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About

Our Mission

We build AI systems that think economically and reason mathematically. In a world of prediction-obsessed machine learning, we focus on verified reasoning—AI whose conclusions can be independently audited and mathematically demonstrated.

Our goal is not to replace human judgment but to augment it with systems that are provably consistent, interpretable, and durable. We believe the future of financial AI is not more black boxes, but more transparent reasoning.

Our Principles

Research Over Marketing

We publish substantive research and working papers, not marketing materials. Our claims are derived from methodology, not opinion.

Acknowledge Limitations

We are explicit about what AI can and cannot do. Overpromising erodes trust; intellectual honesty builds it.

Independence

Our research is self-funded and conclusions are our own. We have no vendor relationships that influence our findings.

Verifiable Results

Every claim we make can be independently audited. Mathematical guarantees, not marketing guarantees.

Intellectual Heritage

Our approach draws from three distinct traditions, synthesized into a unified framework for verified financial intelligence.

Quantitative Finance

Fama-French, AQR

Empirical rigor, factor-based thinking, evidence over narrative

Formal Verification

Methods from aerospace and defense

Mathematical proofs of system behavior, automated verification

Value Investing

Columbia Business School tradition

Intrinsic value thinking, margin of safety, long-term compounding

Our Approach

What We Do

  • Build neurosymbolic AI systems for financial reasoning
  • Publish open research and working papers
  • Deploy verified strategies in live markets
  • Partner with academic institutions on foundational research

What We Don't Do

  • Make unverifiable claims about AI capabilities
  • Chase short-term hype cycles
  • Treat prediction accuracy as the sole metric
  • Build black boxes without interpretability requirements

We are long-term value investors in intelligence itself— allocating capital and effort to systems whose truth can be demonstrated, not merely hypothesized.