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
- Practices that want AI agents to cover the full front desk
Bottom line
Tennr is worth using for specialty practices whose intake staff spend too much time reading faxed and emailed referrals by hand. It checks eligibility and payer rules before a referral is scheduled, which cuts the drop-off between referral and 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. Based in New York City, 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. 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, and Phreesia when a large group wants intake and payments at scale. Luma Health fits when scheduling and referral workflows run across a health system, and Artera when patient messaging matters most. Yosi Health fits a specialty practice that wants pre-visit intake only.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Phreesia | 8.2 | 7.8 |
| Artera | 7.7 | 7.0 |
| NexHealth | 7.6 | 7.5 |
| Basalt Health | 7.6 | 7.4 |
| 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 |
| Tennr | 8.0 | 7.9 |
Pricing
| Item | Detail |
|---|---|
| Model | Fees are quoted for each customer and are not listed on the website. |
| 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 before you buy | The price per referral or per document, setup costs, and what counts as a processed referral. |
| Published pricing | Prices are quoted for each customer and are not listed on the website. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Tennr to function | |
| Intake manager who owns referrals | The product changes how the intake team spends its day. |
| Fax and email intake channels | Tennr reads the referrals that arrive on those channels. |
| Payer rules for your services | Eligibility and payer checks depend on those rules. |
| Practice management system access | Referrals land in the schedule only when the practice management system is connected. |
| Baseline referral-to-visit rate | A baseline rate is what later gains in referral-to-visit conversion are compared with. |
| What will maximize your value | |
| Start with the highest-volume referral types | Highest-volume referral types usually produce the biggest time savings first. |
| Turn on missing-records requests | Missing-records requests stop referrals from stalling while charts are incomplete. |
| Track referrals lost by reason | Tracking losses by reason shows which steps still break after launch. |
| Deal-breakers | |
| The practice receives few referrals. | |
| Nobody on staff owns intake. | |
| The buyer wants enterprise patient messaging across every department 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.