Patient engagement & telehealth · Patient intake & access
Tennr
Tennr makes referral intake software that reads faxed and emailed referrals with its own AI model, checks insurance eligibility and payer rules, and routes each patient to scheduling.
Strong fit
- Specialty practices that receive many referrals by fax, email, and portals
- Practices that lose patients between referral and first visit
- Groups that want insurance and payer rules checked before a referral is scheduled
Weak fit
- Practices that receive few referrals
- Health systems that want patient messaging across every department
- Buyers who need a published price list
Bottom line
Tennr, based in New York City, makes software that reads incoming patient referrals, pulls out patient and insurance details, checks eligibility and payer rules, and routes each referral to a scheduler. It fits specialty practices that receive many faxed referrals and lose patients before the first visit.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Trey Holterman, Tyler Johnson, and Diego Baugh, who met at Stanford University working on AI research, founded Tennr in 2021. Baugh had once waited six weeks to hear from a specialist after a hospital stay and ended up back in the hospital. Tennr reads referrals that arrive by fax, email, and portals with its own AI model, trained on tens of millions of medical documents. It pulls out the patient's details, checks insurance eligibility and payer rules, asks for missing records, and routes the referral to a scheduler, so fewer patients drop out before their first visit.
Tennr processes more than 10 million documents a month. It raised a $37 million Series B in October 2024 and a $101 million Series C led by IVP in June 2025 at a $605 million valuation, when its revenue was in the eight figures, three times its level eight months earlier. Toby Cosgrove, the former chief executive of Cleveland Clinic, advises the company.
Compare Notable when AI agents should handle more of the front-desk work, Phreesia when a large group wants intake and payments at scale, Luma Health when scheduling and referral workflows run across a health system, Artera when patient messaging matters most, and Yosi Health when a specialty practice wants pre-visit intake only.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Phreesia | 8.2 | 7.8 |
| Tennr | 8.0 | 7.9 |
| Artera | 7.7 | 7.0 |
| NexHealth | 7.6 | 7.5 |
| Notable | 7.5 | 7.0 |
| Luma Health | 7.4 | 7.0 |
| CipherHealth | 7.3 | 7.1 |
| Relatient | 7.2 | 6.9 |
| Clearwave | 7.1 | 6.8 |
| Yosi Health | 6.8 | 7.3 |
Pricing
| Item | Detail |
|---|---|
| Model | Custom quote from Tennr. |
| What usually drives cost | Referral and document volume, the number of locations, which workflows are used (intake, eligibility, scheduling, follow-up), and system integrations. |
| What to ask in diligence | The price per referral or per document, setup costs, and what counts as a processed referral. |
| Published pricing | No public list price. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Tennr to function | |
| Intake manager who owns referrals | Changes the intake team's day. |
| Fax and email intake channels | Tennr reads what arrives. |
| Payer rules for your services | Checks depend on them. |
| Practice management system access | Referrals land in the schedule. |
| Baseline referral-to-visit rate | Shows change later. |
| What will maximize your value | |
| Start with the highest-volume referral types | Biggest time savings. |
| Turn on missing-records requests | Stops referrals from stalling. |
| Track referrals lost by reason | Shows what still breaks. |
| Deal-breakers | |
| Few referrals | |
| No owner for intake | |
| Wants enterprise messaging first | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Contract and intake channels connected | 2-6 weeks |
| 2 | First referral types live | 1-2 months |
| 3 | All locations and workflows | 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.