CONFIDENTIAL
✦ Deep tech for health

Meet Ema.
The women's health AI.

The problem
75% of U.S. health companies are betting on AI. But…
~60%
of women's health scenarios fail across the 13 leading LLMs
Gruber et al., A Women's Health Benchmark for Large Language Models, arXiv 2025.
AI is calibrated for men and applied to women.
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 and diagnostics were built around men.Nature Reviews Bioengineering (2024), “Funding research on women's health.”
Complex

One body, one system
but women's health lives in silos.

Ovarian aging reshapes everything; the data never connects.Mayo Clinic — “How menopause affects heart, brain and bone health”
The solution

Ema turns general AI into
women's health intelligence
any company can trust and deploy.

InputAny frontier modelGeneral-purpose, male-baselined
→
The Ema layeremaeqClinical rubrics · guardrails · 10M+ conversations
→
OutputA partner's productTrusted women's health experiences
Why they choose Ema

Ema's intelligence plugs into any digital health experience, guiding users to the care they need.

Health education

Care navigation

Assessments

Data analytics

Partner use cases
Cycle tracker with 2K MAUs sitting on years of logged symptom data

Most of it runs in the background of Aavia's onboarding and quizzes — no chatbot required.

Symptom forecastingHealth education→Predicts the week ahead from up to 14 logged cycles
Personalized planningAssessments→A 3-action relief plan built around her diet, goals and phase
Impact reportingData analytics→Day-7 before-and-after data — then the loop restarts
Aavia relief plan powered by Ema
↑67.5%plan completion vs. 30–40% industry
↓30%in fatigue after a completed plan
What they got
Ema more than
DOUBLED
the industry standard for 30-day retention because Ema actually personalized the recommendations their users needed.
68.73%30-day retention — 2.3× an industry average of 25–30%
+15ptretention for users with full Ema access
Source: Ema × Aavia partner case study; Aavia, The Hormone Cycle Is Not Noise, 2026 — 250M+ lived-experience data points from 150,000 members.
LetsGetChecked
Embr Wave
Julie
Womaness
PatientsLikeMe
Work&
Otsuka
lovu
Flourish
Stanford University
Women's Heart Alliance
Wavebye
Why build on Ema

Ema's output is more clinically accurate than LLMs.

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

She asks“I'm exhausted, nauseous, and my jaw aches.”
Raw model

“Try gently massaging your jaw or applying a warm compress.”

● Misses a heart attack.
Ema

“Jaw ache and nausea can signal a heart problem — especially in women, where it shows up without chest pain.”

● Catches the red flag. Escalates. Uses her history.
The suit is Ema's women's health intelligence. That's the difference.
Ema's proprietary DNA
Ema as an astronaut's suit A frontier model drawn as an astronaut. Each part of the suit is labelled with a layer Ema provides: the helmet is safety and guardrails, the chest module is clinical frameworks, the life-support pack is the proprietary dataset, the arm telemetry is data analytics, the gloves are agentic capabilities, and the boots are secure infrastructure. Safety & guardrailsred flags, escalationProprietary dataset10M+ conversationsSecure infrastructureHIPAA · GDPR · SOC 2Clinical frameworksclinician-approved rubricsData analyticsfrom every conversationAgentic capabilitiestriage, screening, scheduling
Astronaut = any frontier model, swappable  ·  Suit = Ema, running on our hybrid language model
TL;DRThe LLM missed a woman having a heart attack. Ema didn't.
What we built

LLMs don't want to build this,
and history proves that.

5%

of global R&D funding has gone to women's health — for decades.

Why no one has done this: history shows people don't 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

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

Total Addressable Market

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
Business focus

Consumer-driven health and wellness solutions for women.

SAM $250BAvg customer $250Ke.g. Julie
2
Diagnostics & pharma

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

SAM $150BAvg customer $500Ke.g. Let's Get Checked
3
Tech-enabled health

Telehealth, at-home lab testing, virtual pharmacy.

SAM $100BAvg customer $350Ke.g. TruePill
Segment TAM: wellness $1,000B · diagnostics & pharma $600B · tech-enabled health $400B.  Sources: McKinsey, WHO, GlobalData, Fortune, Statista, Grand View.
Financial status

We've already sold a lot of it.

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.

Forbes: AI-Powered Women's Health — Ema's Mission To Combat Bias
Entrepreneur: These Founders Are Building Healthcare Companies for the People the System Keeps Missing
Forbes: 60% AI Failure Rate In Women's Health — Standards Are Coming
Serena Williams highlighting Reckitt Catalyst and Acumen America cohort
Forbes TIME Entrepreneur Fortune The Wall Street Journal
WHAI Consortium
Co-founder — board from:
Willow Clue Carrot Fertility Center for Reproductive Rights Midi Health
AI Innovation of the Year
Who's building it

Meet the team that built the first agentic AI for women, circa 2019 ✦

CEO
Amanda Ducach
Amanda Ducach
Serial tech entrepreneur,
sales strategist
15+ yrs
CTO
Vish Sharma
Vish Sharma
Serial entrepreneur
Data scientist & architect
15+ yrs
CXO
Karishma Patel
Karishma Patel
Scrum-certified, CX
10+ yrs
CSO
Morgan Rose
Morgan Rose
WHNP-BC, CNM & IBCLC
15+ yrs
AI Biologist
Russ Foltz-Smith
Russ Foltz-Smith
OpenAI Ambassador,
Microsoft MVP
25+ yrs
Head of Finance
Chris Scudellari
Chris Scudellari
CPA, Partner at EY
for 40+ years
Medical Advisory Board
Peds & Internal Med advisor
Peds &
Internal Med
Lifestyle & Emergency Med advisor
Lifestyle &
Emergency Med
OBGYN advisor
OBGYN
Emergency Med advisor
Emergency
Med
Team alumni: BlueCross BlueShield, Microsoft, OpenAI, Clue, UChicago Medicine, Starbucks, Match, WolframAlpha, Marriott, EY
The ask

We're raising a SEED+ round.

$3.5Mraised to date
Backed by Curate Capital Hearst Lab, Kubera Venture Capital, Wormhole, Techstars, Emmeline Ventures, Victorum Capital, Acumen
This round takes us the rest of the way
STEP 01

Self-serve Ema

Developers build on Ema on their own — no hand-holding, no services layer.

→
STEP 02

Deeper into enterprise

A scalable go-to-market that moves Ema up-market, partner by partner.

→
STEP 03

Profitability

This is the round that gets us there.

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