Eticas vs Openlayer

Independent, outcome-agnostic evaluation of AI running in its real environment rather than a lab setting.

Assessment of hallucination rates, output consistency, retrieval quality, prompt safety and data leakage risk.

Reviews of action traceability, goal alignment, failure modes and the adequacy of human oversight.

Evaluation of data quality, threshold logic, explainability and bias across populations in decision systems.

Dozens of pre-built evaluations cover quality, safety, security, and performance, with LLM-as-a-judge scorers, session-level metrics, and support for writing your own custom tests against your own data.

Import a repository and run evaluation suites automatically on every change, benchmark each iteration, and block releases when a metric regresses beyond the threshold you define.

Trace every request through multi-step agent workflows, including tool calls and retriever steps, with token counts, latency, cost per request, alerting integrations, and subpopulation analysis.

Register each AI system with an owner, purpose, data sensitivity, and risk tier, score inherent risk across multiple dimensions, and move systems through staged approval gates before they reach production.