SEO for ChatGPT Search: what WebRankInfo advises

WebRankInfo has published advice on improving a site's visibility inside ChatGPT Search, the search engine operated by OpenAI. The French publication frames the channel as one that could gain ground over the coming months and years, without specifying traffic volumes, user numbers or how the engine selects its sources.

That framing matters for teams used to thinking in terms of Google, Bing and the social platforms. A discovery surface owned by an AI provider sits between classic organic search and generative answers, and the advice published assumes it is worth preparing for rather than waiting for scale.

What the report leaves open

The announcement does not detail specific ranking factors, technical requirements, or the mechanics by which pages are surfaced. It also does not give a timeline beyond the general expectation of growth, nor a measurement method for estimating the traffic such a channel sends. Any team planning budget around it is therefore working from a hypothesis, not a forecast.

That is not a reason to ignore it. It is a reason to treat it as an experiment with defined success criteria rather than a line item.

What an acquisition team can check this week

Most of the groundwork overlaps with what already serves conventional search and answer engines. The useful exercise is an audit, not a rebuild.

• Confirm that key pages render correctly without client-side JavaScript, since crawlers vary in what they execute.
• Check whether page titles and headings state plainly what the page answers, rather than relying on implied context.
• Review structured data coverage on the pages that carry commercial intent.
• Verify that pricing, product specifications and service descriptions are present as text on the page, not locked inside images or gated forms.
• Identify which pages already attract impressions for question-style queries in existing search console data.

None of these steps is specific to ChatGPT Search. They are the baseline any retrieval-based system needs in order to cite a source accurately, and they cost little to verify.

The measurement question

Referral traffic from AI assistants is notoriously hard to attribute. Sessions may arrive without a referrer, and users often act on an answer without clicking through. The report does not propose a measurement framework, so teams should define their own before testing: branded search lift, direct traffic patterns on pages likely to be cited, and assisted conversions in analytics are the practical proxies.

It is also worth deciding internally what success would look like in a quarter. A handful of qualified inquiries traced to an AI surface is a different signal from a flat line, and without a prior definition neither result teaches much.

A channel to watch, not to bet on

OpenAI's search product competes for attention with established engines and with the assistant behaviour of simply answering without a source. Visibility there depends on being quotable: clear statements, verifiable facts, stable page structure. That is a discipline worth applying regardless of which engine ends up winning, and it is the most defensible reading of the guidance published by WebRankInfo.