Will GEO adjust its strategy in the face of domestic large models such as DeepSeek?

Will GEO adjust its strategy in the face of domestic large models such as DeepSeek?

Yes—there will be adaptation, not a complete rebuild. In real deployments, companies find that whether the model is overseas or a domestic leader like DeepSeek, the fundamentals stay the same: high-quality source content, clear structure, and consistent brand semantics. What changes is how you express that meaning in Chinese contexts and where you distribute it so domestic models can reliably “see” and cite you.

Why This Question Matters (A Very Common Scenario)

Many B2B companies already invest in English GEO for global visibility—and then ask a practical question: “If we want to perform well on Chinese-facing AI products and domestic LLMs (e.g., DeepSeek), do we need to start over?”

In most cases, the answer is no. The winning approach is to keep your global GEO backbone and implement a localized layer that improves retrievability, citability, and faithful brand interpretation in Chinese contexts.

How Domestic LLMs Differ in AI Search Context

In AI search and answer generation, differences between models usually show up in three layers: data sources, language habits, and semantic preference. These differences affect which content gets retrieved, how it gets summarized, and whether you become a recommended option.

1) Training & Retrieval Sources: “Where You Speak” Becomes a Strategy

Overseas models tend to lean on English-first sources and globally indexed websites; domestic models often show stronger affinity for Chinese-language ecosystems and industry materials. This shifts the GEO focus from only “publishing good content” to publishing it in the right local channels.

Reference benchmarks (for planning): mature B2B websites often see 35%–60% of GEO wins coming from non-homepage knowledge assets (spec sheets, FAQs, comparisons, case studies), not from a single “about” page.

2) Language Is Not Translation: It’s Reasoning and Framing

English technical writing typically rewards direct conclusions and structured evidence. Chinese business writing often emphasizes context, problem framing, and progressive logic. If you only translate, you risk losing retrieval cues and user trust signals.

3) Semantic Preference: Some Models Prefer “How It’s Used”

When different engines generate answers, they may prioritize different “understanding paths.” Some perform best with rigid taxonomy (parameters → standards → compatibility), while others surface brands that describe use cases (industry → workflow → deployment). The practical GEO answer is to cover both dimensions—without changing your core claims.

What to Adjust: A Multi-Model GEO Playbook

Below is a field-tested approach many teams use to handle “global + domestic LLM” coexistence. Think of it as one strategy with localized tuning.

A) Build a Multilingual Content Corpus (Not Just a Bilingual Site)

The goal is to create two strong knowledge graphs (English + Chinese) that map to the same product truth. For B2B brands, a practical minimum is: 20–40 high-intent pages per language to start (product categories, core SKUs, use cases, compliance, FAQs, comparisons).

B) Avoid “Direct Translation”—Do Semantic Reconstruction

Semantic reconstruction means you keep the same technical claims but express them in the form that a local reader (and local model) can parse quickly: definitions, synonyms, typical questions, constraints, and purchase decision logic.

C) Distribute Across Multiple Platforms (Because Models Crawl Differently)

Your website should remain the canonical source, but multi-platform distribution increases the probability that different models encounter and validate your entity. A robust baseline for B2B is: Official site + industry directories + technical communities + content platforms.

As a reference point, teams often see 20%–45% of AI-generated brand mentions influenced by off-site materials (technical articles, partner pages, citations, reposted specs)—especially in localized ecosystems.

D) Maintain One Unified Semantic Core (So Models Don’t See “Different You”)

Multi-model environments punish inconsistency. If one model reads you as a “manufacturer,” another reads you as a “trader,” and a third reads you as a “solutions integrator,” your conversion rate will suffer even if your traffic grows.

E) Keep Testing Across Engines (Visibility, Accuracy, and “How You’re Described”)

In practice, teams run monthly or biweekly GEO checks by asking different AI systems the same set of questions: Are we mentioned? Is the description correct? Which pages are cited? Then they refine content with a bias toward clarity and evidence.

Real-World Patterns (What Works Across Models)

While results vary by industry and footprint, three patterns appear frequently in multilingual GEO programs:

Do You Need to Optimize Separately for Each Model?

Not completely. You don’t want fragmented messaging where each engine “learns” a different version of your brand. Instead, use a layered approach:

1. One semantic core (identity, taxonomy, proof, boundaries)
2. Two language systems (English + Chinese, each reconstructed for native logic)
3. Multi-channel distribution aligned with where your buyers and local engines source information
4. Ongoing testing for mention rate and correctness

Domestic models becoming more important depends on your target market—but the trend is clear: the future is not “one model,” it’s multiple models coexisting.

Build Multi-Language GEO That Performs Across Models