Feature
Recommendations
Prioritized actions generated from your own measurements, not generic advice.
Most tools stop at charts. Geonimo's recommendations are generated from what was actually measured on your project: the buyer intents where you never surface, the sources engines trust that don’t name you, and the claims models make about you that are wrong or stale.
Recommendations regenerate after collection runs, so the advice tracks the data, and because every recommendation is traceable to a measurement, "why are we doing this?" always has an answer.
Gap recommendations
Intents and prompts where your brand never appears while competitors do: the visibility deficit ranked by how much of the market each gap represents.
Source opportunities
Trusted, repeatedly-cited pages in your market that don’t mention you. A stable, much-cited domain that names rivals but not you is the highest-leverage page on the internet for your brand.
Perception corrections
Claims the models assert about your brand that are wrong, outdated or off-position: extracted from real answers and clustered by theme, so you fix the narrative at its source.
Prioritized, not exhaustive
Actions arrive ranked, so the queue starts with what moves visibility most, instead of a hundred equal-weight suggestions nobody triages.
Advice is only as good as its evidence
Generic GEO checklists tell everyone the same thing. Geonimo’s recommendations cite the measurement they came from: the prompt set where you’re absent, the citation data behind a source opportunity, the perception claims behind a correction. That evidence trail is what makes the queue defensible in a planning meeting.
The team closes the loop
The recommendations feed the work: the Geonimo team ships the fixes the data calls for, and on Expansion and above your strategist walks the queue with you in monthly reviews. Measurement, diagnosis and execution stay one system instead of three vendors.
Frequently asked questions
Where do the recommendations come from?
From your project's own measured data: visibility gaps by intent, source and citation analysis, and perception claims extracted from real answers. They are regenerated after collection runs so they track the current state, not last quarter's.
Are these generic best practices?
No. Each recommendation is tied to a specific measurement on your project: a prompt set, a source, a claim. If the data doesn’t support an action, it isn’t in your queue.
Who executes the recommendations?
You can, or we can: the service layer ships content, schema and outreach fixes with you, and on Expansion and above, monthly strategy reviews walk the queue together. The recommendation always states what to do and why the data says so.
Works together with
See recommendations on your data
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