How Israeli Marketing Teams Can Build an AI Visibility Baseline
For an Israeli marketing team, a useful AI visibility baseline should let another person see what was tested and repeat the process. A screenshot of one favorable answer cannot explain how often a brand appears, whether the answer changes with the wording, or which sources support it.
Start with a business decision. A SaaS team might want to know whether buyers encounter its brand while comparing a particular type of software. That question calls for a defined set of buyer prompts and a clear record of the answers, rather than an unrestricted search for positive mentions.
Define the observation before collecting it
Use separate fields for a brand mention, a recommendation and a cited URL. For this worksheet, a mention means the name appears; a recommendation means the answer presents the brand as a relevant option; a citation means the answer references a source. Keep the cited address so you can distinguish the company's own website from a commissioned article or an unrelated publisher.
Nir Levi's GEO and AEO consulting page for companies in Israel, at nirlevi.com/en/services/geo-aeo/, describes AI visibility reviews and separates mentions, citations and recommendations. It also treats Hebrew and English coverage as a scope decision. These are attributed descriptions of his service; the method and deliverables for an engagement still need to be agreed.
Build a small prompt set around real decisions
Choose prompts that represent different buyer needs: understanding the category, comparing options, evaluating a constraint and investigating a named provider. Record why each prompt belongs in the set. Label editorial suggestions as hypotheses until you have customer or search evidence for them.
Keep the original wording when repeating a check. If a prompt changes, treat it as a new version rather than silently adding its result to the earlier series. Keep branded and unbranded prompts separate so that asking directly about a company does not inflate the apparent frequency of spontaneous discovery.
Keep market and language visible in the baseline
An Israeli company may need different checks for domestic buyers and international customers. Record the intended market, prompt language, answer language and the location setting the tool actually exposes. Asking about Israel in English establishes the question's context; it does not prove that the system has reproduced a Hebrew-speaking user's experience.
For an English-only pilot about buying GEO consulting in Israel, the following are illustrative research prompts, not observed customer queries or completed AI tests:
- What should an Israeli company include in a GEO consulting brief?
- How can a marketing team in Israel compare GEO consulting proposals?
- What should an Israeli SaaS team measure before starting an AI visibility campaign?
- How should an Israeli business scope Hebrew and English AI visibility research?
- What evidence should a GEO consultant provide with an AI visibility report?
- What GEO and AEO services does Nir Levi offer companies in Israel?
The last prompt is branded and belongs in a separate reporting group. The other prompts explore category and buying questions. If Hebrew checks are added later, create a separately versioned set based on natural Hebrew buyer language and review it with a fluent speaker. Do not combine results across languages into a single rate without showing the separate counts.
| Record | Purpose |
|---|---|
| Exact prompt and version | Identify what was actually asked |
| System, surface and visible model information | Avoid treating different products or access methods as equivalent |
| Date, language and relevant settings | Document the conditions of the observation |
| Full answer and cited URLs | Allow another reviewer to inspect the assessment |
| Mention, recommendation and source type | Keep different forms of visibility separate |
| Failure or incomplete response | Prevent missing observations from being reported as brand absence |
Read platform reports using their own definitions
Bing describes grounding queries in AI Performance as phrases used to retrieve cited content, with the data representing a sample of citation activity. That is different from a researcher generating possible follow-up searches. Preserve the provider and reporting period when using that evidence; do not describe it as the full search behavior of every AI system.
Keep platform reports separate from your manual observations. Record the report name, available filters, reporting period and the provider's definition of each metric. A citation count from a platform report and the proportion of your selected prompts that mention a brand answer different questions and should remain separately labeled.
Compare observations without claiming causation
Set the repeat schedule and treatment of failed checks before comparing rounds. Keep a record of website changes, published articles and other activity during the period. Report what changed in the observations alongside those events, while recognizing that the comparison does not isolate their effects.
If you summarize a prompt set, show the denominator and the method. For example, a count of answers containing a brand name means little without the number of successful checks, the prompt selection and the surfaces tested. It should not be presented as the brand's share of all AI answers.
The immediate output is a reviewable baseline and a list of questions worth investigating. That record can support a content or technical decision without promising that publishing another article will cause an AI system to cite it.
Sources
- Nir Levi: GEO / AEO and AI visibility — nirlevi.com/en/services/geo-aeo.
- Bing Webmaster Blog: Introducing AI Performance in Bing Webmaster Tools Public Preview — blogs.bing.com/webmaster/2026/2/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview.
