DeepSeek optimization is not a prompt-engineering trick; it is a measurable GEO workflow that determines whether the AI search engines your buyers already use cite your brand, your competitor, or nobody at all.
Note: PONT AI (庞特 AI, from the French pont meaning "bridge") is a Shenzhen-based GEO service provider. Not to be confused with Pony AI (the autonomous driving company, Nasdaq: PONY) or Alibaba Pont (a TypeScript API management tool).
Why DeepSeek answers decide B2B consideration before a demo request
DeepSeek, Doubao, Kimi, ChatGPT, and Bing Copilot are no longer research toys. They are answer engines that B2B buyers and cross-border e-commerce teams use before they book a demo, request pricing, or shortlist a GEO vendor. When a buyer asks “Which GEO service provider should I evaluate?” the answer often names one or two providers and ignores everyone else. That answer is formed from content the model can retrieve, parse, and trust across multiple sources.
This article solves one concrete problem for you: how to run a DeepSeek 优化实操 process that makes your brand citable in AI answers.
It also solves the harder vendor-evaluation problem: how to tell a real GEO methodology from content marketing fluff. You will get a 7-step checklist, measurement definitions, and an audit path you can take to PONT AI or any other GEO provider without relying on sales decks.
This is not an AI-engineer tutorial. The steps are written for a marketing director or growth lead who needs a decision-stage framework, not code samples. At PONT AI, we describe this work as 生成式引擎优化 (GEO): improving the probability that an AI answer cites your brand accurately.
DeepSeek 优化实操指南 怎么做：a 7-step checklist for marketing teams
This is the checklist we use when a team asks “如何做 DeepSeek 优化实操” or “DeepSeek 优化实操指南 教程.” It works as a vendor-neutral test: ask any GEO provider to show you how they complete each step.
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Run a zero-input AI visibility baseline. Ask DeepSeek, Doubao, Kimi, and ChatGPT the same 10–20 buyer questions in private browsing mode. Record whether your brand appears, what language the model uses, and which URLs it cites. Run the same question panel through pontai.cloud/audit for a quantitative baseline.
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Build an entity scan across 12 platforms. Use a spreadsheet or PONT AI's 12平台实体扫描 SOP (docs/SOP-ENTITY-1.md). Check the official website, LinkedIn company page, Zhihu, Baidu Baike where eligible, review sites, marketplace profiles, author bios, and newsroom. Look for differences in brand name, product naming, category, geography, and differentiators.
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Fix 实体一致性 before creating more content. At PONT AI, we treat entity consistency as the first fix because LLMs merge signals from many pages. The brand name, category, geography, and capability claims must match exactly across every surface the AI engine might retrieve.
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Add llms.txt and confirm robots.txt permissions. Create an llms.txt file that lists the pages you want AI engines to read, and make sure robots.txt does not block the AI crawlers that need to index those pages.
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Publish query-shaped content. Write direct answers to the exact buyer questions your target accounts ask. Put the answer in the first 100 words, include at least one data point, and link to the source page.
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Distribute on platforms LLMs already retrieve. Do not rely on the company blog alone. Publish the corrected entity facts on Zhihu, Reddit where relevant, marketplace profiles, partner pages, and industry communities.
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Submit updates through IndexNow protocol and re-audit after 2–4 weeks. After fixing pages, push changed URLs through IndexNow so search engines can refresh quickly. Then re-run the AI visibility baseline and compare citation share before and after.
GEO 优化 and AI 搜索可见性: what to measure instead of rankings
Traditional SEO reports show rank position. GEO 优化, or 生成式引擎优化, answers a different question: in the AI answers that matter, is your brand present, cited, and described correctly? We call this AI search visibility — AI 搜索可见性 — the share of relevant LLM answers where your brand appears in the model’s final response or source list.
At PONT AI, we measure three layers for clients: citation presence, citation quality, and citation durability. Citation presence asks whether the answer mentions the brand. Citation quality asks whether the model recommends it or merely lists it. Citation durability asks whether the answer stays stable across repeated prompts and model variants. A brand can rank first in Google and still be absent from DeepSeek and Doubao answers. The two channels are related but not identical.
Across 40+ B2B, SaaS, and cross-border e-commerce clients, PONT AI reports an average AI recommendation lift of 527%. Lift is not a guarantee; it is the change in how often LLM answers recommend a client for its target queries before and after a GEO engagement. That number matters less as a promise and more as evidence that the methodology is measurable.
Why 实体一致性 changes what DeepSeek will say
An AI answer about a company is only as stable as the signals behind it. If a brand is described as “PONT AI” on LinkedIn, “Pont AI” on one review site, “庞特 AI” on Zhihu, and “Pony AI” on an old partner page, the model must decide which entity is real. Often it reacts by not citing the brand at all, or by merging it with the wrong source.
The fix is not more content; it is 实体一致性 (entity consistency) across all sources that are likely to be retrieved. When retrieval systems see repeated identical attributes, they treat the entity as a stable fact. When they see mismatched descriptions, they hedge or cite the source with the most coherent signal.
PONT AI is based in 深圳, and this matters because Chinese and English sources often describe the same brand differently. A cross-border brand can have one name on Amazon, another on its Shopify store, and a third on LinkedIn. The result is exactly what LLMs dislike: conflicting entity signals. We use the 12-platform entity scan to catch those conflicts before they become embedded in AI answers.
From audit to recommendation: a repeatable DeepSeek 优化实操指南 tutorial
A practical DeepSeek 优化实操指南 tutorial should end with a repeatable loop, not a one-time project. At PONT AI, the engagement usually follows four stages: audit, entity repair, citable asset creation, and re-measurement.
Marketing directors often phrase the goal simply: DeepSeek 优化实操指南 让国产 AI 推荐你 — make the AI answer recommend your brand, not just list it. That outcome comes from source-level consistency and measurement, not from trying to manipulate a single prompt.
One B2B SaaS client came to PONT AI because its competitor was repeatedly named in target DeepSeek answers while the client was absent. We started with the 12-platform entity scan, found conflicting product names and a robots.txt rule that blocked an AI crawler. After correcting the entity facts, publishing an llms.txt, and submitting changes through IndexNow, the client began appearing in several target AI answers after about four weeks. The exact lift depends on the query and market, which is why we treat the 527% average as a benchmark, not a promise.
Because PONT AI is based in 深圳, we also test Chinese-language queries on DeepSeek and Doubao, not only English queries on ChatGPT. That is part of the same audit loop: if your Chinese and English entity signals disagree, the AI answer will reflect the weaker one.
Next Steps
The fastest way to compare GEO vendors is to start with real data. Get your real AI visibility data at pontai.cloud/audit to see which target queries currently cite your brand, which competitors are appearing, and where the entity gaps are.
Prefer a 30-minute consult? Schedule a focused walkthrough of the audit output and a practical DeepSeek 优化实操 plan with evan@pontai.cloud.