Revenue cycle · AI denial appeals for hospitals
Cofactor AI
Cofactor AI is software for hospital revenue cycle teams that reads denied claims and patient records, ranks the denials and drafts appeals with supporting evidence.
Strong fit
- Hospitals with large backlogs of clinical denials
- Revenue cycle teams that want AI to draft appeals
- Buyers who prefer paying from money recovered
Weak fit
- Buyers who want a vendor with a long track record
- Physician practices with few denials
- Buyers who want a posted price
Bottom line
Cofactor AI is a conditional choice for hospitals that want AI to sort denials and draft appeals, paid from the results. One customer reports strong overturn rates, but the company started in 2023 and has few public references. Buyers should run a pilot on their own denials first.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Adi Tantravahi and Juan Sebastian Lozano founded Cofactor AI in Chicago in August 2023. Tantravahi is chief executive and Lozano is chief technology officer.
Cofactor connects to a hospital's EHR and clearinghouse, reads each denial and the patient record, and checks the claim against coding rules, clinical guidelines, payer policies and the hospital's contract. It ranks denials by value and drafts an appeal letter with the supporting evidence for staff to review and send.
Cofactor also flags underpayments against contract terms and works on denial prevention and prior authorization. Its pitch is that a single staff member can review and appeal many denials in minutes a day.
Cofactor says Memorial Health System cut appeal work from more than 60 minutes a case to 15 to 20 minutes, won 35% of appeals against an industry rate of 17%, and saw five dollars back for every dollar spent. UT Southwestern Medical Center began using it in January 2026.
Cofactor raised a $4 million seed round led by Drive Capital. It is a young company with a small team, so buyers should test it on a sample of their own denials before a wider rollout.
Adonis fits provider groups that want AI to work denials and claim follow-up. Janus Health fits hospitals that want to automate revenue cycle tasks in Epic, and Sift Healthcare fits teams that want payment and denial analytics.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Adonis | 7.8 | 7.6 |
| Sift Healthcare | 7.1 | 6.8 |
| Janus Health | 6.9 | 6.9 |
| Cofactor AI | 6.7 | 6.6 |
Pricing
| Item | Detail |
|---|---|
| Model | Cofactor AI charges on outcomes, such as revenue recovered, denials avoided and authorizations approved, with no seat or API fees. |
| What usually drives cost | For Cofactor AI, cost depends on the money it helps recover and the modules you use for denials, underpayments and prior authorizations. |
| What to ask before you buy | Ask Cofactor AI what share of recovered money it takes, how it counts a win, and which hospitals like yours you can call. |
| Published pricing | Cofactor AI does not publish a price list. |
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.