Flexible fertility benefits your employees will truly appreciate

Univfy is an affordable and easy way to satisfy employees, improve employee retention, and attract talent. Layer our customizable benefits program to any existing health plan or coverage.


Univfy meets you where you are

Already have fertility coverage? Starting from scratch? Univfy enhances value no matter where you are.


Univfy supports employees through their entire journey


Your employees need fertility care to build their families

Employers want to cover IVF to satisfy employees

Source: 2021 Mercer National Survey of Employer Sponsored Health Plans


The Univfy® PreIVF™ Report increases affordability and employee satisfaction

Univfy uses artificial intelligence to make it affordable to offer IVF coverage to your employees, increasing employee satisfaction and loyalty.

Ask how Univfy can enable value-based pricing

Univfy’s IVF success predictions support development of value-based pricing where employers only pay for the best outcomes. With Univfy, individuals and couples can afford a treatment plan that maximizes their chances of having a healthy baby.

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How Univfy® A.I. works

Univfy makes predictions of an individual’s chances of having a baby with each IVF cycle, based on the analysis of dozens of key factors in the employee’s health profile. Leveraging artificial intelligence and machine learning, Univfy predicts IVF success rates with more than 95% accuracy. Read more about our methodology and research, here.

 
 

Provider Testimonials


What providers are saying about Univfy

Univfy collaborated with the American College of Obstetrics and Gynecology (ACOG), at the 2021 ACOG Annual Meeting, to share how fertility specialists are using the Univfy AI Platform to counsel patients.

Using AI/ML to Support Personalized Counseling in IVF

Dr. Matthew G. Retzloff, a fertility specialist at the Fertility Center of San Antonio, shares how he uses the Univfy PreIVF Report when counseling patients about IVF. This clip is from a presentation made by Dr. Retzloff during the Best of ASRM and ESHRE 2021 conference.


Univfy Published Research: Validated IVF Outcomes Prediction Models

 
 
 
 

2008 - PLOS One
Univfy® founding team reported factors pertaining to embryos as a cohort had greater predictive power in IVF-live birth probabilities than factors describing individual embryos.

2008 Research Page 1

2010 – PNAS
Using a prediction model developed and validated with machine learning, more patients were found to have higher IVF success probabilities than age-only control model. More than 80% of patients tested by Univfy’s prediction models were proven to have higher probabilities of IVF success, compared to age-based model.

2010 Research Page 1

2012 - Fertility and Sterility
Using machine learning, we showed that the probability of having twins from transferring two embryos in an IVF-embryo transfer procedure can be predicted for each patient and her partner.

2012 Research Page 1

2013 – Fertility and Sterility
We reported that first-cycle IVF data and outcomes from multiple centers can be merged to train and validate a multi-center IVF-live birth prediction model that has superior predictive power, discrimination and reclassification rates than possible from an age-only control mode.

2013 Research Page 1

2015 - Fertility and Sterility
Univfy and collaborators show the ovarian reserve marker AMH, when used together with other clinical predictors, provided predicted probabilities of IVF success with or without the concomitant use of antral follicle count.

2015 Research Page 1

2020 – RBMO
Univfy® Collaborates with EU Fertility researchers to discuss the use of AI-assisted IVF prognostics as one approach to reducing barriers to IVF utilization. Economic factors and stress are among key barriers to ART utilization. One patient-centered approach to addressing these barriers is to clearly communicate individualized prognostic information to patients with transparency and empathy, thereby helping patients set realistic expectations of ART [assisted reproductive technology]. Shared decision-making may also reduce the stress of healthcare practitioners counselling patients on ART prognosis.

2020 Research Page 1

Our methods have been published in top, peer-reviewed research publications, in which we reported and established benchmarks for measuring the performance of our IVF outcomes predictive model. Click on each of the circles above to view the corresponding research paper abstracts.

We work with providers globally and ensure that our data use methods are compliant with the authorities of each country including HIPAA and GDPR.

Contact us with questions or to learn more: Heather Holland | Director of Communications | heather.holland@univfy.com


About Univfy®

Univfy is a technology company founded by Stanford University researchers with expertise in reproductive medicine and artificial intelligence (AI). Dr. Mylene Yao and Dr. Wing H. Wong cofounded Univfy to make IVF success more predictable for women and couples navigating their family-building options, while improving efficiency for providers. Through product innovation and response to feedback from fertility specialists and patients, we’ve built a proprietary IVF-success predictive platform that is scientifically validated and makes fertility success and costs more predictable and transparent. Univfy helps women and couples make better informed and cost-effective decisions about their treatment.


About Univfy® Founders


Contact Us

We look forward to meeting you! Please reach out to us to schedule a meeting. Please include information about the number of employees at your company and whether you are looking to add Univfy to an existing health plan or looking for us to design and implement a stand-alone fertility benefits program for your organization. Email us or fill out the form below and we will respond to you shortly.

Mylene Yao | CEO & Cofounder
Phone: (650) 799-8003
Email: mylene.yao@univfy.com

Heather Holland | Director of Communications
Phone: (646) 400-2745
Email: heather.holland@univfy.com