Galileo

LLM Observability

Logging, tracing, and metrics for LLM applications in production — latency, cost, quality, and error rates across traces and users.

LLM Evaluation

Systematic testing of LLM outputs for correctness, relevance, safety, and consistency using automated scorers, rubrics, or human review.

Agent Tracing

End-to-end visibility into multi-step LLM agent runs: tool calls, intermediate reasoning, token usage, latency, and errors at each step.

Hallucination Detection

Automated scoring of LLM outputs for factual accuracy and groundedness, typically via retrieval-backed verification or reference comparison.

LLM Guardrails

Runtime input/output filtering for LLMs — PII redaction, toxicity blocking, prompt injection defense, policy enforcement.

Model Monitoring

Production monitoring for performance, drift, data quality, and fairness regressions.

Prompt Management

Versioning, templating, A/B testing, and deployment workflows for LLM prompts treated as production artifacts.

Drift Detection

Automated detection of distribution shift, feature drift, prediction drift, and performance degradation in deployed ML/AI models.

Audit Logging

Tamper-evident logging of governance events (approvals, model changes, policy decisions) required by EU AI Act Article 12 and similar regulations.