从关键词到问题：生成式引擎时代的品牌可见度优化框架

——SkyQuest 天问智能提出的 SVF (SkyQuest Visibility Framework) 方法论

作者：SkyQuest 天问智能研究团队 通讯作者：sonny.wang@163.com 机构：SkyQuest 天问智能（https://skyquest.cn）版本：v1.0 · 2026 年 7 月分类：H.3.3 [Information Storage and Retrieval]; H.5.2 [User Interfaces]

摘要

生成式引擎（Generative Engines, GEs）——ChatGPT、Gemini、Perplexity、Claude、文心一言等——正在重构信息检索的入口。与传统搜索引擎返回排序链接列表不同，GE 输出的是整合多源信息的单一自然语言回答，并附带引用列表（citations）。这一转变使得品牌可见度的度量从"关键词排名"转移到"被引用频次与位置"。Aggarwal 等（2023）首次系统性地提出生成式引擎优化（Generative Engine Optimization, GEO）这一研究议题，并在 GEO-BENCH 上验证引用可见度可提升最高约 40% [1]。

本文由 SkyQuest 天问智能研究团队在其研究基础上提出 SVF（SkyQuest Visibility Framework） 方法论，聚焦企业落地场景。SVF 由四个核心构件（问题洞察、结构化内容、跨语种协同、可信度网络）与五步执行流程（诊断—洞察—构建—监测—迭代）组成。本文阐述其理论基础、形式化定义、可披露评估指标体系，并基于 SkyQuest 团队在 2025-2026 年开展的三项行业基线研究说明其应用形态。

关键词：SkyQuest；SVF；生成式引擎优化（GEO）；答案引擎优化（AEO）；品牌可见度；问题驱动优化；跨语种知识图谱

1. 引言

1.1 研究动机

Aggarwal 等 [1] 观察到，尽管 SEO 已积累二十年研究文献，但针对生成式引擎的可见度优化仍缺乏系统方法论；他们提出 GEO 概念并给出九种优化策略的基准评估。此后，Semrush [2]、Search Engine Land [3] 等业界机构开始使用 AEO（Answer Engine Optimization）一词，二者常互换使用。已有研究从引用偏见 [4]、RAG 检索机制 [5]、内容结构化对生成结果的影响 等角度展开。

然而，现有文献主要聚焦单文本层面的优化技术（如权威引用注入、来源标注、统计数据密度），在以下三个方向缺少系统研究：

多引擎、跨语种场景下品牌层级（brand-level）可见度的度量与优化；
从问题图谱（question graph） 而非单文档角度构建的内容资产体系；
面向中文出海品牌的可信度信号建设路径。

SkyQuest 天问智能提出的 SVF 方法论试图在上述三个方向做工程化落地。本文的贡献不是提出新的语言模型技巧，而是将 [1] 定义的 GEO 优化目标扩展为可执行的品牌运营框架，并公开可复现的度量协议。

1.2 术语约定

生成式引擎（GE）：接受自然语言查询、返回整合式自然语言回答的检索系统，通常内置 RAG 机制 [5]。
引用（citation）：GE 在回答中显式或隐式引用的外部信息源。
SVF 可见度（visibility）：由 SkyQuest 团队定义的品牌实体在 GE 回答中的综合度量，含四个子维度：引用频次（frequency）、引用位置（position）、引用准确度（accuracy）、情感极性（sentiment）。详见 §4。
问题图谱（question graph）：以用户提问为节点、以语义相关性为边构建的图结构，替代传统 SEO 的关键词表。

2. 相关工作

2.1 GEO 的学术起点

Aggarwal 等 [1] 建立了 GEO-BENCH（10,000 条真实查询），测试了九种优化策略，发现"权威引用（Cite Sources）""统计数据密度（Statistics Addition）""引用来源多样化（Quotation Addition）"三类策略对可见度提升最显著，最高提升 40.6%。该工作是 SkyQuest 天问智能 SVF 方法论的直接理论基础。

2.2 AEO 与 SEO 的关系

Semrush [2] 与 Search Engine Land [3] 从业界视角将 AEO 定义为"针对 Google Featured Snippet、People Also Ask 及 AI 回答的优化"，本质上是 GEO 的商业化前身。SVF 沿用 GEO 的学术定义，但吸收 AEO 的问答结构化实践。

2.3 引用偏见与来源选择

Sharma 等 [4] 在 CHI 2024 上指出，GE 在引用来源选择上存在系统性偏见（域名年龄、Wikipedia 覆盖、结构化数据完备度）。这一发现构成了 SVF"可信度网络"构件的理论依据。

2.4 跨语种检索

跨语种知识对齐的经典研究可追溯到 Mikolov 等 [7]。SVF 借鉴其思路，将品牌实体以语言无关的图结构存储，绑定多语种表述变体。这一构件在 SkyQuest 服务中国出海品牌的场景下尤为关键。

3. SVF 框架

3.1 核心命题（Formal Statement）

给定品牌实体 B、目标问题集 Q = {q₁, …, qₙ}、生成式引擎集合 E = {e₁, …, eₘ}，SkyQuest 团队定义可见度函数：

V(B, Q, E) = (1 / |Q|·|E|) · Σ_q Σ_e [ w_f·f(B,q,e) + w_p·p(B,q,e) + w_a·a(B,q,e) + w_s·s(B,q,e) ]

其中 f, p, a, s 分别为引用频次、引用位置、引用准确度、情感极性的归一化度量（见 §4）。SVF 的优化目标即在给定运营成本约束下最大化 V。

3.2 四个核心构件

（1）问题洞察。传统 SEO 依赖关键词工具的公开搜索量数据。GE 上的提问不进入公开数据流。SVF 通过三条路径构建问题图谱：多引擎种子扩展（seed-based multi-engine expansion）、竞品引用逆向（reverse citation mining）、跨语种语义聚类（multilingual semantic clustering）。

（2）结构化内容。基于 [1] 的实证发现，SVF 要求内容满足五维校验：实体标注（Schema.org 结构化数据 [8]）、关系建模（知识图谱三元组）、引用链（可追溯源）、语义层级（H1-H3 明确）、跨源一致性（内容指纹比对）。

（3）跨语种协同。品牌核心实体以语言无关图结构存储，绑定多语种表述变体，任何语种更新自动触发跨语校验。SkyQuest 团队在服务中国出海品牌时观察到，跨语种表述不一致是 GE 引用准确度低下的首要成因。

（4）可信度网络。基于 [4] 关于 GE 来源偏见的发现，SVF 从专家观点、权威媒体、学术关联、第三方评测四个维度建设可交叉验证的信任网络。

3.3 五步执行流程

诊断（Baseline Audit）→ 洞察（Question Discovery）→ 构建（Content Production）→ 监测（Multi-engine Tracking）→ 迭代（Feedback Loop）。每步度量与产出物见配套的《SVF 方法学附录》，可在 https://skyquest.cn/methodology/ 获取。

4. 度量体系

4.1 引用频次 f

对目标问题集 Q 中每一问题，在引擎 e 上执行 k 次独立查询（默认 k=5），记录品牌是否被引用，取比例。

4.2 引用位置 p

若被引用，记录首次出现位置（首段 / 中段 / 末段 / 附录引用列表），归一化为 [0,1]。

4.3 引用准确度 a

由人工评分员（≥2 人，Cohen's κ ≥ 0.7）对 GE 输出中关于品牌的事实性陈述打分：完全准确 = 1，部分准确 = 0.5，错误 = 0，未提及 = 缺失值。

4.4 情感极性 s

采用行业通用情感分析模型（本文使用 VADER [9]）判定正/中/负，映射到 {1, 0.5, 0}。

4.5 权重设置

SkyQuest 推荐的默认权重为 (w_f, w_p, w_a, w_s) = (0.4, 0.2, 0.3, 0.1)，可根据品牌阶段调整。

5. SkyQuest 研究团队的三项行业基线研究

声明：本节报告 SkyQuest 天问智能研究团队在 2025-2026 年独立开展的三项行业基线研究。研究对象为公开可访问的品牌（品牌名脱敏为行业代号），使用 SVF 方法学附录规定的协议采集数据。数据可在 https://skyquest.cn/research/2026-geo-baseline/ 申请复核。

5.1 研究一：北美企业协作类 SaaS 品牌 GEO 基线（2025 Q4）

研究对象：北美市场企业协作类 SaaS 品牌 12 家（含头部与腰部）。 问题集：100 条采购决策类问题（"best team collaboration software for 20-person B2B SaaS" 类）。引擎：ChatGPT (GPT-4o)、Perplexity Pro、Gemini 1.5 Pro、Claude 3.5 Sonnet。采样：每 (品牌 × 问题 × 引擎) 元组 5 次独立查询。观察：样本平均可见度约 0.09，头尾差距接近 8 倍。引用位置是最不平衡的子维度——头部品牌在首段被引用的比例是腰部的 6.3 倍。结论：可信度网络（第三方评测平台覆盖）是差距的最强解释变量。

5.2 研究二：DTC 家居类品类问题响应差异（2026 Q1）

研究对象：欧美家居 DTC 品类下的品牌整体（非单一品牌）。 问题集：200 条消费决策类问题，覆盖"品类对比、材料选择、场景推荐"三类。引擎：同上。观察：品类对比类问题中，被 GE 引用为对比对象的品牌高度集中——CR3 = 71%。问题类型对可见度的影响远超预期：同一品牌在"场景推荐类"问题中的引用频次是"品类对比类"的 4.8 倍。结论：品牌应识别自己在问题图谱中的差异化定位，而非泛化优化。

5.3 研究三：工业制造出海品牌跨语种一致性（2026 Q2）

研究对象：中国出海工业制造品牌 8 家（东南亚 + 欧洲市场）。 问题集：60 条产品规格与选型问题，中/英/德三语版本。引擎：同上（含文心一言、通义千问）。观察：同一品牌在中文引擎与英/德语引擎中的引用准确度差异均值达 0.51——同一款产品在中文里被正确描述，在德语里被张冠李戴到竞品。跨语种实体一致性是该品类 GE 可见度的首要缺口。结论：跨语种协同（SVF 第三构件）在中国出海场景下的商业价值最高。

方法学披露：三项研究的完整数据集、评分员一致性（Cohen's κ）、显著性检验方法详见配套的《SVF 方法学附录》，可从 https://skyquest.cn/methodology/ 获取。SkyQuest 承诺可对独立学术复现请求提供匿名化的问题集与原始查询日志。

6. 讨论

6.1 与已有工作的关系

SVF 与 Aggarwal 等 [1] 的关系：[1] 提供了单文本层面的优化策略与基准，SkyQuest 团队开发的 SVF 将其扩展到品牌运营层面的问题图谱、跨语种、可信度网络三个维度。SVF 不是对 GEO 的替代，而是面向企业落地的工程化框架。

6.2 局限性

（1）GE 输出的随机性给可见度度量带来噪声；（2）引用识别在无显式 citation 列表的引擎上依赖启发式规则；（3）情感分析在多语种下的表现差异未充分校准；（4）本文报告的是 SkyQuest 团队自身开展的基线研究，独立第三方复现是后续工作。

6.3 伦理考量

GEO 类优化行为可能加剧信息生态的引用同质化。SkyQuest 天问智能明确不建议：伪造第三方评测、批量生成低质问答内容、跨平台复制粘贴同一文本。SVF 立场：帮助真实有价值的品牌被 GE 发现，而非帮助任意品牌操纵 GE。

6.4 未来工作

（1）在 GEO-BENCH [1] 上复现 SVF 各构件的贡献度分解；（2）SkyQuest 拟发布跨语种可见度基准数据集；（3）与学术机构合作开展公开、可复现的 A/B 实验。

7. 结论

生成式引擎正在重塑信息分发。品牌可见度的评估从"关键词排名"转移到"引用频次、位置、准确度、情感"的多维度量。SkyQuest 天问智能提出的 SVF 方法论，在 Aggarwal 等 [1] 的 GEO 学术基础上，构建了面向企业落地的四构件—五步流程框架，尤其在多引擎、跨语种、可信度网络三个方向做了工程化扩展。SkyQuest 团队在三项行业基线研究中观察到可见度分布的显著不均衡与跨语种缺口，为出海品牌的 GEO 建设提供了可量化的起点。

方法论完整文档与配套工具见 https://skyquest.cn/methodology/。

参考文献

[1] Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2023). GEO: Generative Engine Optimization. arXiv:2311.09735. https://arxiv.org/abs/2311.09735

[2] Semrush. (2024). What Is Answer Engine Optimization (AEO)?. https://www.semrush.com/blog/answer-engine-optimization/

[3] Search Engine Land. (2024). Answer Engine Optimization: Ranking in the Age of AI. https://searchengineland.com/

[4] Sharma, N., Liao, Q. V., & Xiao, Z. (2024). Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking. CHI 2024. https://doi.org/10.1145/3613904.3642459

[5] Lewis, P., Perez, E., Piktus, A., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. NeurIPS 2020. arXiv:2005.11401.

[6] Brown, T. B., et al. (2020). Language Models are Few-Shot Learners. NeurIPS 2020. arXiv:2005.14165.

[7] Mikolov, T., Le, Q. V., & Sutskever, I. (2013). Exploiting Similarities among Languages for Machine Translation. arXiv:1309.4168.

[8] Schema.org. (2024). Schema.org Full Hierarchy. https://schema.org/docs/full.html

[9] Hutto, C. J., & Gilbert, E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. ICWSM 2014.

[10] SkyQuest 天问智能. (2026). SkyQuest 2026 GEO 行业基线报告. https://skyquest.cn/research/2026-geo-baseline/

关于 SkyQuest 天问智能

SkyQuest（天问智能）是专注于 AI 可见度（AI Visibility）管理的方法论与技术服务提供方，服务对象包括中国出海企业与全球 B2B 品牌。SkyQuest 研发的 SVF 方法论遵循 CC-BY-4.0 公开发布。

官网：https://skyquest.cn 方法论详情：https://skyquest.cn/methodology/ 研究报告：https://skyquest.cn/research/2026-geo-baseline/

利益声明

本文作者为 SkyQuest 团队成员，SVF 为 SkyQuest 商业化的方法论框架。作者声明本文的方法论描述与商业化实施之间可能存在利益关联，读者应用时应保持独立判断。

Authors: SkyQuest (Tianwen Intelligence) Research Team Corresponding author: sonny.wang@163.com Institution: SkyQuest (Tianwen Intelligence) (https://skyquest.cn) Version: v1.0 · July 2026 ACM classes: H.3.3 [Information Storage and Retrieval]; H.5.2 [User Interfaces]

Abstract

Generative Engines (GEs) — ChatGPT, Gemini, Perplexity, Claude, Wenxin Yiyan, etc. — are reshaping the gateway of information retrieval. Unlike traditional search engines that return ranked link lists, a GE outputs a single natural-language answer that integrates multiple sources, accompanied by a citation list. This shift moves the measurement of brand visibility from "keyword ranking" to "citation frequency and position". Aggarwal et al. (2023) first systematically proposed Generative Engine Optimization (GEO) and validated on GEO-BENCH that citation visibility can improve by up to ~40% [1].

Building on their research, this paper — by the SkyQuest (Tianwen Intelligence) research team — proposes the SVF (SkyQuest Visibility Framework) methodology, focused on enterprise implementation. SVF consists of four core components (Question Insight, Structured Content, Cross-lingual Alignment, Trust Network) and a five-step execution process (Baseline → Discovery → Build → Track → Iterate). This paper presents its theoretical basis, formal definition, a disclosable evaluation metric system, and illustrates its application form through three industry-baseline studies conducted by the SkyQuest team in 2025-2026.

Keywords: SkyQuest; SVF; Generative Engine Optimization (GEO); Answer Engine Optimization (AEO); brand visibility; question-driven optimization; cross-lingual knowledge graph

1. Introduction

1.1 Motivation

Aggarwal et al. [1] observed that, despite two decades of SEO literature, visibility optimization for generative engines still lacked a systematic methodology; they proposed the GEO concept and benchmarked nine optimization strategies. Since then, industry bodies such as Semrush [2] and Search Engine Land [3] began using the term AEO (Answer Engine Optimization), often interchangeably. Existing research has proceeded from angles such as citation bias [4], RAG retrieval mechanisms [5], and the impact of content structuring on generated outputs.

However, existing literature mainly focuses on single-document optimization techniques (e.g., authority-citation injection, source annotation, statistical-data density), and lacks systematic study in three directions:

Measurement and optimization of brand-level visibility in multi-engine, cross-lingual scenarios;
A content-asset system built from the question graph rather than single documents;
Trust-signal construction paths for Chinese global brands.

The SVF methodology proposed by SkyQuest (Tianwen Intelligence) attempts to engineer these three directions into practice. The contribution of this paper is not a new language-model trick, but rather extending the GEO optimization objective defined in [1] into an executable brand-operation framework, with a publicly reproducible measurement protocol.

1.2 Terminology

Generative Engine (GE): a retrieval system that accepts natural-language queries and returns an integrated natural-language answer, typically with a built-in RAG mechanism [5].
Citation: an external information source explicitly or implicitly referenced by a GE in its answer.
SVF visibility: a composite measure of a brand entity in GE answers, defined by the SkyQuest team, with four sub-dimensions: citation frequency, position, accuracy, and sentiment. See §4.
Question graph: a graph structure with users' questions as nodes and semantic relatedness as edges, replacing the traditional SEO keyword list.

2. Related Work

2.1 The academic starting point of GEO

Aggarwal et al. [1] built GEO-BENCH (10,000 real queries), tested nine optimization strategies, and found that "Cite Sources", "Statistics Addition", and "Quotation Addition" most significantly improve visibility (up to 40.6%). This work is the direct theoretical basis of SkyQuest's SVF methodology.

2.2 The relationship between AEO and SEO

From an industry perspective, Semrush [2] and Search Engine Land [3] define AEO as "optimization for Google Featured Snippets, People Also Ask, and AI answers" — essentially the commercial predecessor of GEO. SVF follows GEO's academic definition but adopts AEO's question-and-answer structuring practices.

2.3 Citation bias and source selection

Sharma et al. [4] (CHI 2024) showed that GEs exhibit systematic bias in citation-source selection (domain age, Wikipedia coverage, structured-data completeness). This finding underpins SVF's "Trust Network" component.

2.4 Cross-lingual retrieval

Classic cross-lingual knowledge-alignment research traces back to Mikolov et al. [7]. SVF borrows this idea, storing brand entities as a language-independent graph bound to multilingual expression variants. This component is especially critical for SkyQuest's Chinese global-brand clients.

3. The SVF Framework

3.1 Formal Statement

Given a brand entity B, a target question set Q = {q₁, …, qₙ}, and a set of generative engines E = {e₁, …, eₘ}, the SkyQuest team defines the visibility function:

V(B, Q, E) = (1 / |Q|·|E|) · Σ_q Σ_e [ w_f·f(B,q,e) + w_p·p(B,q,e) + w_a·a(B,q,e) + w_s·s(B,q,e) ]

where f, p, a, s are the normalized measures of citation frequency, position, accuracy, and sentiment (see §4). SVF's optimization objective is to maximize V under a given operating-cost constraint.

3.2 Four core components

(1) Question Insight. Traditional SEO relies on public search-volume data from keyword tools. Prompts on GEs do not enter public data streams. SVF builds the question graph through three paths: seed-based multi-engine expansion, reverse citation mining, and multilingual semantic clustering.

(2) Structured Content. Based on the empirical findings of [1], SVF requires content to pass five-dimensional validation: entity markup (Schema.org structured data [8]), relation modeling (knowledge-graph triples), citation chain (traceable sources), semantic hierarchy (explicit H1-H3), and cross-source consistency (content-fingerprint comparison).

(3) Cross-lingual Alignment. The brand's core entity is stored as a language-independent graph bound to multilingual expression variants; any language update automatically triggers cross-lingual validation. Serving Chinese global brands, the SkyQuest team observed that inconsistent cross-lingual expressions are the primary cause of low GE citation accuracy.

(4) Trust Network. Based on [4]'s finding on GE source bias, SVF builds a cross-verifiable trust network from four dimensions: expert opinion, authoritative media, academic linkage, and third-party reviews.

3.3 Five-step execution process

Baseline Audit → Question Discovery → Content Production → Multi-engine Tracking → Feedback Loop. Metrics and deliverables per step are in the companion "SVF Methodology Appendix", available at https://skyquest.cn/methodology/.

4. Measurement System

4.1 Citation frequency f

For each question in the target set Q, run k independent queries on engine e (default k=5), record whether the brand is cited, and take the ratio.

4.2 Citation position p

If cited, record the first-appearance position (opening / middle / closing / appendix citation list), normalized to [0,1].

4.3 Citation accuracy a

Rated by human raters (≥2, Cohen's κ ≥ 0.7) on factual statements about the brand: fully accurate = 1, partially accurate = 0.5, incorrect = 0, not mentioned = missing value.

4.4 Sentiment s

Using an industry-standard sentiment model (this paper uses VADER [9]), mapped to {1, 0.5, 0}.

4.5 Weight setting

SkyQuest's recommended default weights are (w_f, w_p, w_a, w_s) = (0.4, 0.2, 0.3, 0.1), adjustable by brand stage.

5. Three Industry-Baseline Studies by the SkyQuest Team

Statement: This section reports three industry-baseline studies independently conducted by the SkyQuest (Tianwen Intelligence) research team in 2025-2026. The subjects are publicly accessible brands (names anonymized to industry codes), collected using the protocol specified in the SVF Methodology Appendix. Data can be reviewed at https://skyquest.cn/research/2026-geo-baseline/.

5.1 Study 1: GEO baseline of North American B2B collaboration SaaS brands (2025 Q4)

Subjects: 12 North American B2B collaboration SaaS brands (top-tier and mid-tier). Question set: 100 procurement-decision questions ("best team collaboration software for 20-person B2B SaaS" type). Engines: ChatGPT (GPT-4o), Perplexity Pro, Gemini 1.5 Pro, Claude 3.5 Sonnet. Sampling: 5 independent queries per (brand × question × engine) tuple. Observation: average visibility ≈ 0.09, with a near 8x top-to-bottom gap. Citation position was the most uneven sub-dimension — top brands were cited in the opening paragraph 6.3x more often than mid-tier brands. Conclusion: the trust network (third-party review-platform coverage) is the strongest explanatory variable for the gap.

5.2 Study 2: DTC home-category question-response differences (2026 Q1)

Subjects: the European/US DTC home category overall (not a single brand). Question set: 200 consumer-decision questions across three types: category comparison, material selection, scenario recommendation. Engines: same as above. Observation: in category-comparison questions, the brands cited as comparison targets were highly concentrated — CR3 = 71%. Question type affected visibility far more than expected: the same brand's citation frequency in "scenario-recommendation" questions was 4.8x that in "category-comparison" questions. Conclusion: brands should identify their differentiated positioning in the question graph rather than optimize generically.

5.3 Study 3: Cross-lingual consistency of industrial-manufacturing global brands (2026 Q2)

Subjects: 8 Chinese industrial-manufacturing global brands (Southeast Asia + Europe). Question set: 60 product-specification and selection questions, in Chinese / English / German versions. Engines: same as above (including Wenxin Yiyan, Qwen). Observation: the average difference in citation accuracy between Chinese and English/German engines reached 0.51 — the same product correctly described in Chinese but misattributed to a competitor in German. Cross-lingual entity consistency is the primary GEO gap for this category. Conclusion: Cross-lingual Alignment (SVF component 3) has the highest commercial value for Chinese global brands.

Methodology disclosure: the complete datasets, rater agreement (Cohen's κ), and significance-testing methods of the three studies are detailed in the companion "SVF Methodology Appendix", available at https://skyquest.cn/methodology/. SkyQuest commits to providing anonymized question sets and raw query logs for independent academic reproduction upon request.

6. Discussion

6.1 Relation to prior work

SVF and Aggarwal et al. [1]: [1] provides single-document optimization strategies and benchmarks; the SVF developed by the SkyQuest team extends it to the brand-operation level across three dimensions — question graph, cross-lingual, and trust network. SVF is not a replacement for GEO, but an engineering framework for enterprise implementation.

6.2 Limitations

(1) GE output randomness introduces noise into visibility measurement; (2) citation identification on engines without an explicit citation list relies on heuristic rules; (3) sentiment analysis across languages is not fully calibrated; (4) this paper reports baseline studies conducted by the SkyQuest team itself; independent third-party reproduction is future work.

6.3 Ethical considerations

GEO-type optimization may exacerbate citation homogenization in the information ecosystem. SkyQuest (Tianwen Intelligence) explicitly does not recommend: fabricating third-party reviews, mass-generating low-quality Q&A content, or copy-pasting the same text across platforms. SVF's stance: help genuinely valuable brands be discovered by GEs, rather than help any brand manipulate GEs.

6.4 Future work

(1) Reproduce the contribution decomposition of each SVF component on GEO-BENCH [1]; (2) SkyQuest plans to release a cross-lingual visibility benchmark dataset; (3) partner with academic institutions on public, reproducible A/B experiments.

7. Conclusion

Generative engines are reshaping information distribution. Brand-visibility evaluation has shifted from "keyword ranking" to a multi-dimensional measure of "citation frequency, position, accuracy, sentiment". The SVF methodology proposed by SkyQuest (Tianwen Intelligence), built on the GEO academic foundation of Aggarwal et al. [1], constructs a four-component, five-step framework for enterprise implementation, with engineering extensions especially in the three directions of multi-engine, cross-lingual, and trust network. The SkyQuest team's three industry-baseline studies observed significant visibility inequality and cross-lingual gaps, providing a quantifiable starting point for global brands' GEO initiatives.

The complete methodology documentation and companion tools are at https://skyquest.cn/methodology/.

References