Focal

Focal is an AI-native analytics and observability platform designed to help teams understand and improve the performance of their AI products in production. It connects to your existing stack—applications, LLMs, vector databases, and data pipelines—to automatically capture events, inputs, outputs, and metadata. Focal provides detailed tracing for complex AI workflows, allowing you to follow requests across multiple model calls, tools, and services. You can define custom metrics and evaluations to measure quality, latency, cost, and user outcomes, and then monitor these in real time through configurable dashboards and alerts.

The platform supports experiment comparison, making it straightforward to A/B test models, prompts, or configurations and identify which variations perform best against your chosen metrics. Focal also offers error analysis and outlier detection features that help you quickly identify failure modes, regressions, and edge cases. Common use cases include monitoring LLM-powered applications, optimizing retrieval-augmented generation systems, tracking agent behavior, and validating changes before rollout. By centralizing observability for AI features, Focal enables data scientists, ML engineers, and product teams to iterate faster, reduce incidents, and make data-driven decisions about how to evolve their AI systems.