Most AI platforms focus on improving model performance, and better models do lead to better outputs. But for enterprise adoption, performance alone is not enough.
Before adopting any AI system, organizations ask a fundamental question: Can this system be trusted with our data and decisions? The answer often determines whether evaluation moves forward at all.
At Causify, trust is foundational to how we build and deliver our causal AI platform. Today, we are proud to share an important milestone: Causify is SOC 2 Type II compliant, reinforcing our commitment to enterprise-grade security, reliability, and operational excellence.
TL;DR DataFlow is a computational framework for simulating causal models on time series data using a directed acyclic graph architecture enhanced with knowledge time semantics for temporal causal reasoning.
TL;DR Traditional forecasting models optimize only for accuracy, ignoring an important issue: predictions that fluctuate significantly from day to day undermine confidence in production. This paper introduces the AC score metric that balances accuracy and temporal stability, achieving 91% reduction in forecast volatility while improving multi-step prediction accuracy by up to 26%.
TL;DR Structural causal models outperform traditional energy forecasts by revealing critical interdependencies correlation-based approaches fail to capture.