PSN vs SLAI

Parsons Corporation vs SOLAI Limited — Valuation Comparison 2026

PSN

Information Technology Services
Parsons Corporation
Quality
8.7
out of 10
Value Trap
37
LOW
Price
$58.83
Last close
Models
12/13
Active
VS

SLAI

Information Technology Services
SOLAI Limited
Quality
2.2
out of 10
Value Trap
12
SAFE
Price
$0.77
Last close
Models
9/13
Active

Model-by-Model Comparison

ModelType PSN Fair ValuePSN Upside SLAI Fair ValueSLAI Upside
Bayesian DCF Intrinsic $40.76 -30.7% $0.20 -73.5%
Earnings Power Value Intrinsic $6.76 -88.5%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
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 $56.22 -4.4% $0.68 -11.2%
🔒

Unlock Full 13-Model Comparison

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

Access Full Analysis — From $27/mo →

PSN vs SLAI — Which Stock Is More Undervalued?

PSN scores higher with a 8.7/10 quality rating vs SLAI's 2.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Parsons Corporation (PSN) and SOLAI Limited (SLAI) 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.

PSN currently trades at $58.83 with a QOC of 8.7/10, while SLAI trades at $0.77 with a QOC of 2.2/10.

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).