Every engagement starts with the same live AI search visibility audit. The six cases below show the work pattern across recent 1DOT engagements.
We count what compounds: recommendation share, cited commercial pages, qualified enquiries. Not impressions, not keyword counts, not guaranteed rankings.
Each card shows the diagnosis, what changed, and the directional outcome.
Service pages overlapped. AI systems could not distinguish what the business actually did versus adjacent construction sub-categories.
Rebuilt page structure so each commercial service owned its intent. Tightened entity definition and pruned duplicate coverage.
Buyers landing on the right page first time. Cleaner AI-system understanding of the business category.
AI systems described the firm inconsistently. Sometimes one type of consultancy, sometimes another. Multi-service breadth was making the business harder for AI to categorise, not easier.
Rewrote entity definition. Restructured service relationships so each offer was legible to AI systems as a coherent practice, not a list of capabilities.
AI systems now describe the firm consistently across platforms. Service-page citations follow the actual buyer-question structure.
Strong category authority on Google. No presence in AI assistant answers when buyers asked which training providers to consider.
Built answer-ready content for the questions buyers were actually asking in AI assistants. Tied entity signals back to commercial pages.
Now appearing in AI assistant answers for the category. Qualified discovery via prompts, not only keywords.
Measurement was reporting activity: mentions, citations, dashboard counts. None of it showed whether anything moved on the commercial pipeline.
Built measurement methodology tied to commercial pages. Tracked recommendation share, cited commercial pages, and qualified enquiries instead of vanity citation counts.
Clear read on which AI-search work moves pipeline versus what creates noise without leads.
After relaunch, the site lost visibility across both search and AI-assisted discovery. Crawl, indexation, and render issues were making published content invisible to AI systems.
Diagnosed the technical blockers limiting AI readability. Fixed crawl, schema, and render issues that affected citation potential.
Content visible again to AI systems. Citations recovered across platforms once the technical foundation was restored.
After rebranding, the product had two entity identities in AI systems' understanding. Old name and new name were treated as separate things. Citation signal split across both.
Consolidated entity signals around the new brand. Mapped the rebrand transition so AI systems understood it as one continuous entity.
Single clean entity in AI systems post-transition. Citation signal consolidated under the new brand instead of fragmenting.
Six diagnoses. Six different work patterns. One commercial discipline running through all of them: recommendation share, cited commercial pages, qualified enquiries.
These are what 1DOT's engagements actually move. Not impressions, not citation counts, not dashboard activity. The work pattern is the proof.
Every case here began with one live walkthrough of what was blocking visibility.
Start yours and get a clear recommendation for the right next step before any work is scoped.
