily our responsibility, once we have access to customer systems. Second, change management, which entails training and coaching the customer

s staff, like revenue cycle or finance teams, on workflow adjustments and our new solution offerings. Third, the transition involves managing vendor displacement, ensuring smooth handoffs and cutoffs during the switch.

Internally at Adonis, the setup phase is crucial, as is our collaboration with our customers and any third-party vendors they might have used previously. The typical go-live estimate ranges from 6 to 8 weeks, but we can adjust the timeline as needed to make the process work well and ensure we can deliver a healthy ROI. Sometimes external factors, such as state-specific EDI enrollment, can extend the timeline.

Our priority is to shoulder as much of the implementation burden as possible, allowing the customer to engage minimally, which addresses a common barrier in enterprise sales—internal bandwidth constraints. By minimizing the required effort from our customers, we’re trying to eliminate that concern for folks.

We consider them two distinct product lines. Depending on the customer

s needs, we may offer both simultaneously or introduce one first and consider an upsell later. It

s important for us to tailor our approach, ensuring we provide solutions that match the customer

Typically, if a healthcare organization is seeking an improved practice management (PM) system, we introduce Adonis Billing. However, Adonis Billing inherently includes Adonis Intelligence, as it cannot be deployed independently. So, effectively, when we sell Billing, Intelligence is part of the package.

We see ourselves as a software company, with the goal of creating best-in-class revenue cycle management software. With that being said, it’s important to not lose the pulse of the market, which still is sometimes looking for a full end-to-end provider that can be both software and services. Because we don’t see ourselves as the best billing services team, we’ve partnered with a number of groups that can help us do that, so we now have a number of marquee partners on the services side where we can join forces so that we provide the software and they provide billing and add-on services. So there are a number of contracts we have that might read and look like we’re a services company, but we’re able to partner with great folks so we can focus on what we do best, which is building and deploying software.

Potential customers should recognize that even the most efficient revenue cycle teams have the potential to optimize their finances further. Adonis

s Intelligence product uncovers hidden opportunities to increase revenues—even by small percentages—across various areas such as claim denials, underpayments, and contractual adjustments. While providers are aware of these issues, pursuing them hasn

t been cost effective in the past, but our solution makes it feasible.

We empower healthcare groups to leverage AI and machine learning, and we

re observing a significant interest in applying AI first to revenue cycle management. Adonis is positioned to support the healthcare industry

s focus on reducing staffing expenses and increasing the speed of revenue collection. We anticipate that the coming years will emphasize these efficiency gains, and we

re focusing on leveraging our AI capabilities to tackle the issue of denials in healthcare billing. Traditionally, we

ve concentrated on automating the appeals of denials, which has proven effective. Looking ahead to the next 12 months and toward our 2025 goals, we aim to advance our system so that it can prevent denials before they occur by being predictive. The core of our roadmap is about becoming smarter at identifying issues earlier in the revenue cycle.

Adonis Intelligence provides actionable alerts, predictive analytics, and real-time reports to optimize revenue outcomes. The system integrates with EHR, billing, and practice management systems to enhance insights on collections, accounts receivable, claim denials, and payer performance. The platform helps healthcare organizations identify revenue roadblocks, prevent denials with real-time alerts, and improve cash flow with smart prioritization and comprehensive KPI tracking.

Adonis Intelligence provides actionable alerts, predictive analytics, and real-time reports to optimize revenue outcomes. The system integrates with EHR, billing, and practice management systems to enhance insights on collections, accounts receivable, claim denials, and payer performance.

Adonis Intelligence

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