VEEV vs VSEE

Veeva Systems Inc. vs VSee Health, Inc. — Valuation Comparison 2026

VEEV

Health Information Services
Veeva Systems Inc.
Quality
10.0
out of 10
Value Trap
18
SAFE
Price
$164.38
Last close
Models
13/13
Active
VS

VSEE

Health Information Services
VSee Health, Inc.
Quality
4.4
out of 10
Value Trap
43
WARN
Price
$0.18
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType VEEV Fair ValueVEEV Upside VSEE Fair ValueVSEE Upside
Bayesian DCF Intrinsic $167.74 +2.0% $0.04 -75.6%
Earnings Power Value Intrinsic $44.09 -73.2%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $86.57 -47.3% $0.61 +170.3%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for VEEV vs VSEE — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

VEEV vs VSEE — Which Stock Is More Undervalued?

VEEV scores higher with a 10.0/10 quality rating vs VSEE's 4.4/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Veeva Systems Inc. (VEEV) and VSee Health, Inc. (VSEE) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

VEEV currently trades at $164.38 with a QOC of 10.0/10, while VSEE trades at $0.18 with a QOC of 4.4/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).