Genei replaces the admin with AI. We match willing donors to research facilities at 80–90% lower cost — in seconds, not weeks.
A donor-facing brand that builds the database. A research-facing brand that monetizes it. Connected by an AI matching engine.
Every step of traditional biospecimen recruitment is manual, slow, and expensive. We automate the admin, coordination, and overhead that dominates traditional costs.
| Process | Traditional CRO | Genei |
|---|---|---|
| Patient identification | Manual screening, weeks | AI matching, seconds |
| Eligibility parsing | Human review of protocols | NLP auto-parse study criteria |
| Donor communication | Call centers, mail | Automated SMS/email/app |
| Scheduling | Admin staff coordination | AI scheduling with draw sites |
| Consent management | Paper forms, in-person | Digital consent, e-signature |
| Donor retention | Manual CRM follow-up | Automated lifecycle engagement |
| Overhead | Office, HR, middle management | Lean team + AI ops |
In 2019, we co-founded Genetic Inception — same concept, same founders. COVID killed the timing. But the core insight was right, and everything that was hard then is easy now.
Matching donors to studies required manual data review, human phone calls, and paper consent. The admin cost we were trying to eliminate — we had to recreate it ourselves. We needed 20 employees to do what AI now does in seconds.
NLP parses study protocols instantly. AI matches donors to eligibility criteria in seconds. Automated consent, scheduling, and donor communication. Two founders can run the entire operation that would've required a 20-person team in 2019.
We know the regulatory landscape, the draw site economics, the donor acquisition playbook, and where the last attempt stalled. We're not learning — we're executing with better tools.
Existing players either aggregate from biobanks or operate as full-service CROs. Nobody is building a direct-to-donor AI marketplace.
| Traditional CROs | iSpecimen | Genei | |
|---|---|---|---|
| Model | Full-service recruitment | Biobank aggregator marketplace | Direct-to-donor AI marketplace |
| Specimen source | Hospital networks, sites | Existing biobanks | Own donor pool (fresh, on-demand) |
| Cost per specimen | $30,000–$50,000 | $8,000–$15,000 | $2,000–$8,000 |
| Time to match | 4–8 weeks | 1–3 weeks | < 30 seconds |
| Donor relationship | None (hospital-owned) | None (biobank-owned) | Direct — donors are paid, profiled, retained |
| AI operations | None | Partial | End-to-end |
| Donor incentive | $0 to patient | $0 to patient | $20–$500 per donation |
| Revenue | $B+ (Covance, ICON, PPD) | $1.93M in 2025 (down 79%, going concern) | Pre-revenue (Year 1 target: $300K–$600K) |
iSpecimen is the closest comparable — a publicly traded biospecimen marketplace. But they're in trouble: revenue fell 79% to $1.93M in 2025, they carry going concern risks, and Nasdaq compliance issues. Their model has a fundamental weakness: they don't own the supply.
Their "ready to ship" categories tell us exactly which conditions researchers are buying. We use this as a targeting roadmap for donor acquisition — recruit the people they can't find fast enough.
Other competitors in the space: BioIVT, Cureline, The Sample Network, Novaseek, NDRI. All operate the same old model — source from hospitals and biobanks. None build direct donor relationships. None pay the donor.
iSpecimen just launched an AI matching agent (March 2026) — they're scrambling to modernize. But bolting AI onto a broken supply model doesn't fix the core problem. We're AI-native from day one with a fundamentally better supply chain.
We built the first version of this in 2019. COVID killed the timing, not the idea. Now we're back with AI ops, automation expertise, and the same domain knowledge — ready to execute.
20% equity · $800K pre-money · 12 months to profitability
$800K pre-money valuation. $1M post-money. Minimal base salaries with upside tied directly to performance.
$3K/mo base each. 15% of monthly gross profit split between founders. No profit = no bonus.
Per-placement fees anchor the model. Database subscriptions and feasibility searches add recurring revenue as the donor pool grows.
A single 40mL draw is processed into multiple specimen types — each matched to a different study. One donor visit generates the revenue of eight placements.
$128K in operations. $72K in founder base salaries ($3K/mo each) + 15% gross profit kicker. Built to reach profitability before the capital runs out.
| Category | Phase 1 (Mo 1-3) | Phase 2 (Mo 4-6) | Phase 3 (Mo 7-12) | Total |
|---|---|---|---|---|
| Legal & regulatory | $4,500 | $500 | $2,000 | $7,000 |
| Business formation | $1,500 | — | — | $1,500 |
| Ad spend (donor acquisition) | $7,000 | $9,000 | $18,000 | $34,000 |
| Donor incentives | $10,000 | $6,000 | $12,000 | $28,000 |
| Draw site fees | $7,500 | $4,500 | $9,000 | $21,000 |
| Logistics / shipping | — | $3,000 | $6,000 | $9,000 |
| Sales expenses | — | $2,000 | $3,000 | $5,000 |
| Ops hire (Phase 3) | — | — | $15,000 | $15,000 |
| Platform / hosting / misc | $1,200 | $500 | $1,200 | $2,900 |
| Founder base salaries (2 x $3K/mo) | $18,000 | $18,000 | $36,000 | $72,000 |
| Total | $49,700 | $43,500 | $102,200 | $195,400 |
Build the supply side first, prove the match, then scale. Physical Genei studios come only after the marketplace proves out.
Each donor draw yields 3-8 specimens for different studies. We project conservatively at 3 matched specimens per draw, with upside to 5+. Even the conservative model returns profit on $200K invested in Year 1.
| Metric | Conservative | Moderate | Aggressive |
|---|---|---|---|
| Donors in database | 1,000 | 2,500 | 5,000 |
| Active research clients | 3 | 8 | 15 |
| Donor draws (Year 1) | 100 | 200 | 400 |
| Avg specimens matched per draw | 3 | 4 | 5 |
| Total placements | 300 | 800 | 2,000 |
| Avg per specimen | $1,500 | $1,500 | $1,500 |
| Revenue | $450K | $1.2M | $3M |
| Total expenses | $130K | $350K | $680K |
| Net profit | $320K | $850K | $2.32M |
Most marketplaces burn cash for a decade before turning profitable. Genei's asset-light model, multi-specimen yield, AI ops, and 97% per-draw margins mean we don't need to.