The first longitudinal dataset of
the GLP-1 & peptide generation.
LifePrint pairs a useful consumer engine for advanced bio-optimizers with a real-world evidence pipeline for pharma — turning protocol tracking into a self-funding longitudinal cohort.
The Data Flywheel
Stage: pre-seed1. Protocol Tracking (Consumer Utility)
Users log high-stakes biological interventions.
2. Anonymized Aggregation
Data is pooled into privacy-protected RWE cohorts.
3. Pharma/DeSci Monetization
B2B sales of longitudinal data access.
4. Elite Reinvestment (Retention)
Capital buys advanced diagnostics for users, restarting loop.
Market figures sourced from MarketsandMarkets (2025); range across analysts is $2.4B – $5.4B. Cohort retention is the program design horizon, not a measured churn rate.
Seed round to ship Research Cohort fulfillment and close the first 3 B2B deals.
Round size and terms shared under NDA. The 18-month milestone we are underwriting is the graduation criteria for a Series A: a productized Research Cohort with funded panels in hand, three to four B2B logos with recurring data-access revenue, and a SOC 2 Type II report delivered.
Use of funds
- 60%Engineering — Research Cohort fulfillment, lab integrations, de-identification pipeline
- 20%Pharma BD + data-pack delivery — first 3–4 B2B logos
- 20%Operations & compliance — SOC 2 Type II, HIPAA BAAs, legal
Year 1 milestones (realistic case)
- 3,000-person Research Cohort with funded panels delivered at 3-, 6-, and 12-month milestones
- 3 pharma / CRO / DeSci data-pack deals closed (avg ~$150K subscription + 1 custom build)
- $1.2M ARR run-rate by month 12, on path to Series A traction
- SOC 2 Type II report in hand by month 13
Worst- and best-case scenarios available in the deck. See Unit Economics for the full unit-economics model.
What could derail us — and what we are doing about each.
Pharma sales cycles are 6–12 months.
Year 1 revenue depends on closing 3–4 enterprise B2B deals. Mitigation: published price ladder + productized data packs so buyers can self-qualify, shortening the qualification phase.
The regulatory framework around consumer-contributed RWE is still maturing.
FDA encourages RWE for label expansions but specifics evolve. Mitigation: HIPAA technical safeguards + SOC 2 Type II in flight + consent audit trail + Expert Determination de-identification from day one.
Bio-optimizer cohort loyalty depends on shipped diagnostic delivery.
If funded panels lag the marketing promise, cohort churns. Mitigation: wholesale partnerships with Quest, Labcorp, Function Health, and TruDiagnostic to keep per-panel delivery cost (~$30–$300) well below per-user B2B revenue.
Explore the full brief
Mackenzie Rae Knapp
Mackenzie Rae Knapp is building LifePrint to put the economics of health back in the hands of the people who generate it. After a decade building AI products and advising nearly every major pharmaceutical company, she kept seeing the same pattern: enormous value created from people’s health and their data, captured by almost everyone except those people.
As Head of Data Science at DuMont Project, she led the team that built the deep machine learning technology used to optimize entire businesses, technology compelling enough that Cart.com acquired the company for it. At Cart.com she became the product manager for its AI product portfolio, where that work helped underpin a $400M investment round. She has also led multimillion-dollar data brokering deals, which is how she came to understand how badly that model can serve the individuals at its center.
As Partner and Head of Global AI at a 4,000-employee management consulting firm, she has built and shipped AI products generating $10M+ in new revenue lines, including the offerings used by the firm’s largest pharmaceutical clients.
LifePrint is her answer to all of it: private health intelligence that gives individuals the tools to read their own health pattern, then lets them choose if and how that data contributes to collective research. The product stays quiet, precise, and trustworthy. People own their telemetry, and insight never costs them their privacy. She holds a Finance degree from BYU and a Data Science certification from UCLA, is fluent in Spanish, and is based in Austin, TX.