Overview
AI vendor reviews may need to examine model-training data, data retention and usage, inference-time handling, output ownership, subprocessors, update cadence, and the effects of outages or model changes in addition to standard software questions.
This engagement reviews your AI vendor and model dependencies in depth: mapping actual workflow dependence on each vendor or model, assessing data exposure and handling practices, identifying dependency concentration and fallback gaps, evaluating change-control and update risk, and producing decision-ready output for leadership and procurement. The focus is on risk that compounds as adoption deepens — not just the initial procurement decision.