What is AEO / GEO and why does it matter in Lebanon?
AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) are common names for work that improves and measures how a business appears in AI-powered search and answers such as ChatGPT, Perplexity, Gemini, and Google AI features. For Google, this work is still grounded in normal SEO: useful content, index eligibility, clear technical structure, and trustworthy evidence. The additional work is measuring mentions and citations across several surfaces instead of claiming one universal rank.
How is tar/'s AEO service different from other agencies selling it?
Measurement and evidence. Every engagement starts with a dated, fixed set of buyer questions, separates brand mentions from owned and third-party citations, checks live crawl and index eligibility, and uses engine-owned reports where the client has access. You see the baseline, the evidence behind each finding, the fixes shipped, and the re-test. Tarek uses this methodology on his own domain and publishes the approach.
How much does an AI-visibility (AEO/GEO) engagement cost?
The scored AI-visibility audit is $1,500 and takes 48 hours: baseline citation share, the specific gaps (entity, schema, content, authority), and a prioritized fix list. Implementation plus monthly re-measurement runs as an $800/month optimization retainer. Both are quoted in USD and published — no discovery-call pricing games.
Is AEO different from normal SEO?
For Google Search, Google explicitly says that optimizing for generative AI features is still SEO, with no special AI schema or required llms.txt file. A broader AI-visibility audit also measures other surfaces with their own crawlers and answer behavior, such as ChatGPT, Perplexity, Claude, and Copilot. tar/ covers the shared search foundation and reports the platform-specific observations separately.
Can you guarantee my business appears in ChatGPT's answers?
No. Assistants change and answers vary by surface, mode, country, date, and phrasing. tar/ provides a reproducible baseline, an evidence-backed fix list, implemented changes within scope, and re-tests that show what was observed afterward without promising a placement or claiming that one change caused it.