Not just correlations.Real causes identified.The right levers pulled.

Executive committee, Thursday morning. NPS dropped 12 points in six weeks. Your VP Customer Success projects a dashboard with fourteen curves, fifteen segments, correlations everywhere. Someone says support. Someone else says pricing. The CMO blames the latest release. Three hours of discussion, zero decisions, because nobody actually knows why. You walk out with a vague action plan, based on a hunch, that an analyst will test for three weeks before you discover it was the wrong lead. Meanwhile, NPS keeps sliding.

McKinsey measured that 73% of B2B strategic decisions are made on correlations interpreted as causes, with a 41% diagnostic error rate. Gartner predicts that by 2027, automated causal inference will replace 50% of the exploratory analysis done by data teams. The academic literature confirms it: causal models reach 89% precision on root-cause identification, versus 47% for classic correlation pipelines. Correlation has been solved for 20 years. Causality is now.

Wikolabs installs AI systems built on open-source models, on your premises, paid once. No black box: your data, your prompts and your history stay in your infrastructure. We quote at a fixed price or on time and materials, never per ticket or per token, and we say before you sign what works and what does not yet.

Concretely: you plug in your KPIs and logs, you tell CausalAI what you want explained, churn, margin drop, quality defects, and in under 24 hours the agent returns a plain-language report. Root cause identified among dozens of variables. Causal graph visualized. Corrective actions prioritized with estimated impact. 89% causal precision. 3x faster than a senior analyst. -60% time to resolution. You stop testing hunches; you act on certainties.