Distributional

Distributional is a platform designed to help teams understand and improve their AI products by analyzing production log data. It tackles the core problem that production AI often operates as a black box, where standard monitoring provides aggregate statistics but misses the nuanced behavioral interplay between users, context, tools, and models. The tool ingests logs and traces, then uses adaptive, unsupervised analysis to surface meaningful signals—like daily shifts, behavioral clusters, or outliers—that indicate issues or opportunities for product refinement.

What sets Distributional apart is its focus on behavioral signals rather than just performance metrics. It enriches raw logs with additional statistical metrics, custom evaluations, and LLM-as-judge assessments to create a high-fidelity view of your AI's state. The platform then continuously runs analyses like high-dimensional clustering, topic modeling, and anomaly detection to uncover insights automatically. For enterprises, it offers flexible deployment options (including local or Kubernetes), robust security controls, and integrations with existing LLM providers and frameworks via OTEL, SQL, or an SDK.

This tool is most valuable for teams running complex AI applications in production, such as AI agents or multi-step LLM workflows, who need to move beyond basic dashboards. It helps product managers and engineers rapidly triage issues by connecting signals to the relevant contextual traces, and allows them to track the evolution of specific behaviors over time. Essentially, Distributional aims to fix the broken AI product feedback loop, turning overwhelming log data into actionable, daily insights for continuous improvement.