AI governance & evaluation

Model risk, evaluation harnesses, and operating controls that keep production AI defensible.

Governance that scales with every new model release

Radiantze treats evaluation and governance as part of delivery. Model inventories, access policies, test suites, and human review paths define clear promotion criteria from the start.

Programs cover red-team testing, audit logging, cost and latency monitors, and enablement so model risk, security, and product owners share one operating picture.

Partnership

Evaluation as a continuous practice

Radiantze embeds evaluation into CI and release cycles so quality, safety, and cost stay visible as usage grows.

  • Promotion criteria agreed with business, risk, and engineering before cutover.
  • Grounding and citation checks for RAG with measurable retrieval quality.
  • Token, latency, and error dashboards tied to product SLOs.

Radiantze delivery examples

Model inventory & ownership

Catalog of models, owners, data sources, and intended use across production.

Evaluation harnesses

Offline and online tests for accuracy, grounding, bias, and safety before promotion.

Red-team & robustness

Adversarial testing and failure playbooks product and security teams can rehearse.