A nine-billion-dollar problem,
built for the mid-market.
Sizing the opportunity for the Aurelyn Clinical Platform — Aurelyn Trial | OS™, the Clinical Evidence & Consistency Engine, and the eTMF Intelligence OS — across the CTMS, eTMF, and AI-in-clinical-trials markets, then translating that into a defensible product valuation today and at scale.
The market, by the numbers
Aurelyn sits at the intersection of three converging software markets. Each is growing double-digit; the AI layer that ties them together is growing far faster. Figures are 2025–26 estimates from public market-research sources — directional, not gospel.
Combined addressable market, 2026 → 2032
CTMS + eTMF + the AI-in-clinical-trials layer, weighted to a ~17% blended CAGR. The AI segment drives most of the acceleration.
From total market to obtainable revenue
A big market only matters in the slice you can actually reach and sell. Here's the honest funnel: a $9B universe narrows to a serviceable segment of AI-ready, mid-market Western sponsors and CROs, and then to what a focused early-stage team can realistically capture in five years.
Competitive landscape
The category is dominated by large, capable, expensive incumbents. The opening isn't to out-feature them — it's to be AI-native and accessible for the segment they overlook.
Vertical axis: enterprise / large-pharma focus (top) → mid-market accessibility (bottom). Aurelyn's whitespace is the lower-right quadrant.
| Player | Focus | Posture | Aurelyn's edge |
|---|---|---|---|
| Veeva Systems | Life-sciences cloud suite (Vault eTMF, CTMS, EDC) | Enterprise | AI-native consistency + mid-market pricing & onboarding |
| Medidata (Dassault) | Clinical data & EDC leader | Enterprise | Lighter footprint, faster time-to-value |
| Oracle / IQVIA | End-to-end clinical & RWD platforms | Enterprise | Focused engines vs. heavy, all-in-one stacks |
| Florence / Suvoda | Site enablement, eConsent, IRT | Mid-market | AI-first evidence & eTMF intelligence layer they lack |
| ConcertAI / Unlearn.AI | AI for data & trial design | AI-native | Operational ops + compliance focus, not just modeling |
| Aurelyn AI Clinical | AI-native trial ops, evidence consistency & eTMF intelligence | AI · Mid-market | The accessible, human-centered, AI-first platform for the underserved middle |
Bottom-up revenue model
Built directly on the three published pricing tiers — Growth ($42K/yr), Professional ($145K/yr), Enterprise (from ~$420K/yr realized) — plus a lower-multiple services line (Clinical Academy + advisory). A deliberately aggressive-but-plausible early SaaS ramp off a small base.
Annual recurring revenue, Years 1–5
SaaS ARR by tier (stacked) with the services line shown separately. Year 5 SaaS ARR ≈ $12.8M; total revenue ≈ $14.6M.
| Metric | Yr 1 | Yr 2 | Yr 3 | Yr 4 | Yr 5 |
|---|---|---|---|---|---|
| Customers (logos) | 5 | 15 | 35 | 64 | 103 |
| SaaS ARR | $0.42M | $1.63M | $4.05M | $7.63M | $12.81M |
| Services revenue | $0.30M | $0.60M | $1.00M | $1.40M | $1.80M |
| YoY ARR growth | — | +288% | +149% | +89% | +68% |
| Total revenue | $0.72M | $2.23M | $5.05M | $9.03M | $14.61M |
Key assumptions: net revenue retention ~115–120% (customers expand by adding trials & modules), low logo churn from regulatory lock-in, blended gross margin ~78–82% on SaaS. Services valued separately at a lower multiple.
Product valuation
An early-stage, AI-native platform is worth modest money today and meaningful money at scale — the spread is execution risk. Three lenses triangulate the number; comparable multiples anchor it.
EV / ARR at scale
Year-5 SaaS ARR of $12.8M × a healthcare-vertical-SaaS multiple of 5–9× (AI-native premium, regulatory moat). Base case ~7× ≈ $90M.
Present value today
Discounting the ~$90M base-case Year-5 enterprise value back 5 years at a 35–45% early-stage rate. Base ~40% ≈ $17M risk-adjusted PV.
Realizable today
Early ARR & pipeline × premium early multiple, floored by the replacement value of three live platforms + the Academy IP. The number you'd defend in a seed / pre-A round.
| Reference | Revenue / ARR | EV / Revenue | Relevance |
|---|---|---|---|
| Veeva Systems (public) | ~$3.2B (FY26) | ~6–7× | Category-defining life-sciences cloud anchor |
| Medidata / Dassault (2019 M&A) | ~$0.6B then | ~9× (at deal) | Strategic-acquisition precedent (~$5.8B) |
| Healthcare vertical SaaS | private | 5–12× ARR | Regulatory moat & lock-in command premiums |
| Private lower-mid SaaS (median) | $5–50M EV | ~4.5× (3–7×) | Realistic band for sub-$50M ARR |
| Aurelyn — applied multiple | $12.8M (Yr5 ARR) | 5–9× → base 7× | AI-native + healthcare vertical + small-ARR discount |
Note: public SaaS multiples compressed sharply in Q1 2026 (the "SaaSpocalypse"), with the public median falling toward ~3.3× before AI-genuine names re-rated. Vertical healthcare SaaS with defensible AI held its premium — which is the band applied above. Sources: SaaS Capital Index, Aventis Advisors, Windsor Drake, PitchBook (2025–26).
Valuation sandbox
Change the assumptions and watch both the at-scale enterprise value and the discounted present value move. This is the same arithmetic an investor will run — better to run it first.
Enterprise value & present value
Defaults reflect the base case: $12.8M Year-5 ARR, a 7× healthcare-vertical multiple, and a 40% early-stage discount rate over five years.
What pushes the multiple up
- Net revenue retention above 120% with low logo churn
- Rule-of-40 (growth + margin) comfortably above 50
- Genuine, defensible AI — not a thin wrapper
- Reference logos and a repeatable, efficient sales motion
What pulls it down
- Single-founder / key-person concentration risk
- Customer concentration or lumpy enterprise deals
- Long, services-heavy implementations that dilute SaaS margin
- Macro SaaS-multiple compression and AI-disruption fears