Tracking What DeepSeek Says About Your Brand

Start with the gap, because it shapes everything else. Other vendors publish crawler pages. OpenAI, for example, separates GPTBot (training) from OAI-SearchBot (ChatGPT search) and says each setting is independent. That gives site owners a documented control and a documented consequence.

As of October 2026, the evidence base this library is built on contains nothing equivalent for DeepSeek. It names no DeepSeek crawler user agent. It names no robots.txt token, no IP list, no search provider and no ranking disclosure. We won't invent one, and you should be wary of any guide that does without linking to a DeepSeek page.

What this means in practice:

• You can't write a robots.txt rule and know what it does for DeepSeek.
• You can't check a firewall allowlist against published DeepSeek IP ranges.
• You can observe answers. That's the only measurement available to you.

So tracking DeepSeek is a logging exercise, not a configuration exercise. Compare that with platforms that publish more, covered in our guides to monitoring ChatGPT brand answers and tracking Claude's answers.

Every AI answer comes from one of two places. Either the model answers from what it learned in training (parametric knowledge, frozen at a cutoff), or it runs a live search and reads pages first (retrieval). The two can say very different things about your brand.

The distinction matters because the fixes are different:

• Retrieval off. Nothing you publish this quarter reaches the answer. The model's view of you is fixed until the vendor trains a new version.
• Retrieval on. Your pages, and pages that mention you, can enter the answer, if the engine can fetch them.

Across the platforms our evidence base covers, retrieval fires per query and at the vendor's discretion. We have no DeepSeek documentation saying when it searches. Treat it the same way: unknown unless the answer shows you.

The practical rule is simple. If an answer lists sources or links, log it as "search visible". If it lists none, log it as "unknown", not "training data". An answer without citations may still have used retrieved text, and you can't verify either way.

A spreadsheet is enough. The discipline is in what you record and how consistently you record it.

Pick the prompts

Choose 10 to 20 questions a real buyer would ask. Mix three types: category questions ("best payroll software for small agencies"), comparison questions, and direct brand questions ("what does [your brand] cost"). Our page on tracking prompts that name competitors covers when comparison prompts earn a place. Keep the wording fixed between rounds, or you're measuring your own edits.

Control the session

Use a fresh, logged-out or new session where the app allows it. Turn off any memory or custom instructions. Note which model or mode the interface shows, and whether a search or web toggle was on.

Record every run

Log these columns for each answer:

1. Date and time.
2. Exact prompt text.
3. Model or mode shown in the interface.
4. Search toggle state, if visible.
5. Whether sources appeared, and which URLs.
6. Whether your brand was named, and in what position in the list.
7. Every specific fact stated about you (price, founding year, product names, location).
8. Whether each fact is right, wrong, outdated or unverifiable.

Column 8 is the one that pays off. A wrong price or a discontinued product named as current is something you can act on. A mention count on its own isn't.

One honest note: 99Visibility's paid plans audit ChatGPT, Google AI Overviews, Gemini, Perplexity and Claude. DeepSeek isn't on that list, so for DeepSeek this manual log is the method. If you want a combined view of the engines that can be automated, see free AI brand monitoring across several engines.

Ask the same question twice and you may get two different lists, in a different order, with different facts. This is normal behavior for generative models, not a sign your log is broken.

Three things follow:

• Run each prompt several times per round. Three runs is a reasonable floor for a manual log. Record all of them, not the best one.
• Report rates, not single answers. "Named in 2 of 3 runs" is a finding. "DeepSeek mentioned us on Tuesday" isn't.
• Don't call it a rank. List position shifts between runs. Treat position as a note on one answer, not a score you can trend.

Also keep platforms apart. One engine can cite you heavily while others barely do, and a blended number hides that. Log DeepSeek in its own tab, the same way you'd track Le Chat separately from ChatGPT.

Finally, keep the log comparable over time. If you change the prompt set, the number of runs or the session setup, start a new series. Mixing methods produces a trend line that measures your process changes.

Be plain about control. You can't change DeepSeek's training data this quarter, and no published technique reliably changes what an engine retrieves. A 2026 critical survey of 45 GEO studies (arXiv 2607.14035) found no technique with a stable, cross-platform causal effect on discoverability.

What is in your control is removing blocks, if and when DeepSeek fetches pages:

• Put key facts in the HTML. Vercel's crawler study found that ChatGPT and Claude crawlers fetch JavaScript files but don't execute them. DeepSeek wasn't in that tested set, but content that only appears after JavaScript runs is the most common way any non-rendering crawler misses it.
• State specific facts plainly. A page that says your price, your plans and a one-line definition of what you do gives an engine a sentence it can quote.
• Fix contradictions on your own site. If your pricing page, footer and about page disagree, an engine reading any of them can get it wrong.
• Watch your firewall. A CDN or bot-management rule can turn away crawlers you never meant to block. Without a documented DeepSeek user agent, you can only check this in your server logs.

What you shouldn't do: add markup, rewrite pages in an "AI-friendly" style, or publish an llms.txt file expecting DeepSeek to cite you. No evidence in our base supports any of those for DeepSeek or any other engine.

Most wrong answers trace to one of three sources. Logging tells you which.

• Your own site is wrong or inconsistent. Fix the page. This is the fastest win.
• A third-party page is wrong. If DeepSeek shows sources, open them. An outdated review or directory listing may be the origin. Contact that site.
• No source is shown. The error may come from training data. Correct the public record where you can and re-check after the vendor ships a new model.

One more case deserves attention. Research on LLM search agents (arXiv 2606.16821) shows that manipulated web content, such as hidden instructions, can change what an agent endorses once that content is in its results. If a source behind a wrong claim looks engineered, read our page on whether a competitor can manipulate AI answers. Never answer with hidden text of your own. It's illegitimate and puts your own site at risk.

Does DeepSeek have a crawler I can allow or block in robots.txt? As of October 2026, our evidence base contains no DeepSeek documentation naming a crawler user agent or robots.txt token. We can't tell you which line to write or what it would do. If you see an unfamiliar AI bot in your server logs, check the vendor's own documentation before writing rules. Don't copy user-agent names from unsourced lists, because a wrong token does nothing.

How often should I run my DeepSeek prompt log? Monthly works for most brands, with three runs per prompt each round. Run an extra round after you change pricing, launch a product or fix a wrong fact on your site, so you can see whether answers changed. Keep the prompt set and session setup identical between rounds. Otherwise you can't tell a real shift from a change in method.

Can I tell whether a DeepSeek answer used live search? Only partly. If the answer shows sources or links, it retrieved pages. If it shows none, you can't tell from outside whether it answered from training data or used retrieved text without citing it. Log those answers as unknown. Treating every uncited answer as training data leads to the wrong fix.

Is there a tool that alerts me when DeepSeek says something false? We can't point to a documented automated feed for DeepSeek answers in our evidence base, so a manual log with a right-or-wrong column per fact is the dependable method. For engines that can be monitored automatically, our page on [tools that alert you to false AI claims](/questions/tool-alerts-ai-false-claims) explains what to look for and what such alerts can and can't see.