Revenue cycle · Denials & AR automation
SuperDial
SuperDial makes voice AI agents that call insurance companies for billing teams to verify benefits, check claim status, and follow up on prior authorizations and denials, with a human call center as backup.
Strong fit
- Billing companies and provider groups whose staff spend hours on payer phone calls
- Dental support organizations and management services organizations with large claim backlogs
- Teams that want benefits checks, claim status, and prior authorization calls handled by voice AI
Weak fit
- Practices whose payers answer most questions through portals or electronic transactions
- Buyers who want denial analytics and appeals from the same vendor
- Buyers who need a published price list
Bottom line
SuperDial makes voice AI agents that place the phone calls billing teams make to insurance companies, such as benefits verification, claim status checks, prior authorization, and denial follow-up, and it writes the results back to the billing system. A human call center takes over when an agent cannot finish. It fits billing companies and provider groups whose staff spend much of the day on hold.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Sam Schwager and Harrison Caruthers, classmates at Stanford University, first ran SuperBill, a billing company that helped specialty practices get paid. Its staff spent thousands of hours on calls to insurers, so they built an internal tool to automate those calls, and in late 2023 they turned it into SuperDial. Its agents navigate phone menus, wait on hold, and talk with insurer representatives to verify benefits, check claim status, follow up on prior authorizations and denials, and handle credentialing calls. When an agent cannot finish a call, SuperDial's own call center steps in, and the results are written back to the customer's billing system.
In 2025 SuperDial bought MajorBoost, which had built technology for navigating insurer phone menus, and raised a $15 million Series A led by SignalFire. It had handled more than one million calls by mid-2025. West Coast Dental uses it for more than 10,000 claim status calls a month.
Compare Adonis when a health system wants denial analytics and AI agents together, Enter Health when a group wants AI across the billing cycle, and MD Clarity when underpayment recovery is the main goal.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Adonis | 7.8 | 7.6 |
| Enter Health | 7.3 | 7.0 |
| SuperDial | 7.2 | 7.0 |
| MD Clarity | 6.9 | 6.7 |
Pricing
| Item | Detail |
|---|---|
| Model | Custom quote from SuperDial. |
| What usually drives cost | Call volume, which call types are automated, and integration with the EHR or billing system. |
| What to ask in diligence | The price per completed call, what happens to calls the AI cannot finish, and setup costs for system integration. |
| Published pricing | No public list price. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get SuperDial to function | |
| Billing manager who owns payer calls | Decides which calls to hand over. |
| List of call types and volumes | Shows where automation pays off. |
| EHR or billing system access | Results are written back. |
| Payer phone scripts and needed fields | Agents need to know what to ask. |
| Baseline calls per biller per day | Shows productivity change. |
| What will maximize your value | |
| Start with claim status calls | Most repetitive and lowest risk. |
| Route freed time to appeals | Staff work higher-value claims. |
| Audit a sample of call results | Keeps data quality high. |
| Deal-breakers | |
| Few payer phone calls | |
| No system write-back path | |
| Wants denial analytics first | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Contract and call types chosen | 2-4 weeks |
| 2 | First call types live | 1-2 months |
| 3 | More call types and integrations | 3-6 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.