The wet lab shouldn’t be where you start; it should be where you validate. We’re looking for teams using AI-driven computational approaches to identify novel targets and design candidates before a single pipette is lifted. We’re especially excited by companies that integrate across the discovery and development value chain, rather than offering point solutions, and that pair technical breakthroughs with business models designed to capture a meaningful share of the value they create.
Biological raw data generation has scaled dramatically in recent decades, but the tools used to analyze and interpret that data have not, making analysis both a bottleneck and an opportunity. We’re particularly excited by teams creating end-to-end or deeply integrated AI-native systems that improve how biological data is processed, analyzed, and turned into value, meaningfully compressing the time from experiment to insight.
We believe that the leading life sciences companies of the next decade will increasingly treat biology as an engineering discipline. We’re interested in platforms that let you design, predict, and iterate on biological systems with tight feedback loops and increasing predictability, so value compounds across programs instead of resetting with each new experiment.
Biology is still too manual. We want to fund the infrastructure that makes experiments reproducible, scalable, and more accessible to teams without their own lab space. We expect the winners in this space to optimize for complex formulations and streamlined delivery mechanisms that high-value therapeutics require.
Clinical trials fail too often, and usually for predictable reasons: poor protocol design, wrong patient populations, unrealistic endpoints. We're looking for companies that use data from completed trials to design better ones, and platforms that bring real-world clinical expertise into protocol development.
Manufacturing advanced therapeutics remains far more artisanal than other high-value industries. We’re interested in companies that apply automation, sensing, and software-driven control to the production of biologics and cell therapies, reducing variability, improving yields, and enabling cost structures that scale with demand rather than labor.
We have more biological data than ever: genomics, proteomics, metabolomics. But it’s siloed and underutilized. Creating comprehensive multi-omic profiles is still technically complex and expensive to produce at scale. We’re interested in approaches that both integrate across data types and materially reduce the cost and complexity of multiomic data generation, making patient-therapy matching and stratification economically viable beyond narrow research settings.
AI and machine learning are transforming biomedical innovation and regulation, enabling rapid analysis of complex datasets for submissions and long-term compliance. The FDA now accepts real-world evidence for label expansion. Wearables generate clinically-relevant data at scale. Payors cover expensive drugs with limited long-term efficacy data. These create opportunities for novel solutions: CROs focused on post-market surveillance and outcomes tracking, "digital twins" for patients on high-cost therapies, and regulatory infrastructure automating interactions between innovators and regulators.
The current model (manufacturers to wholesalers to pharmacies to patients) is broken. Patients with chronic diseases who depend on specialty medications deserve better. We’re interested in companies rethinking how medicines get to patients: direct-to-patient models, digital-first distribution, and platforms that cut costs while improving adherence. The disintermediation of traditional PBMs in favor of transparent, lower-cost alternatives is long overdue.
Empowered consumers, frustrated by fragmented and opaque healthcare experiences, are reshaping the industry. Within the broader shift toward consumer-powered care, the most significant opportunities are emerging where patient dissatisfaction is highest. Women's health is particularly ripe for next-generation solutions, as women continue to self-navigate major life-stage health transitions. From fertility through menopause, most solutions remain symptom-driven or built on limited biological understanding. We believe the next generation of women’s health will replace fragmented and reactive point solutions with more cohesive, proactive, and biology-first approaches that can meaningfully improve care.
Employer-sponsored healthcare is unraveling. Premiums are rising faster than they have in decades, and employers are responding by pushing costs onto employees or carving out specialized benefits such as mental health, fertility, and GLP-1 coverage to vendors outside the core benefits. We're excited about companies building for this fragmented future: platforms helping employers manage carve-outs intelligently, infrastructure enabling the shift from defined benefit to defined contribution plans, and models making cash-pay and direct-to-consumer care viable alternatives to traditional coverage.
EHRs, claims, wearables, patient-reported outcomes: the data exists but doesn’t talk to each other. We’re looking for companies that solve the integration problem and bring in context to unlock longitudinal patient views that payors, providers, and researchers need to make better decisions.
If you're building in any of these areas, we want to meet you. We work with founders and advisors at the earliest stages (even pre-product when you have a spark of an idea or wish something existed in your workflow). We're particularly drawn to operators with deep domain expertise who've felt these problems firsthand.
INITIATE builds and backs AI-native health companies from inception.