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A modular studio for evaluating RAG systems, LLMs, and agents on performance, reliability, security, fairness, and effectiveness, built on one principle: LLMs cannot be trusted to evaluate LLMs.
"The LLM seems fine" is not evidence. Eval Studio produces the evidence, in a form a model risk team accepts.
Automated testing on the RAG's own corpus, calibrated by humans, with holistic diagnostics.
Eval Studio points at the generative lane: h2oGPTe collections, Cascade-tuned models, external LLMs, and agents, and reports into one audit trail. Highlighted is Observability.
Every agent the H2O AI Super Agent runs can go through Eval Studio first: the RAG it grounds in, the model it reasons with, the answers it gives under adversarial prompts. The report goes to the model risk team, and the threshold they set is the gate the agent has to clear.
Same platform either way: the same capabilities, the same governance, and the same audit trail. The choice stays open, so you can switch between them as the business changes, without rebuilding what runs on top.
The H2O AI Cloud for Government has achieved FedRAMP High Certification, delivering the highest standard of cloud security so federal agencies can harness predictive and generative AI without compromising data sovereignty or compliance.
H2O Eval Studio is a modular, extensible studio for Retrieval-Augmented Generation (RAG) and LLM evaluation. It provides evaluators for performance, reliability, security, fairness, and effectiveness, visualizes results across models, produces HTML reports, and runs Model Risk Management workflows. It also evaluates agent-based systems.
We will run the MRM workflow on your own corpus and hand your reviewers the report.