Open DeepSeek, switch on web search, and ask "which tools can check a brand's visibility in AI search?" If the answer lists half a dozen names and yours is not among them, you are in the majority. AiVisible's own daily self-scan on 5 September 2026 scored just 12 out of 100 on the DeepSeek platform (source: AiVisible daily self-check), while the same answers kept recommending established SEO suites such as Semrush and Ahrefs. For most small and mid-sized companies, "not ranking in DeepSeek" is simply where things start.
This tutorial breaks "how to rank in DeepSeek" into five steps you can run week by week: measure a baseline, unblock crawling, rewrite pages so the model wants to quote them, build third-party sources, and set up a re-testing cadence. Each step comes with a checklist you can follow as-is, along with an honest note on what you can and cannot expect.
Step 1: Establish a baseline — learn what DeepSeek says about you today
Before any optimization, run a fixed set of questions through DeepSeek and record the answers; that baseline is the control group for everything that follows, and without it you cannot tell whether your work changed anything.
The key property of a baseline is repeatability. Split your question bank into three groups and reuse the exact same bank on every re-test:
- Brand queries — "What is AiVisible?", "Is brand X reliable?" — check whether DeepSeek's understanding of your brand entity is accurate.
- Category queries — "Recommend some GEO tools", "How do I check brand visibility in AI search?" — check whether you make the shortlist at all. This is what "ranking" really means in a generative engine.
- Comparison queries — "A vs B for an export manufacturer?" — check how the model describes you when you share the frame with competitors.
Run each question in both modes: with web search off (what the model remembers from training) and with web search on (whether live retrieval can bring you back). The gap between the two is itself a diagnosis. If you only appear with web search on, the model has no memory of your brand yet, and Steps 2 and 4 matter most. If you appear in neither, the problem sits mainly in Step 3, the content layer.
Record more than "mentioned or not": note the position in the list, whether the description is accurate, and, in web-search mode, which source links were cited. Those citations tell you exactly whom DeepSeek trusts on that question. Doing this by hand for dozens of prompts is tedious; the free AiVisible scan runs the bank in one pass and stores the cited sources as your baseline snapshot.
Step 2: Unblock crawling — make sure DeepSeek's retrieval can read your pages
DeepSeek's web-search mode depends on live retrieval and fetching of web pages, so any block in robots.txt, rendering, or response status makes every later content improvement invisible.
This step is the most frequently skipped and the most frequently wrong. Over the past two years many sites used a hosted "block all AI bots" toggle to keep content out of training sets, and in doing so also shut out the retrieval crawlers that power AI search. Work through this checklist:
- robots.txt — review every User-agent rule against RFC 9309 and confirm you are not serving
Disallow: /to AI retrieval crawlers. If you rely on a CDN's managed AI-bot blocking, turn it off in the dashboard; editing the file alone is not enough. - Readable HTML — the main content must be present in server-rendered HTML, not injected by client-side JavaScript after load. Fetching the page with
curland searching the source for your body text is the fastest check. - Canonicals and status codes — every page needs one canonical URL, the sitemap should list final URLs only (no entries that 301/308 elsewhere), and unknown paths must return a real 404 rather than 200. These details decide whether a retrieval system treats your site as one trustworthy entity.
- Structured data — add Schema.org Organization and
WebSitemarkup to the home and product pages, andArticleplusFAQPageto posts. Structured data is not DeepSeek-specific, but it is the lowest-cost cross-engine entity signal you can ship.
On the question of what DeepSeek's crawler is called: unlike OpenAI's published GPTBot documentation, DeepSeek has not released an official crawler specification at the time of writing. The safe policy is therefore allow-by-default — block only the specific bots you explicitly do not want, rather than maintaining an allow-list of names. For the broader logic of how engines pick citations, see how AI search chooses its sources.
Step 3: Rewrite pages — produce paragraphs the model wants to quote verbatim
Generative engines favour passages that are clearly structured, precisely defined, and backed by verifiable sources; reshaping pages into "one conclusion sentence, one explanation, one table" is the core lever for getting cited.
Researchers from Princeton and partner institutions tested this systematically in "GEO: Generative Engine Optimization" (KDD 2024). They applied different rewriting strategies to the same set of web pages and measured how visibility inside generative-engine answers changed. The paper reports that adding citations, statistics, and authoritative quotations improved visibility by up to roughly 40% on its benchmark, while classic keyword stuffing produced little to no gain. The direction transfers to DeepSeek: make content more credible and more extractable, not more "SEO-shaped".
In practice, rewrite along these lines:
| Page element | Before (typical) | After (quotable) |
|---|---|---|
| Brand definition | "We are committed to delivering leading solutions" | "AiVisible is an AI search visibility monitoring and GEO optimization platform covering DeepSeek, ERNIE Bot, Qwen, ChatGPT and Perplexity." |
| Feature description | Adjective-heavy paragraphs | A two-column "does / does not" list |
| Comparisons | Scattered prose | An HTML table, one comparable dimension per row |
| Data points | "Studies show great results" | "According to [report, year, link]…" — delete any number without a source |
| FAQ | None | 3–5 questions, each answer 2–4 sentences, extractable on its own |
Two further notes. First, entity consistency: brand name, positioning line, and domain must be character-for-character identical across your site and every external platform; a model will happily treat "AiVisible", "AI Visible" and "aivisible.top" as three different entities. Second, language priority: DeepSeek's user base is heavily Chinese-speaking, and the DeepSeek-R1 technical report documents extensive Chinese-language training and evaluation, so Chinese pages usually carry more weight with DeepSeek than English ones. If your buyers are overseas, keep the English version equally strong and link the two with hreflang. For detailed writing guidance, see how to write AI-friendly content.
Step 4: Build third-party sources — let DeepSeek hear about you from others
In web-search mode DeepSeek synthesizes several retrieved results per answer, so a single first-party site rarely makes the cut; consistent brand descriptions across industry media, Q&A communities, and reference sites are what qualify you as a "consensus candidate".
The logic is plain: if only you say you are good, the model has no reason to believe it. It cross-checks across sources. Build them in this order of priority:
- Industry media and vertical blogs — get included in round-ups such as "GEO tools compared" or "AI search optimization guide". Round-ups are the source type most often cited for category queries because they already are shortlists.
- Q&A and community platforms — genuine usage discussions on Zhihu, Reddit, Hacker News, or developer forums. Genuine is the operative word; models detect marketing tone better than most people assume, and fabricated reviews breach both platform rules and advertising law.
- Developer and documentation sites — if the product has an API or open-source component, a GitHub README, a docs site, and developer-community posts are high-trust sources.
- Encyclopedic entries — only where notability criteria are met. A deleted entry is worse than none.
Every third-party description should reuse the standard definition sentence from Step 3. The citation log from Step 1 pays off here: the sites DeepSeek repeatedly cites are the ones where you should try to appear first. Sources that mention your competitors but not you are your most concrete gap list.
Step 5: Set a re-testing cadence — turn optimization into a measurable loop
AI search answers shift with model versions, retrieved results, and prompt wording, so a single test proves nothing; only a fixed question bank, fixed schedule, and fixed record format can turn "ranking improvement" from a feeling into data.
A workable cadence is a weekly re-run of the baseline bank plus a monthly expansion of the bank. On each re-run, watch four changes:
- Mention rate — the share of category queries in which you appear.
- Mention position — whether you move up the shortlist; this is "ranking" in the narrow sense.
- Description accuracy — errors in how the model states your positioning, features, or pricing; fix these at the source page (Step 3).
- Citation changes — which third-party links citing you appeared or disappeared.
To be clear about limits: this method raises the probability of being retrieved, trusted, and described accurately. It cannot promise a specific position or timeline. DeepSeek publishes no ranking rules, and any vendor guaranteeing results deserves suspicion. What you can reasonably expect: crawl fixes from Step 2 tend to show up fastest in retrieval; content and source building in Steps 3 and 4 compound over weeks to months; Step 5 is the insurance that keeps the first four from going to waste.
The 5-step checklist on one page
Compressed into a single table you can pin to a team board and review weekly, these five steps are the lowest-cost operating routine for brand visibility in DeepSeek.
| Step | Core actions | Deliverable | Cadence |
|---|---|---|---|
| 1 Baseline | Fixed bank, both modes, log citations | Baseline snapshot | Once, then per Step 5 |
| 2 Crawl access | robots.txt, SSR, canonical, 404, Schema | Crawl checks passing | Once + after every redesign |
| 3 Page rewrite | Definition sentence, tables, sourced numbers, FAQ | Quotable pages | Ongoing |
| 4 Third-party sources | Media, communities, docs, reference sites | Consistent external descriptions | Monthly push |
| 5 Re-test | Mention rate, position, accuracy, citations | Trend log | Weekly |
If you want to skip the manual work in Steps 1 and 2, the free AiVisible scan returns a baseline and a site health report in one pass; then use the full AI visibility audit process to decide where to begin. For platform-specific details, see the DeepSeek optimization guide.
FAQ
Why does DeepSeek know nothing about my brand when web search is off?
With web search off, DeepSeek answers only from training data. If your brand lacked enough public, consistent descriptions before the model's training cutoff, it has no memory of you. In the short term you can only be found through web-search mode; over the long term, sustained third-party coverage is what later model versions may absorb.
How long does it take to rank higher in DeepSeek?
There is no timeline anyone can promise. Crawl-level fixes such as robots.txt, rendering, and status codes tend to be reflected fastest in web-search results. Page rewrites and third-party source building compound over weeks to months. Updates to the model's own memory depend on DeepSeek's release schedule, which no site owner controls.
Is Chinese-language content enough for DeepSeek?
If your customers are mainly in China, Chinese pages usually have the larger effect on DeepSeek. If you sell to overseas buyers, the English version matters just as much. Link both with hreflang and reuse one identical brand definition sentence so the model does not treat the two language sites as separate entities.
Can I post my own recommendations on forums to boost ranking?
Not advisable. Generative engines detect marketing tone well, and reviews disguised as user posts can be removed by the platform and may breach advertising law on misleading claims. The durable approach is to ship a product and documentation good enough that real users and industry writers cover you unprompted.
Which manual steps does AiVisible replace in this process?
Mainly Steps 1 and 5: running a fixed question bank across platforms, logging mention position and cited sources, and producing weekly trend comparisons, along with the site health check from Step 2. The page rewriting in Step 3 and the source building in Step 4 still require human work.