Jenny Duan spent months entering details about her menstrual cycle into a tracking app, expecting the data to eventually offer real insight. It never did. Her cycle was irregular, the app’s forecasts were frequently off, and the disconnect between what her body was experiencing and what the software could interpret never really narrowed.
That frustration has since turned into a venture-backed startup. Clair Health, founded by Stanford graduates Duan and Abhinav Agarwal, has raised $11.6 million in funding led by Khosla Ventures, with participation from a16z speedrun, Anne Wojcicki and several health-focused investors. The company says more than 25,000 people have already joined its waitlist ahead of a planned November 2026 launch.
Clair’s wearable, however, does not directly measure hormone levels. Instead, it estimates them using machine learning models that analyze skin temperature, heart rate, sleep and breathing patterns—signals that many wearables already capture. That approach is also what makes the company especially notable, and potentially vulnerable to scrutiny.
Duan’s interest in the problem began well before Stanford. While in high school, she worked through an Oregon nonprofit supporting women facing domestic violence and homelessness, where she repeatedly saw healthcare providers dismiss symptoms because there was not enough quantitative data to back them up.
The Cycle App That Told Her Nothing
The experience stayed with her. At Stanford, where she studied symbolic systems with a concentration in AI ethics, she met Agarwal, whose background was in wearable hardware and algorithms. The pair kept returning to the same question: wearable devices had been gathering heart rate, temperature and HRV data for years, yet almost no one was interpreting those signals through a hormonal lens.
Duan’s irregular cycle, in other words, was far from unusual. Clair estimates that about 30% of women fall into the same category—a large enough share that calendar-based predictions and population averages can be little better than guesswork for nearly a third of the people depending on them.
That experience stayed with her. At Stanford, where she studied symbolic systems with a focus on AI ethics, she met Agarwal, who had a background in wearable hardware and algorithms. The two kept circling the same idea: wearables had been collecting heart rate, temperature and HRV data for years, but nothing was reading those signals through the lens of hormones..
Her own irregular cycle wasn’t an edge case. The company puts the number at 30% of women—a population large enough that “calendar-based predictions and population averages” amount to a coin flip for nearly a third of the people relying on them.
A Wearable That Guesses, Not Measures
Here’s what Clair actually is: a jewelry-style wrist device packed with 10 biosensors, paired with an app that runs 130-plus proprietary biomarkers through models trained to estimate levels of estrogen, progesterone, LH and FSH. No blood draws, no urine tests, no needles.
The wrist was a deliberate choice over a finger. Running 130-plus biomarkers through 10 biosensors continuously requires more battery and component capacity than ring-sized hardware can support—the wrist is the smallest form factor that can sustain that load all day, every day.
In beta testing, the company says it has identified nine distinct sub-phases of the female hormone cycle—more than double the four phases most women have never been taught.. It’s a genuinely interesting claim, and if it holds up, it could reshape how cycle-related symptoms get explained to patients.
But “identified” and “measured” are doing very different jobs in that sentence. Clair’s models are inferring hormonal activity from proxies—the same kind of data Oura and WHOOP already collect for sleep and recovery scores. That’s not a flaw so much as the entire bet: continuous glucose monitors don’t measure blood glucose directly either, and they still transformed diabetes care.
The question Clair will have to answer, repeatedly, is what happens when the inference is wrong. A missed workout recommendation is low stakes. A wrong signal about ovulation timing or a perimenopause transition is not—and the company’s own framing, that this is a wellness device rather than a diagnostic one, will get tested the moment 25,000 waitlisted users start making real decisions based on what their wrist tells them.
Why Oura And Whoop Didn’t Build This First
Clair’s pitch leans hard on a structural argument: the wearable giants built their platforms around 28-day cycle assumptions or male physiology, then bolted on women’s health features later. WHOOP, Oura and Fitbit have all expanded in this direction—but in Clair’s framing, hormone monitoring is still a feature sitting on top of someone else’s architecture, not the foundation underneath it.
That’s a fair description of how the category evolved, and it’s also a competitive opening rather than an empty field. Eli Health is pursuing hormone insight through at-home saliva testing, and Inne has already secured regulatory approval in the UK and EU for a saliva-based hormone monitor used for contraception. Both take a more direct measurement approach than Clair’s wrist-based inference—slower, more clinical, but closer to ground truth.
Clair’s wager is that convenience wins: a wearable people already put on every day beats a saliva cartridge, even if the wearable is guessing. Whether that wager pays off depends entirely on how good the guess is.
The Money Behind The Bet
“Clair represents a generational shift in women’s health, moving from guesswork and episodic testing to continuous, real time hormone intelligence that can inform daily decisions,” notes Emily Bennett, Partner at a16z speedrun. “At a16z speedrun, we’re excited to back Jenny and Abhinav as they build the system to make one of the most foundational biological signals measurable and actionable, unlocking a future where women’s health is proactive, deeply personalized, and truly data-driven.”
Bennett’s framing—that this is a generational shift—is the kind of line that’s easy to nod along to and harder to interrogate. Women’s health funding has surged since 2022, and “underfunded and underresearched,” the same phrase the press release uses to describe the broader category, has become almost as common in pitch decks as it is in research papers. That doesn’t make the framing wrong. It does make it worth asking whether Clair represents a shift in what’s possible, or simply a shift in what investors are now willing to fund.
“As a sports medicine physician and scientist focused on female athlete health, I see a major need for better tools that help women understand how hormonal patterns may relate to training, recovery, symptoms, and performance across the lifespan,” explains Dr. Emily Kraus, a Clair Health advisor. “Clair Health is building toward a more personalized and data-informed future for women’s health… That would be a major win for women, not only in sport, but across every stage of life.”
Kraus’s perspective matters because she’s the kind of person who will be asked, professionally, whether Clair’s inferences are clinically meaningful—not just whether the product is well-designed. Her framing, “across the lifespan,” is also a useful gut check: Clair’s founders are 21 and 24, building toward a perimenopause use case neither has lived through yet.
The Real Test Starts In November
Clair says it’s launching an independent clinical trial through Stanford’s Gladstone BeeHive program, with peer-reviewed publication of results—a step the company frames as proof that its science should be checked by people other than itself..
That trial, more than the funding round, is the thing to watch. $11.6 million buys a launch. It doesn’t buy validation. Between now and November, Clair has to prove that a wrist can do what it took medicine decades to admit women’s bodies needed in the first place—pay attention.
