Galileo python sdkv1.2.1

An e-commerce company integrates Galileo to trace and evaluate their AI customer support chatbot. They use automatic LLM call tracing, custom span logging, and run evaluation experiments to monitor response quality and detect hallucination or toxicity.

A healthcare startup uses Galileo to observe and protect their GenAI-powered clinical advice system. They leverage runtime guardrails via galileo-protect and evaluation metrics to ensure context adherence and avoid medical misinformation.

A fintech firm deploys Galileo to evaluate and trace their document summarization pipeline. They use the @log decorator for nested workflows (retrieval, summarization, validation) and run experiments to optimize prompts and reduce hallucination.

A legal tech company integrates Galileo to provide observability and guardrails for their AI contract review tool. They set up GalileoContext to trace the entire review workflow, use metrics like PII detection and context adherence, and evaluate different model configurations.

An educational platform uses Galileo to monitor and evaluate their AI tutor that adapts explanations per student. They wrap the OpenAI client for auto-logging, apply the @log decorator for each pedagogical step, and run evaluation sets to ensure safe, accurate content.

Offer tiered subscription plans (e.g., Pro, Enterprise) based on usage metrics like number of evaluations, traces, or guardrail checks. Galileo's observability features help ensure reliability and justify premium tiers with monitoring guarantees.

Charge per API call or per evaluation experiment, akin to typical cloud AI platforms. Galileo's tracing allows accurate metering of LLM calls, guardrail invocations, and evaluation runs for billing.

Provide professional services to integrate Galileo into client workflows, design custom evaluation metrics, and optimize prompts. This model leverages Galileo's SDK flexibility and reference integrations to deliver tailored solutions.