The complaint “we are not in the neural networks” is not a single diagnosis but a symptom of at least six different failures, each fixed by contradictory actions. A model may simply never have learned the brand. A search crawler may be receiving an immediate refusal. A single line in a page template may be blocking the text from being used in a generative answer. A page may be indexed yet consistently lose to competitors during source selection. A system may mention the company without a link. Traffic may arrive yet disappear into “direct” visits.
Verification is required to turn the complaint into concrete diagnoses. The recommended workflow measures what needs to be measured, in what order, with which tool, and how to avoid mistaking noise for signal. A complete first pass takes one working day and requires no paid services.
Three separate layers produce the same-looking answer
Model knowledge is tested by asking questions with search disabled. The fact that ChatGPT knows a company does not prove the site itself was in the training set; the information may have arrived from third-party publications.
Search layer crawlers such as OAI-SearchBot, Claude-SearchBot and PerplexityBot fetch pages in advance and can cite them with links. Their activity is visible only in server logs and in “search-enabled” mode.
Agent layer bots such as ChatGPT-User, Claude-User and Perplexity-User open specific URLs on user request. Only server logs can confirm their visits; robots.txt rules may not apply.
Mixing the layers in one table is pointless. Blocking a search bot will not fix stale model knowledge, and an analytics report will not update outdated training data.
Step-by-step verification
Step 1. Disable search, open a fresh dialogue without memory, and ask about the company, products, people, prices and specifications. Record concrete factual errors and the competitors named instead of you.
Step 2. Examine 30 days of server logs. Separate requests by purpose (training, indexing, user-directed agents, maintenance). Persistent 403 responses for named AI crawlers indicate access problems; repeated 429 responses indicate rate limiting.
Step 3. Audit four locations that commonly suppress AI usage: page-level meta tags (nosnippet, data-nosnippet, max-snippet:0), robots.txt entries for each distinct agent name, Cloudflare AI-bot toggles (Search / Agent / Training), and the new generative-functions switch in Google Search Console.
Step 4. Disable JavaScript and confirm that title, main text, date, author and internal links remain visible. If core content loads only after rendering, some AI systems will see an empty page.
Step 5. Run at least 20 user-style questions, each three times, separately with and without search. Track six metrics independently: mention rate, linked citations, source ranking, factual accuracy, freshness and competitive share.
Step 6. Pull the new “Visibility in Alice AI” report from Yandex Webmaster and the generative-functions report from Google Search Console.
Step 7. Verify that utm_source=chatgpt.com tags and the new AI Assistant channel in Google Analytics are correctly attributed; roughly 70 % of AI referrals currently arrive as direct traffic.
Step 8. Review content and auto-generated files for hidden instructions aimed at agents. Malicious prompt-style directives have been observed in HTTP headers, comments, structured data and metadata.
After completing the checklist, the generic complaint “we are invisible to AI” is replaced by specific, actionable diagnoses with different timelines and costs.
