KSCP vs SNT

Knightscope, Inc. vs Senstar Technologies Corporatio — Valuation Comparison 2026

KSCP

Communications Equipment, NEC
Knightscope, Inc.
Quality
5.9
out of 10
Value Trap
24
SAFE
Price
$2.92
Last close
Models
9/13
Active
VS

SNT

Communications Equipment, NEC
Senstar Technologies Corporatio
Quality
2.6
out of 10
Value Trap
Price
$2.77
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType KSCP Fair ValueKSCP Upside SNT Fair ValueSNT Upside
Bayesian DCF Intrinsic $0.91 -68.8% $0.53 -80.9%
Earnings Power Value Intrinsic $2.15 -21.4%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $2.00 -31.6% $0.91 -67.0%
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 KSCP vs SNT — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

KSCP vs SNT — Which Stock Is More Undervalued?

KSCP scores higher with a 5.9/10 quality rating vs SNT's 2.6/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Knightscope, Inc. (KSCP) and Senstar Technologies Corporatio (SNT) 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.

KSCP currently trades at $2.92 with a QOC of 5.9/10, while SNT trades at $2.77 with a QOC of 2.6/10.

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).