H2O Eval Studio
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.
- H2O Eval Studio is a modular studio for evaluating RAG systems, LLMs, and agents on performance, reliability, security, fairness, and effectiveness.
- It includes PII and sensitive-data leakage evaluators, radar-plot comparison across models, and downloadable HTML and MRM reports.
- Its nine-step Model Risk Management workflow generates tests from your own corpus and lets domain experts calibrate acceptance thresholds.
- The founding principle is that LLMs cannot be trusted to evaluate LLMs; the framework relies on embeddings, semantic similarity, and humans in the loop.
Evaluate, visualize, report, diagnose.
"The LLM seems fine" is not evidence. Eval Studio produces the evidence, in a form a model risk team accepts.
- Evaluate RAGs, LLMs, and agentsA standardized set of evaluators applied to your model hosts and test suites. Objective, repeatable, and comparable across models and contexts.
- Test for leakage and biasPII and sensitive-data evaluators run prompts built to extract what a model should never reveal, with fairness and security checks alongside.
- Compare on one chartRadar plots and bar charts across metrics and models, so the strengths and weaknesses of each candidate show at a glance.
- Report for the reviewerAn HTML report with tested systems, evaluators, results, error drill-down, and logs, plus a ZIP archive and an MRM report artifact.
- Problems and insightsWhich models fail privacy, fairness, or security. The prompts no system could answer. The failures that actually matter, surfaced.
- Import test dataBring prompts, constraints, corpus, and expectations as files. Generate and augment test suites outside, then run them inside.
A Model Risk Management workflow, in nine steps.
Automated testing on the RAG's own corpus, calibrated by humans, with holistic diagnostics.
- 01Topic modelingExtract and cluster chunks from your corpus with HDBSCAN and k-means, silhouette-scored. The topics guide what to test.
- 02Generate and validate testsQuestion-and-answer pairs generated from the corpus topics, not generic prompts, and validated before they are used.
- 03Evaluate and calibrateGroundedness, fairness, fact checks, and leakage run automatically. Domain experts set the acceptance thresholds.
- 04Cluster and stress-testCommon failure patterns, the question types that underperform, and robustness under syntactic, semantic, and adversarial variation.
The observability layer over every model and agent.
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.
What ships today
No evaluation, no deployment.
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.
You operate it, or H2O.ai operates it for you.
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.
H2O AI Cloud for Government
H2O AI Cloud for Government is FedRAMP High certified, delivering the highest standard of cloud security so federal agencies can harness predictive and generative AI without compromising data sovereignty or compliance.
What's new on H2O Eval Studio
Questions people ask before they call us.
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.
Bring the RAG your risk team has not approved.
We will run the MRM workflow on your own corpus and hand your reviewers the report.