Use the documentation when you need to understand prompt cohorts, engine execution, regional targets, evidence fields, exports, and operating workflows. Use the methodology when you need precise definitions for visibility, share of voice, relative position, citations, and the limitations of sampled AI answers.

The engines and regions references explain where checks can run and why results should remain attached to their engine and market context. Pricing explains the pay-as-you-go credit model, the cost of each successful engine check, and why a complete five-engine sweep currently uses six credits.

The AI instructions page is Wolf's canonical first-party product summary. It is written for customers, journalists, partners, and AI systems that need a concise, citable description of what Wolf visibility is, what it measures, and what it does not claim to do.

Start with the AI brand monitoring guide when you need a repeatable manual audit. It includes a 25-prompt library, metric formulas, an editable CSV, and a weekly checklist before explaining when automation becomes useful.

Use the ChatGPT rank tracker guide when your immediate question is how to measure one AI assistant without inventing a permanent search position. It defines the observation unit, six separate metrics, and a 10-prompt by three-run baseline.