Our SaaS and Software expert, Vijay Raina, specializes in enterprise SaaS technology, providing thought leadership in software design and architecture. With health tech increasingly adopting a software-centric approach, we sat down with him to dissect the recent $29 million funding round for fertility startup Inito. We explored how the company is leveraging its massive dataset of over 30 million hormone data points to pioneer AI-designed antibodies, effectively treating biological components like code. Our discussion touched on the practical journey of an antibody from a computer model to a physical test strip, the technical challenges of expanding their platform to new life stages like menopause, and the strategic roadmap for navigating global regulations and educating new markets on the power of comprehensive at-home diagnostics.
Since 2021, you’ve analyzed over 30 million hormone data points. Could you share a key insight from that data that directly informed your decision to pursue AI-designed antibodies, and how did you use that insight to successfully pitch for your $29 million Series B funding?
When you have a dataset of that magnitude, you start seeing patterns that are simply invisible at a smaller scale. The key insight wasn’t just about fertility cycles; it was about the subtle, intricate dance of hormones and the limitations of existing technology in capturing it. We saw countless instances where conventional hormone ranges didn’t tell the whole story. This led us to a critical conclusion: to truly understand what’s happening inside the body, we needed a more sensitive and specific tool than the traditional antibodies grown in animals could provide. During our Series B pitch, we framed this not as a weakness but as our core opportunity. We told investors we weren’t just another testing company; we were building a foundational technology platform. The 30 million data points were our proof that a huge market need was being underserved, and AI-designed antibodies were the proprietary key to unlocking it, allowing us to build a moat around our technology and redefine the entire at-home diagnostics landscape.
Your CTO mentioned treating antibodies “like software,” from predicting 3D folds to virtual testing. Can you walk us through the practical, step-by-step process of how your team takes a single AI-designed antibody from a computer model to a functional component on a physical test strip?
That “antibodies as software” concept is the heart of the innovation. It’s a systematic, engineering-led process. It starts purely in a digital environment. First, our AI models predict the three-dimensional structure of proteins, essentially creating a blueprint for how an antibody will fold. Then, instead of jumping into a costly and slow lab process, we run millions of virtual simulations. We digitally test countless antibody variants against a target molecule, like estrogen, to see which one binds most effectively. It’s like running a massive A/B test without ever touching a test tube. Only after our AI has identified the most promising candidates do we move to the wet lab to synthesize them. From there, we validate their performance and integrate the winning, hyper-sensitive antibody onto our test strips. This digital-first approach slashes R&D time and produces a final component that is far more stable and consistent than anything developed through traditional methods.
The article states your reader will soon power tests for pregnancy and menopause. What are the primary technical hurdles you must overcome to adapt your four-hormone fertility technology for these distinct life stages, and what does your initial product roadmap for menopause monitoring look like?
The primary hurdle is adapting our platform for different hormonal signatures and measurement needs. For pregnancy, the challenge is accurately tracking hormones that change exponentially, not cyclically. For menopause, it’s the opposite; we need to detect subtle, long-term shifts in hormones like FSH against a user’s unique baseline. This requires developing entirely new AI models trained on datasets specific to these life stages. Our initial roadmap for menopause isn’t about a simple “positive” or “negative” result. It’s about building a longitudinal health record. The first phase will focus on establishing a user’s baseline hormonal patterns. The app will then track deviations over months and years, providing insights into the transition into perimenopause and menopause, helping users understand their symptoms and make informed decisions about their health with lab-grade confidence from home.
Traditional at-home tests only track estrogen and LH, while Inito adds PdG and FSH to confirm ovulation. Could you share a specific metric or user anecdote that demonstrates how providing this more complete hormonal picture has fundamentally changed the family planning journey for your customers?
The most profound change we see is the shift from anxiety-fueled guesswork to data-driven confidence. A story that sticks with me is from a user who had been trying for over a year using standard LH tests, which consistently showed she was having a surge. She was frustrated and started to believe the issue was more serious. When she switched to our platform, the LH surge was still there, but our PdG tracking revealed she wasn’t actually ovulating successfully after the surge. For the first time, she had a concrete piece of data that explained the problem. She took this information to her doctor, who was able to provide targeted advice. It’s this confirmation, provided by measuring that progesterone metabolite, that changes everything. It turns a journey of doubt into a proactive, empowered process.
With plans for global expansion, how will you navigate the different regulatory landscapes for at-home medical devices outside the United States? What is your strategy for educating new international markets on the benefits of your multi-hormone testing platform compared to more established, simpler tests?
Navigating global regulation is a meticulous process that begins with building a robust, unassailable data package. Our strategy is to ensure our clinical data demonstrating the device’s accuracy and safety is so comprehensive that it can be modularly adapted for submissions to various regulatory bodies, whether it’s the FDA in the U.S. or other agencies internationally. For market education, we can’t just show up with a new product; we have to introduce a new paradigm. Our strategy is twofold: First, we’ll partner with local medical professionals and key opinion leaders to validate and champion the science. Second, we will focus our consumer messaging on a simple, powerful ide”Don’t just predict; confirm.” We will use clear data visualizations and testimonials to show, not just tell, why a four-hormone picture is fundamentally more reliable, framing it as the logical and necessary evolution of at-home testing.
What is your forecast for the at-home diagnostics market over the next five years, especially with the rise of AI-driven technologies?
My forecast is that we are on the cusp of a major shift from reactive to proactive health management, all driven from home. The next five years will see platforms like this move far beyond fertility into a full-spectrum view of a person’s endocrine health—tracking everything from stress hormones like cortisol to testosterone and thyroid function. With AI-designed antibodies making these tests more sensitive and affordable, we’ll see the rise of continuous or semi-continuous hormone monitoring. This will create a personalized, longitudinal dataset for every individual, allowing AI to detect subtle deviations from their baseline long before clinical symptoms appear. The endgame isn’t just a better test; it’s a “digital twin” of your body’s chemistry that empowers you and your doctor to preempt issues, transforming at-home diagnostics into a cornerstone of preventative medicine.
