Healthcare still runs on the fax machine. When a primary care physician refers a patient to a specialist, the referral usually arrives as a faxed document or a scanned PDF that a front-office worker must read, interpret, and manually enter into the practice's systems. Errors, delays, and lost referrals are endemic, and the result is patients who never get scheduled and providers who lose revenue. Tennr was founded in 2021 to attack exactly this bottleneck.
Rather than rely on generic OCR or off-the-shelf language models, Tennr built its own specialized model trained on tens of millions of real medical documents and purpose-built for the referral use case. The model reads inbound referrals, parses unstructured clinical documents, extracts the data points that matter — diagnosis, insurance, ordering provider, clinical justification — and routes the referral appropriately, flagging missing information so staff can resolve it before it becomes a denied claim or a no-show.
The company's growth has been rapid. Tennr closed a $37M Series B in late 2024 and, less than a year later, raised a $101M Series C led by IVP with new investors Google Ventures and Iconiq joining existing backers Andreessen Horowitz and Lightspeed, valuing the company at $605M. By the time of the Series C, Tennr was processing about 10 million documents per month and had reached eight figures of revenue, roughly triple its level at the Series B.
Tennr's customers are the businesses where referral volume is both high and operationally painful: specialty practices, durable medical equipment (DME) suppliers, diagnostic labs, and multi-site provider groups. For these organizations, every referral that falls through the cracks is lost revenue and a patient who didn't get care. By automating intake at the document level, Tennr converts a slow, error-prone manual process into a fast, auditable workflow. The strategic bet is that owning the patient front door — the very first touchpoint in the care journey — gives Tennr a defensible position from which to expand into adjacent revenue and scheduling workflows.