Your organization is rapidly deploying Generative AI across customer experiences, enterprise operations, knowledge management, and software engineering. But how do you know your business outcomes are actually reliable?
Traditional AI evaluation methods were never designed for applications powered by Large Language Models, Retrieval-Augmented Generation (RAG), structured data querying, and agentic workflows. Without a rigorous evaluation strategy in place, how do you trust yourself to scale? In this whitepaper, Persistent introduces an enterprise-grade Evaluation Framework that moves you from subjective assessment to measurable performance. The outcome? You improve reliability, accelerate adoption and get more value out of every AI investment.
Whether you’re building RAG solutions, AI-powered enterprise assistants, or agentic workflows, this framework gives you a practical way to prove what’s working.
Key Highlights
- A Framework that Scales: Apply one structured, enterprise-focused approach across every use case and data environment.
- Metrics that Build Confidence: Know whether your answers are accurate, relevant and reliable.
- Coverage that Spans Your AI Architectures: Assess applications built on RAG, structured data , and agentic workflows with a unified framework.
- Improvement that Compounds and Earns Trust: Understand how ongoing evaluation can enhance application performance, strengthen governance, and support enterprise-wide AI success.
