Prior authorization & UM · Health plan prior auth AI
Banjo Health
Banjo Health provides AI-assisted prior authorization, appeals, and clinical criteria automation software for health plans and PBMs. It is plan-side UM software, not a provider EHR.
Strong fit
- Health plans and PBMs that want AI to accelerate prior auth, appeals, and clinical criteria workflows
- UM leaders comparing no-code criteria automation rather than only outsourcing more nurse reviewers
- Buyers that need prior auth case management with composer-style decision trees
Weak fit
- Provider groups shopping only for portal-side prior auth submission tools
- Plans that have already standardized on a single incumbent UM suite with no budget for another AI layer
- Buyers that need specialty lab PA transparency rather than plan-side adjudication
Bottom line
Banjo Health earns a Recommend on the health-plan side of our prior authorization board. The Washington, DC company sells AI-assisted prior auth, appeals, and grievances tooling with a no-code clinical criteria composer aimed at plans and PBMs. Third-party estimates put revenue near $3-4M with under 50 employees after early venture rounds, inside the prefer band and earlier-stage than Agadia or Xsolis. Implementation is a UM operations and criteria-coding project. Pricing is enterprise SaaS. Score sits below Xsolis on AI prediction maturity and above thinner point tools when plan-side PA workflow plus criteria automation is the buy.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Banjo Health is a Washington, DC prior authorization software company founded by CEO Saar Mahna. Health plans and PBMs use BanjoPA and related modules to run authorization cases, appeals, and clinical criteria as decision trees rather than only stacking more manual nurse review.
It is plan-side UM software, not a provider portal bot and not a full core claims system. Compare Agadia when you need a mature PBM/health-plan PAHub suite, Xsolis when AI utilization prediction is the center of the buy, and Careviso when the problem is lab and specialty patient-access PA on the provider/lab side.
Score reflects focused PA workflow automation for plans, with earlier commercial scale than the largest UM incumbents.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Xsolis | 7.8 | 7.3 |
| Infinitus | 7.7 | 7.4 |
| Agadia | 7.6 | 7.2 |
| Careviso | 7.6 | 7.3 |
| Silna Health | 7.3 | 7.1 |
| Banjo Health | 7.2 | 7.0 |
| Hindsait | 7.0 | 6.7 |
Pricing
| Item | Detail |
|---|---|
| Model | Enterprise SaaS for prior auth, appeals, and grievances; quote-based for plans and PBMs. |
| What usually drives cost | Authorization volume, modules (PA, appeals, composer), and integration scope. |
| What to ask in diligence | Annual platform fee at your PA volume, including criteria migration and training. |
| Published pricing | No public list price. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Banjo Health to function | |
| UM clinical criteria owners | Composer stalls without criteria owners. |
| PA volume baseline by specialty | You cannot size ROI without volume. |
| Integration path to UM systems | Cases need a system of record. |
| Appeals and grievances process map | Modules fail without process clarity. |
| Compliance review for AI assist | Plan AI needs policy approval. |
| What will maximize your value | |
| Pilot high-volume specialty first | All-specialty day one multiplies exceptions. |
| Measure turnaround and uphold rates | Prove UM value. |
| Codify gold-card exceptions early | Ad-hoc exceptions break automation. |
| Deal-breakers | |
| Provider-portal PA tool only | |
| No UM staff to operate | |
| Core claims rip-and-replace required | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Criteria and process workshops | 4-8 weeks |
| 2 | Pilot specialty | 6-12 weeks |
| 3 | Scale | 8-16 weeks |
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.