Built on statistics, not shortcuts.
ControlFlow Analytics helps organizations extract reliable insight from complex data by applying rigorous statistical analysis, validated machine learning pipelines, and controlled AI integration. Every system is designed with feedback loops, drift detection, and retraining logic built in.
Start a conversationThorough exploratory analysis, classical statistical testing, and distributional validation to understand signal, bias, and uncertainty before any modeling begins.
Supervised and unsupervised models are applied with strict validation, segmentation checks, and multivariate analysis to identify structure and inconsistencies in the data.
AI models receive only the most relevant, validated information, reducing hallucination, cost, and latency while improving accuracy and interpretability.
Discarded samples and edge cases are analyzed separately to monitor drift, performance degradation, and retraining triggers over time.
Deep evaluation of data quality, statistical assumptions, model validity, and decision risk. Ideal for diagnosing unreliable analytics or AI systems.
Time-series forecasting, regression, segmentation, survival analysis, and Bayesian modeling built for interpretability and robustness.
Cost-aware AI pipelines using prompt engineering, APIs, and SDKs, integrated only where they add measurable value.
If you’re dealing with unreliable models, noisy data, or AI systems you don’t fully trust, let’s talk.