Women spend over 30 years of their lives navigating biological transitions—puberty, fertility changes, pregnancy, perimenopause, and menopause—yet our healthcare system has largely failed to acknowledge the complex nature of these transitions, let alone address them effectively. This gap represents not just a clinical oversight but a $50 billion market opportunity that has remained largely untapped—until now.
Enter HealthEngine, a health tech startup whose Biological Transition Navigator platform is applying sophisticated AI to detect and manage these transitions months before conventional medicine can identify them. Founded by Shalvi Singh, a computational biologist with a background spanning both genomics and machine learning, the company is creating an entirely new product category in femtech by focusing on transitions rather than stable states.
“Most healthcare approaches treat the body as a static system with occasional disruptions,” says Singh, whose own experience with undiagnosed perimenopause led her to investigate this gap. “But women’s bodies are dynamic systems with complex temporal patterns. Traditional snapshots miss the transitions where intervention is most critical.”
The AI Pattern Recognition Rebuilding Women’s Health
The technical architecture behind HealthEngine’s platform represents a significant leap beyond traditional health tracking apps. While most femtech companies focus on tracking cycles or symptoms, HealthEngine’s approach utilizes advanced pattern recognition to identify the subtle signals of transition onset.
The company’s proprietary algorithms employ wavelet decomposition—a mathematical technique for analyzing signals across different timescales—combined with temporal sequence alignment to detect pattern shifts across multiple biological streams. This allows the system to identify transition onset up to 18 months earlier than conventional clinical diagnosis.
“Healthcare has been stuck in a paradigm of seeing ‘normal’ versus ‘abnormal’ readings when what we really need to detect are shifts in patterns over time,” explains Dr. Marina Chen, HealthEngine’s Chief Scientific Officer. “A woman’s hormone levels might be technically ‘normal’ at each individual reading, but the pattern of change could indicate she’s entering perimenopause years before conventional markers would show it.”
The system’s machine learning approach has identified several novel transition biomarkers that weren’t previously recognized in clinical medicine. For example, the platform found that specific patterns of nighttime heart rate variability combined with subtle temperature fluctuations can predict perimenopause onset far earlier than follicle-stimulating hormone (FSH) tests, the current clinical standard.
What makes the company’s approach particularly powerful is its ability to work with sparse data. Rather than requiring frequent, invasive testing, the platform uses transfer learning to map limited individual measurements against dense reference datasets, creating personalized models with minimal data points.
The AI Feedback Loop Transforming Research
Unlike many healthcare applications where AI is applied to existing knowledge, HealthEngine’s approach is generating entirely new scientific understanding. The company has implemented a federated learning infrastructure that allows its models to continuously improve while maintaining strict privacy protections.
“We’re seeing a virtuous cycle where better detection leads to more data, which leads to even better models,” says Singh. “We’ve already identified several transition phenotypes that weren’t recognized in traditional medicine.”
The system has challenged conventional medical wisdom in several areas. For instance, it revealed that hormonal fluctuations during perimenopause follow distinct patterns depending on a woman’s inflammatory markers and metabolic profile—suggesting that the one-size-fits-all approach to hormone therapy may be fundamentally flawed.
HealthEngine has implemented a comprehensive ethical framework for its AI development. The company prioritized removing algorithmic bias by ensuring its training datasets include diverse populations and by continuously monitoring performance across demographic groups.
“We found early on that models trained primarily on data from white women missed important transition signals in women of color,” notes Singh. “We’ve built specific bias detection and correction systems to ensure our platform works for everyone.”
From Research to Market Reality
HealthEngine’s journey from research project to commercial platform wasn’t straightforward. The company initially focused on the consumer market but quickly realized the transformative potential of integrating with healthcare systems.
“We faced the classic healthcare startup dilemma—building consumer trust while establishing clinical credibility,” says Singh. “The breakthrough came when we realized our platform could simultaneously serve women directly while providing healthcare systems with a powerful population health tool.”
The company has navigated the complex regulatory landscape by implementing a staged approach—beginning with wellness features while building the clinical evidence needed for more advanced capabilities. They’ve secured partnerships with one major healthcare system in New Jersey.
With high returning users, the platform has demonstrated strong product-market fit. Enterprise partnerships will provide a stable revenue stream alongside the direct-to-consumer subscription model, allowing the company to grow more efficiently than many healthcare startups.
Singh attributes much of this success to the platform’s ability to deliver immediate value while continuously improving its predictive capabilities. “Women don’t need to wait years to see benefits—they get actionable insights from day one, which keeps them engaged as our models become increasingly personalized.”
A Growing Field with Multiple Approaches
HealthEngine isn’t alone in recognizing the opportunity in women’s transitions, though its network physiology approach represents a unique angle. Several other startups are focusing on various aspects of this space.
Elektra Health, founded by Alessandra Henderson, takes a telehealth-first approach to menopause, combining virtual care with educational content and community support. While less AI-focused than HealthEngine, Elektra has built robust clinical protocols and provider networks specialized in menopause care.
Vira Health’s Stella app focuses specifically on menopause symptom management through behavioral interventions and has recently expanded into prescription services in certain markets. The company has raised significant funding to build clinical evidence for its digital therapeutic approach.
Fertility-focused Kindbody has expanded beyond its clinic network to address the entire reproductive journey, though its approach remains centered around in-person care with digital supplements rather than HealthEngine’s primarily digital model.
“We see these different approaches as complementary rather than competitive,” says Singh. “The women’s health space has been so underserved that we need multiple solutions addressing different aspects of the problem.”
What distinguishes HealthEngine is its comprehensive focus on all major transitions rather than a single phase, as well as its advanced AI capabilities that enable early detection and personalization at scale.
A Day in the Life: User Experience
For users like Jennifer Martin, a 43-year-old marketing executive, HealthEngine’s platform provided answers that multiple doctor visits couldn’t deliver.
“I was experiencing brain fog, sleep disruptions, and mood changes that were affecting my work, but my doctor said all my tests were normal,” Martin explains. “The Navigator identified early perimenopause patterns and gave me a personalized plan. Six months later, my symptoms are manageable, and I’ve maintained my productivity during a critical career phase.”
The platform’s user experience begins with a comprehensive onboarding process that collects baseline data across multiple health dimensions. Users connect any existing health tracking devices or apps and complete assessments on symptoms, health history, and goals.
Within two weeks, most users receive their initial transition assessment, including their current status and projected timeline. The system then delivers personalized insights and recommendations, adapting as it gathers more data.
Perhaps most valuable is the way the platform bridges the gap between self-care and medical care. “The Navigator gave me exactly the information I needed to have a productive conversation with my doctor,” says Martin. “Instead of being dismissed, I came with patterns and data that led to appropriate treatment.”
Healthcare System Impact
The platform’s ability to detect transitions earlier has significant implications for healthcare costs and outcomes. HealthEngine’s data shows that users experience 38% fewer emergency department visits and 42% lower specialist utilization compared to matched controls.
“Early intervention in transitions is the ultimate preventive care,” says Dr. Robert. “We’re seeing reduced healthcare utilization and better outcomes across multiple metrics.”
For healthcare systems, the platform offers population health capabilities that weren’t previously possible. Providers can identify patients at the earliest stages of transitions and intervene before symptoms become severe or complications develop.
The economic case is compelling: for every dollar spent on the platform, healthcare systems are seeing reduced costs—a return on investment that has accelerated adoption even in budget-conscious environments.
The Future of Network Medicine for Women
HealthEngine is rapidly expanding its capabilities. The company recently announced partnerships with diagnostic companies to incorporate additional biomarkers into its models and is developing integration with continuous glucose monitoring systems to better understand metabolic-hormonal interactions.
“We’re moving toward a comprehensive biological navigation system for women throughout life,” Singh explains. “The technology we’re developing today will eventually enable intervention optimization that’s currently impossible in traditional healthcare settings.”
The company is preparing for the next generation of sensors and measurement technologies, which Singh believes will dramatically enhance the platform’s capabilities. “As continuous monitoring becomes less invasive and more comprehensive, our models will become increasingly powerful.”
Industry analysts see companies like HealthEngine as the vanguard of a new approach to healthcare.
A New Paradigm in Healthcare
The significance of HealthEngine’s approach extends beyond women’s health. By focusing on transitions rather than stable states, the company is highlighting a fundamental gap in how we approach healthcare across all populations.
“The human body is never truly static—it’s constantly adapting and changing,” Singh reflects. “Our healthcare system needs to evolve from snapshot thinking to continuous monitoring and early intervention.”
As HealthEngine continues to expand its user base and deepen its AI capabilities, Singh remains focused on the company’s core mission. “Every day, women tell us they finally feel seen and understood after years of having their experiences dismissed. That’s what drives us—creating technology that recognizes the complex reality of women’s biology and provides the support that’s been missing for far too long.”
In a healthcare landscape often criticized for fragmentation and reactivity, HealthEngine’s approach offers a glimpse of a more integrated, proactive future—one where technology doesn’t just treat disease but helps navigate the transitions that define our biological journeys.
Source: How HealthEngine's AI Is Closing the $50B Gap in Women's Healthcare
