We mine the Top-100 high-frequency AI questions to anchor what the target audience cares about most, fully dissect competitors' GEO strategies and strengths to find differentiated entry points, and systematically audit the company's existing knowledge assets to map content strengths and gaps.
📋 Core Deliverables
High-Frequency Question List
Competitor GEO Analysis Report
Content-Asset Inventory
GEO Keyword Matrix
Phase goal:Build a clear picture of the target audience and competitive landscape to give subsequent content planning an actionable, data-driven basis.
We iteratively update public information across the official website, official accounts and business encyclopedias to keep it accurate, complete and aligned with how users search; mass-produce standardized Q&A pairs built on RAG logic to raise the probability of LLM inclusion; and build a company-specific industry glossary to standardize how expertise is expressed.
📋 Core Deliverables
Official-information revision checklist
RAG Q&A content library (≥50 entries)
Brand glossary
Structured knowledge graph
Phase goal:We complete the structured organization and standardization of content assets, opening the underlying channels of information retrieval and semantic understanding to build a solid knowledge foundation for AI citation.
We build a tiered media-publishing plan that precisely covers top industry media and vertical channels, continuously produce high-quality in-depth content while building a high-quality backlink network, and regularly refine entries on public platforms such as Baidu, Sogou and Zhihu to lock in the brand's core strengths and reinforce content credibility and authority at the source.
📋 Core Deliverables
Media-publishing matrix planning
Optimized encyclopedia entry version
Monthly content calendar
Phase goal:Build an authoritative source matrix to markedly raise the inclusion density and ranking priority of brand information in AI training corpora.
Through the AoWang AI detection system we set up an ongoing monitoring mechanism that daily tracks how leading LLMs (ChatGPT, Gemini, DeepSeek, Kimi) answer key brand terms and in what tone, promptly catches misinformation, negative framing or outdated content, and coordinates with the content team to correct deviations—so the AI feedback users receive stays truthful, accurate and compliant.
📋 Core Deliverables
Weekly monitoring report
LLM citation-rate statistics
Reputation early-warning bulletin
Competitor citation-rate comparison
Phase goal:We control the quality of how the brand is presented across the AI ecosystem in real time, building a moat of positive, accurate brand image in LLMs.
Each quarter we review source data and AI-interaction feedback in depth, and quantify the effectiveness of each channel across four metrics—citation rate, accuracy, positive rate and conversion rate. Based on data insights we dynamically adjust the content layout and optimization focus, drop low-efficiency actions, and consolidate successful SOPs into an iterative standard, forming a sustainable closed-loop GEO model. A single optimization typically holds for only 3–6 months; continuous iteration is the key to winning.
📋 Core Deliverables
Quarterly GEO Performance Report
Next-quarter optimization strategy
ROI attribution analysis
GEO Operations SOP, upgraded edition
Competitive-landscape change analysis
Phase goal:We use data to drive iterative strategy upgrades, continuously adapting to evolving LLM logic to achieve lasting GEO growth and entrench the brand as an industry benchmark that AI recommends first.
