Our Approach

Markets often contain internal inconsistencies—mutually exclusive outcomes that don't sum correctly, threshold orderings that violate logical constraints, or conditional relationships that contradict each other. Our systems identify these structural mispricings in real time.

We apply Bayesian inference to continuously update probability estimates as new information enters the market. This allows us to identify divergences between market prices and our model-derived fair values.

Language models provide semantic understanding of market questions, identifying edge cases with ambiguous resolution criteria and cross-validating signals against external information sources.

Systematic position sizing with portfolio-level constraints ensures disciplined capital allocation. Continuous exposure monitoring and drawdown controls protect against adverse scenarios.

A unified data pipeline ingests real-time market data from major prediction market platforms, normalizing heterogeneous formats into a consistent internal representation for analysis.

Signals pass through multiple validation stages before execution consideration. Quality gates, liquidity checks, and slippage modeling ensure that theoretical edge survives real-world execution.