The women's health layer for AI

We turn general AI into
women's health intelligence
any company can trust.

emaeq

The problem

~60%

Leading AI models get women's health wrong most of the time.

13 state-of-the-art LLMs failed roughly 60% of the time on a women's health benchmark. Not edge cases — ordinary questions their users are already asking.

Gruber et al., A Women's Health Benchmark for Large Language Models, arXiv 2025.

Because AI learned women's health from a record that was never properly written.

Under-researched

The studies
were never run.

Women were excluded from most clinical trials until 1993.NIH Revitalization Act, 1993

Male-baselined

The data that exists
is about men.

Dosing, baselines and diagnostic criteria were set on male physiology.Nature Reviews Bioengineering (2024), “Funding research on women's health.”

Complex

The body is one system,
but women's health is treated as many.

Ovarian aging reshapes the brain, heart, bones and metabolism. Care is siloed; the data isn't connected.Mayo Clinic — “How menopause affects heart, brain and bone health”

Why build on Ema

In women's health, a raw model is an astronaut with no suit.

Women's health is a vacuum. Drop proprietary data into it and there's STILL no ground beneath its feet.

Ema is the suit, the women's health layer any model can wear.

The astronaut is swappable; the suit isn't.

Suited up, it goes to work inside your product, and the white-glove team comes with it: model maintenance, continuous core upgrades, an AI biologist keeping the clinical intelligence current, and UI/UX design. None of which a frontier lab has any interest in building, guaranteeing, or supporting.

Astronaut = any frontier model, swappable · Suit = Ema, running on our hybrid language model

What we built

The market needs this,
and no one is building it.

Why no one has done this.

Women's health has received roughly 5% of global R&D funding for decades. History has shown us people do not invest in women's health because it's difficult to execute.Nature Reviews Bioengineering (2024), “Funding research on women's health.”

Any health company built on top of a frontier lab needs Ema.

Modular. Cheaper. Faster. Lower risk.

Every one of these is measured against the alternative: building it themselves, on a frontier model, with their own team.

Modular

Take one capability or the whole layer. Start with intake, add screening later. Nothing is all-or-nothing.

Cheaper

A fraction of the cost of building and staffing it in-house, with no model team to hire and keep.

Transparent

Ema gives ownership, governance, QA, and insights.

Reduces risk

Clinician-approved rubrics, guardrails and escalation. The liability of a wrong answer is the thing they can't insure against.

Trusted

Benchmarked against clinical guidelines and tested against any LLM. Ema co-founded the standard the industry is being measured on.

Market

This is how big the market is that needs to buy it.

Every health company that wants transparent, clinically accurate AI is in this market — across three customer types we already sell into.

TAM$2.0TWomen's health across wellness, tech-enabled health, diagnostics & pharma

SAM$500BReachable with Ema's layer

SOM$110MObtainable near-term

We are here

1

Health & wellness brands

New business focus

Consumer-driven health and wellness solutions for women.

SAM $250BAvg customer $250Ke.g. Julie

Largest SAM and the easiest integration path.

2

Diagnostics & pharma

Fertility diagnostics, hormonal testing, reproductive pharma, lab services.

SAM $150BAvg customer $500Ke.g. My UTI

Highest revenue per customer.

3

Tech-enabled health

Telehealth, at-home lab testing, virtual pharmacy.

SAM $100BAvg customer $350Ke.g. TruePill

Fastest-moving buyers.

Segment TAM: wellness $1,000B · diagnostics & pharma $600B · tech-enabled health $400B. Sources: McKinsey, WHO, GlobalData, Fortune, Statista, Grand View.

Traction

We've already sold a lot of it.

14

Commercial partners live across health and consumer

80%

Prefer Ema to Google, driving engagement and retention

49%

Reduction in operational load for care teams

$2M

Signed in actual contract value in 2025

And this is what they're doing with it:

Care navigation Assessment & screening Engagement Health education Triage & escalation Data insights + many more

Financial status

ARR growth trajectory & pipeline.

ARR Growth: 2025–2028

$563K

2025

$2.5M

YE 26

$7.5M

YE 27

$20M

YE 28

2025 ActualProjected

*Totals in chart above are cumulative

✦Growth Metrics

2025 Q4 ARR

$562K

Q1 → Q4 Growth

326%

✦Active Pipeline

$12M

Total Pipeline Value

Avg Deal Size$125K

Pipeline / ARR21x

25/26' Pipeline Verticals

Pharma
Diagnostics
Women's Health & Wellness Brands

$2M

Signed in actual contract value in 2025

Why teams trust Ema

Recognized, awarded, and setting the standard.

🏆Femtech World — Winner

✨AI Innovation of the Year, 2025

Recognized by

Founding partners

We wrote the standard.

Ema co-founded the Women's Health AI Consortium (WHAI) — the first industry body setting shared benchmarks, ethical standards, and transparent evaluation for women's health AI.

Who's building it

The team

Let's build it together

One intelligence layer.

Build on Ema, layer it onto what they have, or let us build it for them. Their models and data stay theirs — Ema running inside their product in weeks, not quarters.

emahealth.ai · Built on evidence. Shaped by experts.

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Chat with Emaletsconnect@emaapp.co

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We turn general AI intowomen's health intelligenceany company can trust.

The studieswere never run.

The data that existsis about men.

The body is one system,but women's health is treated as many.