Kubernetes changes how infrastructure behaves. Workloads are ephemeral, services scale dynamically, and the system is in constant motion. Monitoring approaches built for static environments do not translate well into this world.
AI has a strange habit: it is expensive because it is forgetful.
We train giant models on staggering amounts of text, logs, time series, and behavioral traces. Then we ask them to solve the same classes of problems again and again: Why did demand move? What caused the outage? Which levers drive revenue? What happens if we change this constraint, this price, this power contract, this promotion?
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.