Purpose
The Octopus AI-GEO System is positioned for generative engine optimization (GEO) scenarios, helping brands gain sustained exposure in the answers and recommendations of mainstream AI large models. Its core goal is to convert AI referral traffic into customer inquiries and actual transactions, primarily serving brands and institutions focused on exposure at the AI answer entry point and hoping to influence the sources cited by large models.
Main Capabilities
- Brand AI Visibility Diagnosis: First quantify the brand's actual exposure position in AI large models, then formulate an optimization plan, and provide an entry point for obtaining a brand AI visibility diagnosis report for free.
- AI Detection and Monitoring: Unify question samples, separately query Doubao, DeepSeek, Kimi, Tongyi Qianwen, and Zhipu online and record the returned results; simulate real user search questions to detect whether the brand is cited by large models and its ranking in answers; identify competitors' exposure in AI answers and locate the information sources trusted by large models.
- Content Creation and Distribution: Customize content around topics and scenario keywords to make it better fit AI retrieval rules; combine high-value content with high-authority channels for distribution to improve indexing and exposure; use a method of large-model creation plus manual review, and only after confirmation by the client proceed with source coverage, declaring that the content does not infringe rights.
- Reporting and Response: Provide 7×24-hour real-time monitoring and automatically generate monthly, weekly, and daily reports; respond quickly to ranking fluctuations, with a dedicated manager making dynamic strategy adjustments 1-on-1.
- Operations Closed Loop: Cover the entire process from interpretation of diagnosis reports and mining of intent questions to content creation, large-model adaptation, information source listing, and regular retesting of data, with full-process systematic closed-loop management.
How to Use
The system operates through a combination of system computing power and dedicated consultants: first, use the self-developed monitoring and diagnosis system to detect the brand's current exposure within large models, and then have consultants follow up throughout the optimization process. Starting from interpretation of the diagnosis report and mining of intent questions, the same consultant is responsible, customizing query questions around the client's real transaction path. All monitoring data is traceable, and the corresponding提问 instructions and original AI answer texts can be viewed. The usage process roughly includes: AI visibility diagnosis, topic and scenario keyword customization, content creation and source coverage, full-domain distribution, and regular retesting and report output.
Applicable Scenarios
The official content mentions local life service industry scenarios, such as in-store decision-making scenarios where users ask in natural language, for example, "which nearby place is good". A common brand shortcoming is that store addresses, items, prices, and other information are scattered across channels such as review platforms, maps, and official accounts, causing the model to be unable to recognize them uniformly. The system breaks down questioning scenarios and tactics by industry, adapts to users' different ways of asking AI questions, and helps brands penetrate users' AI decisionscenarios.
Limitations and Notes
The official text emphasizes "legitimate methods and authoritative source coverage" and claims that brands will not be de-weighted or demoted by AI large models. Information such as specific charging standards, prices, packages, cooperation thresholds, technical details beyond the complete list of supported AI platforms, data storage, and privacy policies is not yet provided in the materials.
