Network-Security GEO in Israel: Studying the Sources Behind AI Vendor Shortlists

A firewall and hybrid-cloud comparison can involve several different purchasing decisions inside one question. Nir Levi’s Israeli GEO pilot examined this scenario in Hebrew and English using Perplexity, tracking Check Point as one selected brand. The purpose was to observe answer construction and exported sources. A useful finding in this category concerns how a vendor is positioned alongside alternatives and which requirements the answer treats as relevant to the buyer.

What the pilot recorded

On 11 October 2026, the pilot collected four answers for this question: two in English and two in Hebrew. Check Point appeared in 2 of 2 English answers and 2 of 2 Hebrew answers. Links to its official domain appeared in 2 of 2 English exports and 2 of 2 Hebrew exports. The mean overlap between the two languages’ exported source-host sets was 20.5%, calculated across the two rounds. All four observations used Perplexity Search with the GPT-6.1 Sol model label in fresh incognito conversations.

Source-host overlap is the number of shared hostnames divided by the number of distinct hostnames across the two answer exports. Perplexity’s Copy output is the measured record; grouped on-screen sources may contain additional links.

Read the rationale surrounding the brand name

The first English answer proposed Fortinet, Check Point and Palo Alto Networks as a core shortlist, with other vendors considered for additional scenarios. It placed the brands inside a discussion about architecture and operating requirements. For a security marketing team, the surrounding rationale is valuable evidence to review. Being included in a list does not tell the team whether its deployment model, intended customer or product boundary has been explained accurately.

Give architectural distinctions a clear home

An editorial review can separate the existing network environment, cloud footprint, administration requirements and support process. Give each topic a page or section that a buyer can inspect. Have product specialists verify the explanation and keep it aligned with current documentation. This approach produces useful material for procurement discussions as well as a concrete basis for checking AI answers. A useful acceptance criterion is whether the revised page answers the named architecture question with current, inspectable information.

Treat local context as a research variable

Both prompt languages explicitly placed the buyer in Israel. That controls the wording of the scenario while leaving the engine free to choose its sources. For future work, record whether the answer discusses local service, language needs or implementation partners, and verify any such statements separately. A source appearing in a Hebrew answer may still be written in English. Keeping the actual URLs prevents language assumptions from replacing the evidence.

Nir Levi’s perspective

First, check which sites are being cited and why. Then identify information missing from the company’s own site and publish useful original content that answers those questions, with real data and examples. The aim is to give AI answers a clearer, better-supported basis for describing the company.

Read the original research

The complete study, paired prompts, evidence downloads and Nir Levi’s commentary are available at https://nirlevi.com/en/research/israeli-geo-hebrew-english/

Put the finding to work

Nir Levi’s GEO work for Israeli technology teams can start with this kind of tightly scoped source review. The resulting brief should identify the buyer question, the relevant architecture explanation and the evidence needed to make a specific page more useful.

Explore GEO and AEO consulting in Israel with Nir Levi at nirlevi.com and bring a buyer question to your next review.