If you build a tool that measures something, you have to measure yourself with it. Otherwise you are selling a scale you have never stood on. So every morning I pull the RankingLocal.ai score for rankinglocal.ai and look at the number before I look at client dashboards. Today it is 57.6 out of 100.
That is not a great number. It is a mixed tier. It is also an honest number, and I would rather publish it than hide it, because the gap between where we are and where we need to be is exactly what this post is about. I run Yellow Pencil, the agency, and I built RankingLocal as the monitoring layer we kept needing for clients. Now the tool is a product, and the product has its own domain, and that domain is a new entity on the web with almost no track record. The score reflects that.
I think the most useful thing I can do is walk through the actual snapshot, explain what is driving each cluster, and tell you what I am doing about it over the next ninety days. If you are in the same spot, a new brand trying to show up in AI answers, the playbook is probably similar to mine.
The snapshot
RankingLocal.ai overall GEO score: 57.6 / 100 — tier mixed. Pulled from production on 2026-04-20. Clusters are uneven: transactional queries are reasonable, entity signals are thin, competitor share is low.
The overall score is a weighted composite of four cluster aggregates: Answer Coverage, Entity Readiness, Competitive Share, and Freshness. A 57.6 means we are passing the floor but nowhere near the tier where we would be a default citation in AI answers. For reference, Ahrefs and Moz — the two SEO brands we benchmark against because they own most of the generative answer space in our category — are sitting in the 80s on the same methodology. They have fifteen years of authority behind them. We have months.
Where we are strong: Answer Coverage on transactional queries
The cluster that carries us is Answer Coverage. When someone asks an LLM a transactional query like "AI visibility tracker" or "how do I check if ChatGPT cites my site," RankingLocal shows up reasonably often. Not dominant, but present. We score around 68 in this cluster.
The reason is narrow and mechanical. I wrote specific, direct answer pages for the exact phrasings people use when they have a job to do. Each page has a clear H2 question, a three-sentence answer right below it, a schema block, and then the supporting detail. LLMs pick these up because the structure matches how they like to quote. It is not clever. It is just doing the thing the format rewards.
The free tools at /free-tools/ also pull weight here. A tool page is a transactional query by definition — the user is trying to do something, not read something — and tool pages get linked to and cited as the canonical how-to. That is compounding slowly.
Where we are weak: Entity Readiness
Entity Readiness is the cluster that drags everything down. We score about 41. The reason is not a mystery. RankingLocal.ai is a new entity. Wikipedia does not have us. Wikidata does not have us. CCBot, the Common Crawl indexer that feeds most of the foundation models, has not done a full pass of our domain yet. The structured data around the brand is thin because the brand is thin.
This is the hardest cluster to move fast. You cannot buy your way into Wikipedia, and you should not try. What you can do is produce a body of work that gets referenced elsewhere — podcast mentions, guest posts, directory listings with real editorial standards, conference talks — so that when a model is asked who RankingLocal.ai is, there are multiple independent signals to triangulate. I am in the middle of that work now. It is slow.
The other half of Entity Readiness is internal: consistent NAP, consistent founder bio, a proper About page with schema, and a knowledge panel effort on the main search engines. We have most of it. The external corroboration is the gap.
Where Ahrefs and Moz eat our lunch
Competitive Share is the cluster where the math is honest and a little painful. For any head query in our space — "SEO monitoring," "rank tracker," "visibility tool" — Ahrefs or Moz is cited first, second, and often third. We sometimes appear fourth or fifth. Sometimes not at all. Our share of voice across the head basket is under ten percent.
This is fine. It is also not something I am trying to fix head-on. Competing with Ahrefs on "SEO monitoring" is a bad trade for a small team. What I am doing instead is owning the narrower category — generative engine optimization, AI citation monitoring, GEO scoring — where the incumbents have less of a head start. Our share of voice on those phrases is closer to thirty percent, and climbing. The trick is to pick a category where the query volume is real but the incumbent moat is shallow.
Freshness is a free win and we are taking it
Freshness is the cluster where a small team can beat a big one. Ahrefs and Moz publish, but they do not publish daily, and their pages are often months or years old by the time an LLM sees them. We publish often, we date-stamp, and we update old posts when the methodology changes. Our Freshness cluster is around 72, higher than either competitor. That is not a brag, it is just the shape of the game. Big teams move slowly.
The specific move I made: every post gets an updated-on date in the schema, and the homepage surfaces the three most recent updates. When a model is choosing between a 2023 Ahrefs post and a 2026 RankingLocal post on the same topic, the recency signal tilts toward us more often than you would think.
The roadmap to 70+
I am not going to hit 70 by next month. Here is the ninety-day plan I am actually running.
First, ten anchor pieces of pillar content on the GEO methodology, each with original data pulled from client runs. These are designed to be cited. Second, a push on external entity signals: five podcast appearances, three guest posts on established marketing sites, and a proper submission to the directories that feed Wikidata. Third, a weekly cadence of small updates to the tool and a public changelog, because freshness compounds. Fourth, a specific effort on the About page and founder schema so the brand entity is machine-readable.
If all of that lands, my model says we get to 68 in ninety days and 72 in six months. That would put us in the tier where we are a default citation on our narrow category. It would still leave Ahrefs and Moz ahead on the head terms, and that is fine. A small business with a clear category is a better outcome than a fuzzy competitor nobody cites.
What this means for you
If you are new, your score is going to look like mine. That is not a failure signal, it is a baseline. What matters is whether the clusters move in the right direction quarter over quarter. Run the measurement, publish the number, and work the plan. The tool builder is playing catch-up too.
You can check your own score at /free-tools/ai-visibility/. It is free, it runs against the same methodology I am using on myself, and it will give you the four cluster breakdowns so you know where to start.
hello@rankinglocal.ai is read by me directly.