DeepSeek Visibility Checklist: General Steps, Labelled Honestly

Start with the gap. For OpenAI, Anthropic, Perplexity, Meta, Google and Microsoft, our evidence base holds first-party pages that name each crawler and say what blocking it does. For DeepSeek it holds nothing comparable as of October 2026.

That means we cannot tell you:

• which user agent, if any, DeepSeek uses to crawl or fetch pages
• whether it honours a robots.txt token, and which one
• whether its consumer app searches the web by default, on a toggle, or not at all
• what its model knowledge cutoff is

Some third-party pages list a DeepSeek user agent. We have not verified one against a vendor page, so we do not repeat it. If you block or allow a string you found elsewhere, treat it as unconfirmed and watch your server logs to see what actually arrives.

The practical consequence: every step below is a general step. It is the same work you would do for any engine, labelled as such. Compare the vendor-documented controls in the Meta AI visibility checklist, where Meta states that allowing Meta-WebIndexer helps it cite your content.

Every AI answer passes through up to four stages. Separating them tells you where effort pays off.

For DeepSeek, the parametric and retrieval stages are undocumented in our evidence base. Access is the one stage you can check mechanically. A critical survey of 45 GEO studies (arXiv:2607.14035, July 2026) found that no reviewed technique shows a stable, cross-platform causal effect on discoverability. So the checklist leans on access and accuracy, not content tricks.

This is not DeepSeek-specific. It is the strongest evidence in the set for any AI crawler that does not render pages.

Vercel found that ChatGPT and Claude crawlers fetched JavaScript files (11.50% and 23.84% of requests) but did not execute them, so they could not read client-side rendered content. Independent re-testing as of June 2026 confirmed the same for several other AI crawlers. We do not know whether DeepSeek renders JavaScript. Assume it may not.

What to do:

1. Fetch your homepage, pricing page and main product page with JavaScript disabled.
2. Compare that text with what a browser shows.
3. List every price, product name and claim that appears only after the page loads.
4. Move those into server-rendered HTML.

Content behind tabs, accordions or "load more" buttons that fetch on click has the same problem.

Also not DeepSeek-specific. robots.txt rules are per bot, and each vendor's settings are independent. OpenAI's crawler page states that GPTBot governs training while OAI-SearchBot controls ChatGPT search. A blanket AI block can remove search eligibility you wanted to keep.

A per-bot rule looks like this:

User-agent: OAI-SearchBot
Allow: /

User-agent: GPTBot
Disallow: /

Without a verified DeepSeek token, you cannot write a DeepSeek-specific rule with confidence. Check what your User-agent: * group says, because that is what an undocumented crawler is likely to read.

Then check the second gate. A firewall or CDN can return a 403 to a crawler that robots.txt allows, and nothing in your analytics will show it. Request a page with each crawler's user agent and record the status code. The AI Crawler Checker does this for named AI crawlers. Repeat the check after any redesign, using the site migration AI crawler checklist.

Stating a specific fact (a price, a number, a one-line definition) on a fetchable page gives an engine a sentence it can quote. It is the only positive experimental signal for on-page content in our evidence base.

Keep those facts consistent. If your pricing page, about page and footer disagree, an engine has conflicting sources to choose from. Fix the contradictions on your own site first.

Then ask DeepSeek directly. Say you sell accounting software for freelancers. Ask the questions a buyer would ask, note whether you are named, and record any wrong price, feature or founding detail. Remember that you usually cannot tell whether an answer came from live search or from the model's frozen knowledge. An answer with no visible citations may simply be stale.

When engines cite other sites instead of yours, being named on those sites is the route into the answer. That is an association in the evidence, not a promised effect. The product launch checklist for AI accuracy covers keeping new facts consistent across those pages.

Several tactics are sold as AI optimization. The evidence does not support them as levers:

• Schema for citations. Ahrefs' matched test found AI Overviews citations fell 4.6% after pages added schema. Existing markup does no harm, but it is not a lever for AI answers.
• llms.txt. Google says Search ignores it, and no engine in our evidence base documents reading it.
• AI-friendly rewriting or chunking. Google's AI optimization guide says you don't need to write in a specific way for generative AI search.
• E-E-A-T or domain authority scores. Neither is a number any engine computes.

Avoid hidden text or instructions aimed at AI crawlers. Research on prompt injection shows manipulated pages can sway some search agents. It is illegitimate, and it puts your brand at reputational and policy risk.

Run it in this order: rendering, access, facts, answers. Access problems make everything downstream meaningless, so fix those first. The free GEO audit guide follows the same access-then-accuracy order.

Repeat it quarterly, and after any CDN change, redesign or pricing update. The quarterly AI visibility review gives a cadence. When DeepSeek publishes crawler documentation, add its named agents to your robots.txt review and your firewall test.

What stays outside your control: whether DeepSeek retrieves your page for a given question, and what its model says when it does not search. No checklist changes that. It only removes the blocks on your side.