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
- Pharma patient-support, specialty pharmacy, and provider teams drowning in payor phone work
- Organizations measuring benefit verification and prior-auth follow-up turnaround
- Groups that need AI agents that can wait on hold and return structured call results
Weak fit
- Payers building an internal clinical UM determination suite
- Clinics with almost no phone-based insurance follow-up burden
- Buyers who will not change how staff handle exceptions after AI calls complete
Bottom line
Infinitus earns a Recommend for organizations whose prior auth and benefit verification work still dies on hold with payors. Outcomes show up in faster time-to-therapy and fewer human hours on repetitive calls. Product strength is purpose-built healthcare voice AI rather than a full payer UM console like Agadia. Implementation needs workflow ownership for handoffs when calls escalate. Pricing is quote-based. Score sits just under Xsolis and above Silna on this board for a different, call-automation shaped problem.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Infinitus builds AI agents that automate healthcare phone and digital outreach for benefit verification, prior authorization follow-up, appeals, and related access work.
It is not a payer utilization-management determination suite. Compare it with Silna when specialty clinics need care-readiness ops, and with Agadia or Xsolis when the buyer is a plan or hospital UM team living inside clinical status workflows.
Score reflects strong outcomes evidence on call automation with less fit for full UM policy engines.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Infinitus | 7.7 | 7.4 |
| Xsolis | 7.8 | 7.3 |
| Agadia | 7.6 | 7.2 |
| Silna Health | 7.3 | 7.1 |
| SamaCare | 7.1 | 7.0 |
Pricing
| Item | Detail |
|---|---|
| Model | AI platform packaging for healthcare access and reimbursement workflows; quote-based. |
| What usually drives cost | Call volume, use cases (benefit verification, PA status, appeals), and channels automated. |
| What to ask in diligence | Cost per completed workflow at your monthly call volume, with human escalation coverage defined. |
| Published pricing | Public list price: not published. Expect volume-based platform pricing. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Infinitus to function | |
| Named owners for benefit verification and prior-auth follow-up queues | AI calls without human exception owners stall therapy. |
| Payor and therapy volume in scope for the first use cases | Unbounded call types hide quality problems. |
| Integration or handoff path into your CRM or work queues | Results that never reach staff do not change outcomes. |
| Compliance review of recorded AI calls | Healthcare voice automation needs policy clearance. |
| Escalation rules when an AI agent cannot finish | Dead-end automations anger patients and providers. |
| What will maximize your value | |
| Measure time-to-therapy and human hours removed from hold time | Those are the outcomes Infinitus is bought for. |
| Start with a narrow set of high-volume call types | Wide first launches create noisy failures. |
| Review call quality samples with clinical or access leads | Empathy and accuracy drift without review. |
| Feed structured results into the same queues staff already trust | Side consoles get ignored. |
| Expand to adjacent workflows only after SLAs stabilize | Scope creep before reliability burns trust. |
| Deal-breakers | |
| You need a payer clinical UM determination engine, not call automation. | |
| Almost none of your access work happens by phone or digital agent outreach. | |
| Compliance will not approve AI voice interactions. | |
| No one will own exception queues after automation. | |
| You expect zero human escalation path. | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Contract signed → kickoff | 2-6 weeks (security, compliance, use-case scoping) |
| 2 | Kickoff → first live workflow | 4-10 weeks for first automated call types in production |
| 3 | First live workflow → steady value | 2-4 months of tuning before broader therapy lines |
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 actually 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.