Revenue cycle · Denials & AR automation
Adonis
Adonis makes revenue cycle software for hospitals and medical groups: Adonis Intelligence finds the causes of denied and underpaid claims, and its AI agents check claim status, research denials, and prepare appeals.
Strong fit
- Health systems and large physician groups with high denial and underpayment volumes
- Revenue cycle leaders who want to see the causes of denials across payers and act on them
- Billing teams that want AI agents to check claim status and prepare appeals
Weak fit
- Small practices that want a simple billing service
- Buyers who want a clearinghouse or patient payment tool
- Buyers who need a published price list
Bottom line
Adonis, based in New York City, makes revenue cycle software for hospitals and medical groups. Adonis Intelligence finds the causes of denied and underpaid claims, and its AI agents check claim status, research denials, and prepare appeals. It fits health systems and large groups that want to recover more of what payers owe them without adding billing staff.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Brothers Akash and Aman Magoon founded Adonis in 2022 after building Nayya, an employee benefits company. Working with insurers there, they saw how much insurers spent on software that denies claims, and they started Adonis to give providers similar tools.
Adonis connects to a provider's billing systems and clearinghouse data. Its analytics product, Adonis Intelligence, shows which payers and rules cause denials and underpayments, so a revenue cycle team knows where to act first. Its AI agents then do the follow-up work, such as checking claim status, researching denials, and preparing appeals.
Customers include Mount Sinai Health System, Baptist Health South Florida, AdventHealth, and ApolloMD, an emergency medicine group. Adonis raised a $31 million Series B led by Point72 Private Investments in June 2024 and a $40 million Series C led by Quadrille Capital in March 2026, bringing total funding past $95 million.
Compare SuperDial when the main job is taking payer phone calls off the team, Enter Health when a group wants AI across the billing cycle with a prior authorization assistant, and MD Clarity when underpayment recovery and patient estimates matter most.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Adonis | 7.8 | 7.6 |
| Enter Health | 7.3 | 7.0 |
| SuperDial | 7.2 | 7.0 |
| Sift Healthcare | 7.1 | 6.8 |
| MD Clarity | 6.9 | 6.7 |
| Janus Health | 6.9 | 6.9 |
Pricing
| Item | Detail |
|---|---|
| Model | Custom contract quoted by Adonis. |
| What usually drives cost | Claim volume, the number of entities and billing systems connected, and which AI agent workflows are turned on. |
| What to ask before you buy | Whether the fee is fixed or tied to the money recovered, what setup costs, and how results are measured. |
| Published pricing | Adonis does not publish a price list. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Adonis to function | |
| Revenue cycle leader who owns denials | Someone has to act on what the analysis finds. |
| Access to billing system and remittance data | The analysis needs both. |
| Baseline denial and underpayment rates | It lets you measure the improvement later. |
| Staff to review agent work at first | Checking their work early builds trust in the agents. |
| Payer contract terms | Underpayments are measured against what the contracts say. |
| What will maximize your value | |
| Fix the top denial causes first | A few rules cause most of the losses. |
| Start agents on claim status checks | It is high-volume work with little risk. |
| Review results with payers monthly | Patterns in the results give you evidence for conversations with payers. |
| Deal-breakers | |
| No access to billing data | |
| You want patient payment tools, not denial work | |
| No one owns denials | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Contract and data connection | 1-3 months |
| 2 | Dashboards and first agents live | 2-4 months |
| 3 | More agent workflows and entities | 6-12 months |
Leadership
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.