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Market Landscape
Free discovery · Pro+ advanced views · Power full entitled depthSee what QSE is observing across market families, structural states, and evidence maturity.
Structured Confidence
for Real-World Decisions
Use Probabilistic analysis and structured Confidence Allocation for a yes or no proposition, or explore published market intelligence and verified outcomes.
QSE's five quantum-state labels
Five confidence states
No-1
Not Likely-0.5
Unknown0
Likely0.5
Yes1
Choose how to use QSE
Direct analysis or published market intelligence
Analyze a proposition or a live market directly through Console, or explore automated market intelligence from Edge Spotter.
Console
On-demand Confidence AllocationRun an on-demand QSE analysis from a proposition you define or a specific live market you select.
Learn more: Proposition Console · Market Console
Edge Spotter
Free to view · Power for advanced intelligenceAutomated signal generation across live markets. Identifies where model confidence diverges from market-implied positions, then tracks published outcomes publicly with no hindsight bias.
Live Edge Spotter performance (real outcomes, not simulations)
Checkingpublished signals
Checkingresolved outcomes
Checkingaverage edge
Checkinglast signal
All statistics reflect resolved signals only. Past performance does not guarantee future results.
Published signals are recorded at publication and resolved against market outcomes. No edits. No deletions. No hindsight.
Market intelligence
Explore what QSE is observing, see where published opportunities emerge, then verify how published signals performed.
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Signals / Opportunity Radar
Power tierDecision context on top of published Edge Spotter signals: lifecycle, persistence, marketability, and module history.
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Track Record
PublicVerify how published signals performed. Recorded at publication, resolved against real outcomes, no edits or deletions.
Console · Free to start
How Console works
Define a yes/no proposition or select a supported live market. The system evaluates evidence, applies locked math, and returns a structured confidence allocation.
Confidence Allocation
NoNot LikelyUnknownLikelyYes
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Define the analysis targetEnter a yes/no proposition, or select a supported live market to analyze
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Engine evaluatesFactors are scored, weighted, and mapped through locked math
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Confidence returnedFive-state allocation with transparent reasoning
Confidence Allocation details
The five-state result keeps uncertainty explicit. Public output includes structured reasoning without exposing internal weights or raw model state.
Edge Spotter · Free to view
How automated signals work
The Edge Spotter pipeline continuously scans markets and identifies meaningful divergence between model and market.
1Scan marketsMonitor supported live Kalshi markets
2Route to moduleApply the supported domain module
3Score + filterAllocate confidence, then apply fail-closed guards
4Publish + verifyRecord signals and resolve public outcomes
Pipeline and module details
Domain-specific modules cover supported sports, economics, and general markets. Signals publish only after routing, Confidence Allocation, and strict guards complete.
Trust & transparency
Non-negotiable system guarantees
Public-safe output
No internal weights or raw model state. Only structured confidence allocations are exposed.
Fail-closed routing
Ambiguous inputs are suppressed or routed conservatively. The system does not guess.
View methodologyVerified record
Published signals are recorded at publication and resolved against real outcomes with no edits or deletions.
Start using QSE or explore live signals
Run your own analysis or review real-world Edge Spotter results.
This system prioritizes integrity over volume and clarity over false precision.