AI mention share
Across the prompts your buyers actually use to find operators, what percentage cite your company by name? How does that compare to local competitors?
Case Studies
Proof of how the system performs.
Operators don't want a slide deck — they want to know what changes month-over-month, how calls are tracked, and what AI is saying about their company versus the competition. Here's what that looks like.
How we report
Monthly Recommendation Report on what AI says about you
Call attribution by source — which marketing made the phone ring
Map-pack and service-area visibility tracked over time
Branded AI mentions tracked across ChatGPT, Gemini, Perplexity, Google AI
What we measure1 2 3 4
The four signals that prove the system is working.
Generic agency reports show traffic. Axis37 reports show whether AI tools are recommending you, whether the calls you're getting are tracked, and whether your authority is compounding month over month.
Map-pack and service-area visibility
For the searches that turn into calls, where do you rank — and is that ranking improving month over month across the cities you serve?
Tracked outcomes by source
Which calls came from organic search, which from Google Business Profile, which from AI mentions, which from referrals — and which converted into booked work?
Authority signals
Reviews, citations, mentions, schema completeness — the trust signals AI tools weigh when deciding which operators to recommend.
Sample report
See exactly what a monthly report looks like.
The Recommendation Report shows what AI says about your company, where you're cited, where competitors are cited instead, and what the next move is. We share an anonymized sample so you can see the structure before you ever sign anything.
AI prompt coverage — which prompts cite you, which don't
Competitor comparison — who AI names instead
Map-pack and service-area movement month over month
Tracked call volume tied to source
The single highest-leverage move for the next month
Recent engagementsPlumbing Law Firm Restaurant
What the system looks like in practice.
Case studies from real engagements. Operator names are withheld at each operator's request. Stats, operational specifics, and the full arc of every engagement below are real.
A 22-year Southern California plumbing operator: 4% in AI recommendations — until we closed the gap.
What we found
The business had proof, but the proof was scattered. Reviews, service pages, Google profile data, and citations were not telling the same story.
What changed
Rebuilt the service hierarchy, cleaned local entity signals, improved schema, tightened service-area pages, corrected citations, and added call tracking by source.
31 → 67Footprint Score, 90 days
4% → 27%AI mention share, core service prompts
7.8 → 4.1Average Map Pack position, priority terms
+46 / moCalls attributed to organic and search
The business was better than its footprint. Closing that gap made it easier for buyers and AI systems to understand why they should be chosen.
Read the full case →A credible local firm almost never named in AI recommendations.
What we found
Expertise was real, but the site was built around practice-area descriptions instead of trust decisions. Attorney proof, reviews, directory profiles, and local relevance were not connected.
What changed
Rebuilt practice-area pages around prospect decision moments, turned attorney bios into proof assets, aligned legal directory profiles, added schema, and improved consultation tracking.
28 → 61Footprint Score, 120 days
2% → 19%AI mention share, local legal prompts
11 → 22 / moOrganic consultation requests
2.1% → 4.4%Conversion rate on priority pages
The firm did not need to sound louder. It needed to become easier to verify.
Read the full case →A locals' favorite missing from the dining moments it actually owned.
What we found
Customers repeatedly described the restaurant as a patio spot, date-night option, locals' favorite, and post-wine-tasting dinner choice — but the website and local profiles did not structure those signals.
What changed
Mapped the restaurant to dining occasions, rebuilt homepage messaging, improved menu and schema structure, refreshed Google Business Profile signals, and pursued real local mentions.
41 → 71Footprint Score, 90 days
5% → 22%AI mention share, dining prompts
0% → 18%AI mention share, “date night” prompts
+18%Reservation clicks from organic and local
Restaurants do not need to win every dining search. They need to become the obvious answer for the right occasion.
Read the full case →The system page walks through every phase of the work behind these results.
Want to see what the report would look like for your company?
Start with a search checkup. We pull your AI footprint, local rankings, and tracking gaps — then walk you through what the monthly report would surface.
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