01
Study design
Define the markets, practice sample, query families, search surfaces, model versions, collection dates, and whether each observation measures discovery, factual accuracy, booking visibility, or another outcome.
Research draft
Dental AI Visibility Index 2026
This draft is the publication shell for an original HeyJarvis study of dental-practice visibility across search and AI answers. It intentionally contains no invented statistics. Before publication, the report needs a locked sample, repeatable query set, dated model/search captures, scoring rules, quality checks, and permission-safe examples that another researcher could understand and challenge.
02
Metrics to publish
Possible metrics include share of practices surfaced, citation/source frequency, entity consistency, directory coverage, structured-data adoption, local-page completeness, booking friction, and the relationship between these signals. Publish only statistics supported by the final dataset.
03
Evidence standard
Keep raw observations, scoring criteria, screenshots or source URLs where permitted, a change log, and clear limitations. Separate descriptive correlations from causal claims and avoid presenting one city's sample as representative of all dental practices.
04
Distribution plan
The finished report should have a permanent URL, downloadable methodology/data where appropriate, quotable charts, city or practice-type breakdowns, and a short press summary for dental trade publications and external citations.