How to Improve Your AI Visibility: The 90-Day AEO Playbook
Summary
Track My Visibility ran a webinar with Ajay and SEO practitioner Amit Panchal walking through Answer Engine Optimization: how AI assistants pick which brands and pages to cite, what content shape earns those citations, and why brand mentions across the web now function the way backlinks once did. The session ends with a staged 90-day plan for teams starting from scratch.
Roughly half the runtime is instructional and the rest is product: two live demos show the vendor's own tool surfacing sources, citations, quick wins, and technical fixes, and the recap carries an offer. The playbook and the platform arrive together, so the framework is presented through the lens of the software that measures it.
Demonstration by Track My Visibility, embedded from YouTube. AIOTruth did not reproduce the test.
Why it matters
The operating assumption here is that a brand can be recommended by an assistant without ranking for the query, which changes where effort goes: bottom-of-funnel pages that answer a specific question in citable form, content refreshed often enough to look current, and mentions on third-party sites that models encounter while forming an answer. If that holds, a team tracking only search positions has no reading on whether ChatGPT, Gemini, Perplexity, or Google AI Overviews name them at all, and no way to tell an improvement from a model update.
Source
- trackmyvisibility.com/ the item itself, published by Track My Visibility, created by Track My Visibility
- trackmyvisibility.com/docs/ Reproducible first-party demonstration, first party
- trackmyvisibility.com/changelog/ Reproducible first-party demonstration, first party
- trackmyvisibility.com/contact-us/ Named expert analysis, or a first-party claim about itself, first party
- youtube.com/watch?v=B7QsRP43IkUwhere AIOTruth found it
What was checked
- canonical URL taken from the page itself
- read the item page (200)
- https://trackmyvisibility.com/docs/: supports the item, tier 2 (Reproducible first-party demonstration)
- https://trackmyvisibility.com/changelog/: supports the item, tier 2 (Reproducible first-party demonstration)
- https://trackmyvisibility.com/: supports the item, tier 8 (Named expert analysis, or a first-party claim about itself)
- https://trackmyvisibility.com/contact-us/: supports the item, tier 8 (Named expert analysis, or a first-party claim about itself)
- https://www.instagram.com/trackmyvisibility: does not mention the item, tier 9 (Reputable reporting)
- https://www.tiktok.com/@trackmyvisibility: does not mention the item, tier 9 (Reputable reporting)
Limitations
- Everything checked traces back to a single domain, so this is one party's account.
- The demonstration was not independently reproduced by AIOTruth.
AIOTruth judgment
Evidence status is Supported. The webinar exists, the timestamps map to a real structure, and the vendor's own documentation and changelog corroborate that the demonstrated features ship in the product, which is what carries evidence to 8 and relevance to 10. Every source traces back to one domain, and AIOTruth did not reproduce the demonstration, so this remains one party's account of its own method. Usefulness sits at 6 because the tactics are described rather than specified: the 90-day plan is a session segment, not a published document a reader can work from. Originality at 7 reflects framing that is coherent and clearly argued while restating positions already circulating in this space.
How this score was calculated
| Dimension | Weight | Score | What produced it |
|---|---|---|---|
| AIO relevance | 30% | 10 | ai visibility, aeo, ai search, answer engine, answer engine optimization; 5 scope question(s) matched |
| Usefulness | 30% | 6 | 1 artifact(s), 4 actionable marker(s), 0 measured figure(s) |
| Evidence | 25% | 8 | Reproducible first-party demonstration; 4 verified source(s) across 1 domain(s) |
| Originality | 15% | 7 | no original testing found; closest archive match 0.286 |
aioRelevance x 0.30 + usefulness x 0.30 + evidence x 0.25 + originality x 0.15. The rubric is published in full on the Editorial Method page. Scoring is deterministic: the same item scores the same on every run.
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