18 days ago
- AI visibility tools promise to measure brand visibility in AI answers but often provide false precision by presenting tidy claims like mention rates and rankings.
- Scraping the frontend of AI products like ChatGPT or Claude captures only one synthetic session with many uncontrolled variables, leading to biased measurements.
- Even with identical prompts, AI systems can produce varying answers due to factors like model batching, personalization, and nondeterministic behavior.
- Consumer apps and APIs behave differently; APIs offer controlled, repeatable measurement but may not match what users see in the product interface.
- The selection and weighting of prompts in AI visibility tools significantly influence scores, making constructed metrics dependent on the chosen methodology.
- Geography is a critical factor often overlooked, as local intent can drastically change AI answers, rendering global visibility ranks meaningless for local businesses.
- Model drift and product updates can cause changes in AI behavior over time, making trend lines in dashboards difficult to interpret without proper context.
- Honest AI visibility measurement should focus on directional, probabilistic findings, such as appearance frequency across prompts and geographies, rather than precise rankings.
- Canonry's approach emphasizes repeated observations, explicit location context, and evidence retention to provide more transparent and accurate measurement for local SEO and AEO.