Prior authorization & UM · Voice AI for access calls
Infinitus
Infinitus builds AI agents that automate healthcare phone and digital work for benefit verification, prior authorization follow-up, appeals, and related access tasks. It is not a payer clinical utilization-management determination suite.
Strong fit
- Drug company patient support, specialty pharmacy, and provider teams buried in phone calls to payers
- Organizations that track how fast benefit checks and prior authorization follow-ups get done
- Groups that need AI agents that can wait on hold and return organized call results
Weak fit
- Health plans building their own system to make utilization management decisions
- Clinics with almost no insurance follow-up by phone
- Buyers who will not change how staff handle unfinished cases after AI calls end
Bottom line
Infinitus is worth using for organizations whose prior authorization and benefit verification work stalls on hold with payers. It suits teams that will own what happens when a call needs a person. Health plans and hospital utilization review teams that need a full decision platform should look at Agadia or Xsolis.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Infinitus builds AI agents that automate healthcare phone calls and online outreach. They handle benefit checks, prior authorization follow-up, appeals, and related work to get patients access to care.
Its voice AI is built for healthcare. It is not a payer platform for utilization management decisions like Agadia.
Customers see patients start treatment sooner and fewer staff hours spent on repetitive calls. The evidence for its call automation is strong.
Setup needs a clear owner for handoffs when a call has to go to a person. Prices are quoted for each customer.
Among prior authorization tools we review, Infinitus scores just below Xsolis and above Silna, though it solves a different problem focused on calls.
Silna suits specialty clinics that need help getting patients ready for care. Agadia or Xsolis suit plans or hospital review teams that decide patient status.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Xsolis | 7.8 | 7.3 |
| Infinitus | 7.7 | 7.4 |
| Humata Health | 7.7 | 7.4 |
| Agadia | 7.6 | 7.2 |
| Careviso | 7.6 | 7.3 |
| Silna Health | 7.3 | 7.1 |
| Itiliti Health | 7.3 | 7.1 |
| Banjo Health | 7.2 | 7.0 |
| Hindsait | 7.0 | 6.7 |
| Rhyme | 6.5 | 6.3 |
Pricing
| Item | Detail |
|---|---|
| Model | An AI platform for patient access and payment work, with quoted prices. |
| What usually drives cost | How many calls you make, which tasks (benefit checks, prior authorization status, appeals), and which channels are automated. |
| What to ask before you buy | The cost per completed task at your monthly call volume, and which cases are passed to a person. |
| Published pricing | Infinitus does not publish prices. Expect pricing based on volume. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Infinitus to function | |
| Named owners for benefit check and prior authorization follow-up work | AI calls with no one to handle problems delay treatment. |
| The payers and treatments covered in the first tasks | Too many kinds of calls hide quality problems. |
| A way to pass results into your CRM or work queues | Results that never reach staff do not change anything. |
| A compliance review of recorded AI calls | Automated healthcare calls need policy approval. |
| Rules for passing the call on when an AI agent cannot finish | Automation that leads nowhere angers patients and providers. |
| What will maximize your value | |
| Measure how soon patients start treatment and staff hours saved on hold | Those are the results Infinitus is bought for. |
| Start with a narrow set of high-volume call types | Broad first launches create many visible failures. |
| Review sample calls with clinical or patient access leads | Kindness and accuracy slip without review. |
| Send results into the queues staff use and trust | Separate screens get ignored. |
| Add related tasks only after service levels are steady | Adding work before it is reliable loses trust. |
| Deal-breakers | |
| You need a payer tool that makes utilization management decisions, not call automation. | |
| Almost none of your patient access work happens by phone or online outreach. | |
| Compliance will not approve AI phone calls. | |
| No one will own unfinished cases after automation. | |
| You expect no way to pass calls to a person. | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | From signing to kickoff | 2-6 weeks for security, compliance, and choosing tasks |
| 2 | From kickoff to first use | 4-10 weeks until the first automated call types are live |
| 3 | From first use to steady results | 2-4 months of adjusting before adding more treatments |
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.