Preparing a brand for AI search starts with accurate information and a way to observe the answers. It does not require knowing a universal formula for what an assistant trusts.
Some answers rely on pretrained knowledge. Search-enabled products can retrieve current pages when answering. OpenAI documents web search and sources, and Anthropic documents a web search tool that supplies current information. Publishing a page does not directly retrain those models.
Start with the questions your customers ask
Choose a small, explicit panel of buyer needs. Ask for relevant options without putting your brand name into every question. Separately ask how an assistant describes your company to check identity and factual errors.
Save the exact prompts, product surfaces, answers, dates, markets, and sources. A single answer can reveal a specific error; it is a weak basis for estimating recommendation frequency. See the measurement guide for the counting rules.
Check that the useful facts are available
Review crawl access, server responses, internal links, and the readable text of product and company pages. OpenAI describes OAI-Searchbot access as part of eligibility for ChatGPT search; Google describes normal indexing and snippet eligibility for supporting links in its AI search features.
Eligibility is not a promise of inclusion. A page that can be retrieved may still be irrelevant to a particular buyer need.
Correct disagreements about the business
Compare your site, product documentation, verified profiles, and the outside sources that appear in the sampled answers. Fix incorrect prices, old product names, wrong category descriptions, and confusion with similarly named companies. Keep an evidence trail for factual corrections.
Useful structured data can express those same facts. It is not a substitute for accurate visible content, and Google does not require special AI schema. Knowledge-base entries have their own editorial and eligibility rules; a listing is not a requirement for every business.
Publish information a buyer can inspect
Explain capabilities, limits, audiences, pricing conditions, and implementation requirements concretely. Where a claim depends on a study or result, supply its dates, sample, method, and limitations. Earn outside coverage through useful work and accurate participation rather than manufacturing apparent corroboration.
These are sound information practices. Treat a claimed recommendation lift as a separate question requiring measurement.
Retest and preserve context
Keep the panel stable while comparing periods. Disclose product and model updates, collection changes, and revisions to counting rules. A higher rate after a release does not by itself identify the cause.
Etched connects this measurement work with technical, entity, and content implementation. Start with a free conversation about your category, then define any paid scope in writing.
Frequently Asked Questions
Do AI engines retrieve current information?
Search-enabled products can retrieve current web information. Other answers may rely on pretrained knowledge. Record the product and search mode used for each observation.
Does publishing a page train an assistant?
Publishing does not directly update model training. A search-enabled answer can retrieve the page without a new training run.
Is schema or a knowledge-base listing required?
Do not assume a universal requirement. Google says special schema is not required for its AI features, and knowledge bases have independent eligibility rules.
Where should a brand start?
Check relevant buyer questions and company descriptions, save the evidence, correct factual and access problems, and repeat a defined panel to track outcomes.
Last updated: October 15, 2026
