Startup profile for Tennr: latest funding, latest known valuation, total disclosed funding, investors, status, sources, and why the company is interesting. Part of the Venture Capital Tracker startup directory.

Startup profile · funding coverage

Tennr funding, valuation and investors

AI that automates healthcare fax and document workflows for referrals and prior auth.

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Funding, valuation & investors

Answer-first snapshot

Latest funding

Series C

$101M · June 2025

Latest known valuation

$605M

Series C · June 2025

Total disclosed equity funding

$156M

Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.

Current status

private

growth · New York, NY

Investors in latest funding

Lead: IVP

Other: Andreessen Horowitz , Lightspeed

Sources: latest funding Last verified: 2026-07-25

Overview

Tennr automates manual healthcare administrative workflows — reading faxes and unstructured documents to process patient referrals, prior authorizations, claims auditing, and intake. Founded in NYC by Trey Holterman, Diego Baugh, and Tyler Johnson, Tennr raised an $18M Series A in March 2024 led by Andreessen Horowitz. It followed with a $37M Series B led by Lightspeed in October 2024 and a $101M Series C in June 2025 led by IVP at $605M valuation, with a16z, GV, and ICONIQ participating.

Why Tennr is interesting

Healthcare still runs on faxes — a16z-led Series A became a $101M Series C at $605M valuation as Tennr's RaeLM model cleared referral backlogs.

Product & use cases

Tennr uses proprietary vision-language models (RaeLM) to read faxes and medical documents, extract data, and automate end-to-end referral and revenue-cycle workflows.

  • Specialty clinics automating inbound referral fax processing
  • Prior authorization document extraction and submission
  • Health systems reducing patient access delays from paperwork backlogs

Key facts

  • Series C (Jun 2025): $101M at $605M valuation led by IVP
  • Series A (Mar 2024): $18M led by a16z
  • Proprietary RaeLM vision-language model for medical documents
  • Fixes 'referral black hole' from fax-based workflows

Funding history (newest first)

Investors in our directory

Funds linked from Tennr's profile — open a fund page for stage focus and related deal articles.

Competitive landscape

Edge: Deep healthcare document AI trained on fax-native workflows — meets providers where data actually arrives, not where EHRs wish it lived.

Healthcare AI automation is crowded but fax-to-action remains unsolved at scale. Tennr's risk is EHR vendors and payers building native intake — speed of provider adoption determines winner.

  • Olive AI (historical) adjacent

    Healthcare automation; Olive wind-down left market gap.

  • Notable Health adjacent

    Intake automation; overlapping referral use cases.

  • Manual offshore coding/intake incumbent

    Labor-intensive; Tennr replaces with AI at scale.

Notable stories

  • a16z's investing post noted Tennr founders spent months on data pipelines before shipping — betting workflow depth over generic LLM wrappers (a16z, Mar 2024).

Industries

Healthtech AI & Machine Learning Enterprise SaaS

FAQs about Tennr

Practical answers founders, operators, and investors typically search for.

Tennr automates healthcare document processing — reading faxes and unstructured records for referrals, prior auth, and intake.
a16z led Series A; Lightspeed led Series B; IVP led Series C. See /fund/andreessen-horowitz and /fund/lightspeed-venture-partners-nyc.
Fax is treated as a secure, legally familiar channel — Tennr meets providers where documents actually arrive.
June 2025 — $101M at $605M valuation.
Both automate intake; Tennr emphasizes fax-native referral backlogs and RaeLM document AI.
Tennr's proprietary vision-language model trained on medical documents and faxes.
Over $150M across Series A through C in 2024–2025.
Not publicly disclosed; hyper-growth healthcare AI company.

By Venture Capital Tracker

Last updated:

Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.