AI Search Visibility Lab
Find out which website changes are worth trying next. Follow real projects from the BellyUp! family to specialist publishing, with the search results and crawler activity beside the work.
Real websites. Results you can inspect.
Explore the case studies, then follow the ongoing experiments below.
Explore AI search visibility case studies
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Local discovery · Working implementation
One restaurant can answer more than one question. BellyUp! gives it the room.
The BellyUp! family connects shared local records to useful city and topic websites. See the live system and the visibility test it makes possible.
121 websites across 15 cities Network snapshot: October 20, 2026. Counts describe the published system. -
B2B publishing · Early search results
B2BMedMarketing gave specialist questions a connected place to land.
A new B2B publication connected buyer guides with definitions and vendor records. Its early Google results show which pages began finding an audience.
2,998 Google impressions in the observed window Search snapshot: September 22 through October 17, 2026. -
Published research · Crawler discovery
We linked the data. AI crawlers came back for the files.
Published SEO datasets gave crawlers something useful to collect beyond the article. The dated study shows file requests and a wider catalog footprint.
OAI-SearchBot catalog reach: 11 sites → 43 sites Historical study published October 11, 2026; retained log window September 6 through October 10. -
Comparative research · Search and crawler results
More crawlers visited all five sites. Search growth told a different story.
A matched website comparison separates crawler access from search growth. It shows why a busier server log needs a second measurement before becoming a visibility claim.
Crawler activity rose on 5 sites; impressions rose on 2 Snapshot generated October 20, 2026; study runs July 9 through October 17.
Exposure Velocity Hypothesis
Digital entities experience measurable increases in discoverability before corresponding increases in user traffic. This hypothesis is being tested across the Digital Karma portfolio through continuous observation of search impressions, AI crawler activity, indexed assets, and other discovery signals.
This Lab does not treat Exposure Velocity as a finished industry standard. It treats it as a working model with measurable signals, explicit exclusions, and accumulating evidence.
Exposure Velocity
Exposure Velocity is the rate at which a digital entity is increasing its discoverable surface area across search engines, AI systems, and other retrieval platforms over time.
Exposure is the opportunity to be discovered. That is why this model focuses on discoverability signals before traffic outcomes arrive.
Exposure, Discovery, Authority, Business
- Exposure How quickly the opportunity to be discovered is expanding.
- Discovery How quickly users are actually finding the entity.
- Authority How quickly trust, citations, and entity strength are compounding.
- Business How quickly visibility turns into measurable outcomes.
- Search impressions (GSC)
- AI crawler activity (GPTBot, ClaudeBot, Perplexity, and similar systems)
- Good bot requests
- Indexed pages
- Newly discovered URLs
- AI citations
- Social search impressions
- Video search impressions
- Dataset downloads
- Syndication
- Clicks
- Traffic
AI Symantix Exposure Velocity Observation
This first Lab experiment watches search impressions and AI crawler activity as exposure signals, then shows clicks separately as a downstream discovery outcome. The goal is observation, not premature certainty.
Exposure Signals Observed
Search impressions, AI crawler requests, and the validated Good Bot rollup are shown as separate raw signals rather than collapsed into a single score.
Discovery Outcome Comparison
Clicks are shown only as a downstream comparison signal. They never belong inside Exposure Velocity itself.
Recent Search Exposure and AI Crawler Observations
How Search Impressions and AI Crawler Activity Are Measured
- Search impressions come from the Digital Karma Warehouse GSC daily tables.
- AI crawler activity comes from the Warehouse ai_crawler_daily rollup.
- Good Bot requests use the validated successful-content definition from the internal Warehouse dashboard.
- Clicks are displayed only as a downstream discovery outcome, not an exposure input.
- The chart updates from a public JSON export generated from the live Warehouse.
Search Exposure and AI Crawler Patterns Being Watched
- Whether machine discovery rises before user traffic becomes meaningful.
- Whether AI crawler bursts align with later impression growth.
- Whether search visibility continues expanding even while clicks remain near zero.
- Whether the AI Symantix pattern repeats across more Digital Karma properties.
AI Discovery and Search Exposure Questions to Test Next
- Does sustained crawler activity ever become predictive of impression growth, rather than merely moving in the same rising environment?
- Do the published pages currently visible only to Good Bots later develop GSC impressions?
- Do stronger workbench and receipt pages turn search visibility impressions into qualified human page requests?
- How long is the lag between exposure growth and discovery growth on young entities?
- Which combination of exposure signals becomes the most reliable leading indicator across the portfolio?