AI Evals as a Service (EaaS)

AI Evals as a Service helps organizations continuously evaluate the quality, safety, reliability, and performance of AI systems before and after deployment.

AI evaluation helps identify hallucinations, unsafe responses, performance issues, and quality gaps before they impact users or business operations.

LLM evaluation is the process of assessing large language models for accuracy, relevance, consistency, safety, reasoning, and business alignment.

RAG evaluation measures retrieval quality, grounding, contextual relevance, and citation accuracy to ensure AI-generated responses are based on trusted information.

AI agent evaluation assesses tool usage, workflow execution, decision-making, task completion, reliability, and safety across real-world scenarios.

AI evaluations use benchmarks, test datasets, grounding checks, and automated assessments to identify unsupported or inaccurate AI-generated responses.

Prompt evaluation measures how different prompts affect AI accuracy, consistency, safety, and overall business outcomes.

Continuous AI evaluation provides ongoing testing and monitoring of models, prompts, RAG systems, and AI agents to detect regressions and maintain performance as systems evolve.

AI evaluations provide measurable evidence, risk assessments, quality benchmarks, and monitoring practices that support responsible and governed AI adoption.

An AI system is considered production-ready when it meets defined standards for quality, safety, reliability, performance, governance, and business requirements.