We classify stress regimes using solar and geomagnetic data.
One signal platform behind live systems in financial markets and space weather, with research lines in infrastructure and healthcare.
Federal Reserve economists independently documented the underlying market correlation (Krivelyova & Robotti, 2003).
One signal platform. Environmental inputs that are structurally upstream of the systems they affect. The same physics that classifies market stress also detects satellite disruptions and is being validated against healthcare demand surges.
The deepest barrier to entry is that the concept is hard to believe until you see it work. Every month the live systems run, that barrier gets lower for us and stays the same for everyone else.
>35+ years of validated environmental data. Walk-forward backtesting across multiple solar cycles. Rules-based, auditable methodology. Cross-domain validation is evidence the signals are physics, not coincidence.
Environmental Intelligence for Critical Systems
The Mindforge Signal Platform reads environmental conditions that influence complex systems before effects become visible in traditional data. Each application uses the same core signal, validated independently across its domain.
Why This Works
The Problem
Most monitoring systems are closed-loop. They measure effects after they happen. Price-based risk models react to volatility. Grid monitors detect faults in progress. Hospital systems track admissions retrospectively.
Our Approach
The Mindforge Signal Platform reads environmental conditions that are structurally upstream of the systems they affect. Solar and geomagnetic activity influences human cognition, electronic infrastructure, and biological systems simultaneously. We classify when those conditions reach stress thresholds, often days before traditional indicators react. The classification methodology is proprietary.
Cross-Domain Proof
When the same signal classifies stress in financial markets, detects satellite disruptions, and correlates with hospital admission surges, it validates the underlying physics. A signal that works across unconnected domains is evidence of a real exogenous driver, not a statistical artifact.
Supplemental Science & Documentation
Every claim on this page is backed by published research, walk-forward backtesting, or independently verifiable methodology.
Angel Edwards
Founder
Who Built This
Founder of Mindforge Intelligence and The Mindforge Research Institute. Background in software engineering and data systems, including classification and telemetry work at ClearDATA (HIPAA-compliant clinical infrastructure) and Dealerware/Audi (real-time vehicle telematics). Research focus on environmental coupling effects since 2021.
At Mindforge: designed the validation methodology behind the signal platform that powers the Market Risk Classifier (financial markets) and the Space Weather Early Warning System (satellites, grids, aviation). Exploring cross-domain applications in healthcare outcomes and infrastructure resilience. Alumnus of The University of Texas at Austin.
For Risk Teams, Investors, and Research Partners
Research use only. Not investment advice. Past performance does not guarantee future results.
Every conversation starts the same way: we show you the live system and the validation behind it.
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