Join NowENDetect GEO Promotional Link Optimization EffectsQuickly assess how your promotional articles are referenced on AI platforms like DeepSeek, Doubao, Tongyi Qianwen, etc., using data to support decision-making.Step 1: Input Article Title0 / 80Step 2: Input Promotional LinkStep 3: Configure Detection Questions(Affects final score coverage)Smart Match Detection QuestionsThe system will automatically generate 5 core questions based on the article content for detectionCustom Detection QuestionsManually configure specific detection directions (requires 3 questions)Step 4: Select Detection PlatformStart AI Citation DetectionWhy Choose GEO Detection?Sent promotional materials, how do you know if they are effective? We help you evaluate how your content is cited on AI platforms.Quick AssessmentAbout 2 minutes after pasting the link, query China’s top 5 AI platforms to quickly obtain citation status.Exclusive GEO ScoringComprehensive assessment from three dimensions: ‘inclusion status’, ‘citation frequency’, and ‘question coverage’ to help you judge the AI exposure effect of promotional content.Citation TracingNot only see the score, but also see which platform mentioned your content when answering what question.Coming SoonBatch DetectionSubmit up to 100 links at once, process them uniformly in the background, and view them uniformly after completion.Effect TrackingHistorical records are automatically saved, supporting comparison at different time points to observe changes in AI exposure trends.Coming SoonReport ExportGenerate visual reports with citation details for easy reporting to clients or leaders.Complete Detection in Three StepsNo complicated configuration required, just paste the link to startStep 1: Paste LinkEnter the title of your published article, then paste the article URL. Supports single submission and intelligently extracts the core content.Step 2: Smart QueryThe system automatically queries relevant questions to various AI platforms, covering China’s top 5 mainstream AI platforms, parallel query to improve efficiency, real-time display of progress.Step 3: View ReportGet scores and citation details to understand how AI platforms cite your content, visual score display, detailed citation details, support export and sharing.SEO vs GEO: New Era Traffic Entry PointsTraditional SEO focuses on search engine rankings, while GEO focuses on whether your content is recommended by AI assistants to users.Comparison DimensionTraditional SEOGEO Generative OptimizationOptimization TargetGoogle/Baidu Search RankingsAI Platform Citations & RecommendationsKey MetricsKeyword Rankings, Click-through RatesAI Citation Rate, Answer CoverageUser BehaviorActive Search ClicksPassive AI Answer ReceptionImpact ScopeSearch Results PageAI Conversations, Smart AssistantsFuture TrendsGrowth StabilizingExponential GrowthOptimization TargetTraditional SEOGoogle/Baidu Search RankingsGEO Generative OptimizationAI Platform Citations & RecommendationsKey MetricsTraditional SEOKeyword Rankings, Click-through RatesGEO Generative OptimizationAI Citation Rate, Answer CoverageUser BehaviorTraditional SEOActive Search ClicksGEO Generative OptimizationPassive AI Answer ReceptionImpact ScopeTraditional SEOSearch Results PageGEO Generative OptimizationAI Conversations, Smart AssistantsFuture TrendsTraditional SEOGrowth StabilizingGEO Generative OptimizationExponential GrowthHear What They SayReal reviews from enterprise marketing managersZhang MingContent Marketing Director · Tech Company"After using the GEO detection tool, we found that only 30% of our articles were cited by AI platforms. Through targeted optimization, the citation rate increased to 78% within 3 months, and brand exposure in AI search tripled.“Li FangSEO Manager · E-commerce Platform"When formulating GEO strategies for clients, this tool is an essential verification method. The professional scoring report allows clients to clearly see the input-output ratio, greatly enhancing our service value.“Wang QiangMarketing Consulting Partner · Consulting Company"Previously, we only focused on Baidu rankings and completely ignored AI search as a channel. GEO detection let us see new traffic entry points, and now AI platforms bring us over 100,000 brand impressions per month.“You Might AskHow does GEO detection work?Based on the URL content you submit, we intelligently generate relevant questions, then query mainstream AI platforms (Doubao, Baidu AI, Tongyi Qianwen, DeepSeek, Tencent Yuanbao, etc.), analyze whether your links are cited in the answers, and provide a comprehensive score.How long does detection take?Single link detection is usually completed within 30 seconds to 2 minutes, depending on the response speed of each AI platform. Batch detection supports background processing, you can check progress at any time, and the system will automatically save results after completion.How to improve citation rates on AI platforms?Our reports provide detailed optimization suggestion

Join NowEN在线咨询提交需求AI Search Visibility CheckerIntelligently detect your brand’s visibility on AI platforms - Professional GEO optimization analysis toolStep 1: Select Detection PlatformsDoubaoWenxin YiyanDeepseekTongyi QianwenTencent YuanbaoStep 2: The question you ask AIRecommended Keywords0/30Step 3: Brand Keywords+Multiple hit terms please press Enter key, support up to 0/10 terms at onceStart GEO DetectionDetection PrincipleDetection PrincipleSimulate real user search scenariosAnalyze brand mentions in AI responsesCount recommendation frequency and rankingExpose competitor performance and impactDetection AdvantagesDetection AdvantagesCover 5 major AI platformsReal-time detection, accurate resultsMulti-dimensional data analysisProfessional optimization suggestionsApplication ScenariosApplication ScenariosBrand awareness monitoringCompetitor analysis comparisonGEO optimization effectiveness assessmentMarketing strategy developmentContact UsLet’s Start the New Era of AI Marketing TogetherContact us now to get exclusive GEO optimization solutions. Help your brand seize opportunities in the era of AI intelligent Q&A and achieve leapfrog business growth.Scan QR code to add human customer serviceBook a Free ConsultationNameCompany NamePhone NumberYour RequirementsSubmit BookingAbout UsXiamen Xianglian Technology Co., Ltd. was founded in 2005 and is a national high-tech enterprise with nearly 20 years of experience in the Internet service field.The company operates core platforms including Webmaster Home, Webmaster Tools, and AIBase: Webmaster Tools, as one of China’s TOP3 SEO/website operation and maintenance tool platforms, provides over 200 free tools, has served more than 5 million users, and processes tens of millions of daily queries;AIBase is positioned as China’s leading AI navigation platform, aggregating 10,000+ AI models and tools to provide one-stop AI services for developers and enterprises.Xianglian Technology is committed to providing professional, efficient technical tools and service support for Chinese Internet practitioners, helping digital transformation and development.Address：11th Floor, Building B, Feiyu Building, Huli District, Xiamen, Fujian ProvincePhone Number：18950101783Empowering the Future, Your AI Solution Knowledge BaseEnglish简体中文繁體中文にほんごCopyright © 2024-2026 厦门享联科技有限公司备案号：闽ICP备08105208号-24

Join NowEN在线咨询提交需求AI Search Engine OptimizationBrand Monitoring ServiceMonitor your brand across Google Gemini, ChatGPT, Claude & all major AI models.1Add Brand Information2Analyze Brand Keywords & Competitors3Analysis ReportMonitor by Brand DescriptionMonitor by Website AddressYour Brand NameYour Brand Description0 / 500Your Products/ServicesPlease press Enter to confirm products/services, at least 2 requiredSelect Monitoring AI PlatformSelect Monitoring Time1days7days15daysCustomizableStart AnalysisBrand Score RankingRankBrand NameScoreMeet Enterprise Data Customization NeedsCustomized ReportsAutomatically track your brand through exclusive brand reports. Track key KPIs such as brand mentions, brand coverage, or domain referencesOn-Demand SupportRely on our experienced experts to support you in building artificial intelligence search strategiesEnd-to-End SolutionsGuide optimization of enterprise/product/service pages, community sentiment, and tweet contentContact UsMost Extensive CoverageAnalyze responses from mainstream large models and AI searches at home and abroad, including: Yuanqi, MetaCat, ChatGPTComprehensive Competitive AnalysisIdentify strengths and weaknesses, find gaps to compensate for brand blind spotsGrasp Current Market HotspotsObtain real-time intelligence on all brand-related prompts, keywords, and content sourcesBrand Exclusive TrackingMonitor key metrics at all levels, from the overall brand to individual productsEmpowering the Future, Your AI Solution Knowledge BaseEnglish简体中文繁體中文にほんご© 2026AIBase

AI改变搜索引擎模式超过60%的用户开始使用AI助手查询产品和服务推荐，而不是传统搜索引擎。当用户问“最好的项目管理工具是什么？”时，你的品牌是否出现在回答中？用户提问哪个品牌的智能手表更适合运动？如果AI没有推荐你的品牌，你就失去了这个潜在客户

AI NEWSLatest AI NewsArticleMeta’s Strongest Model Muse Spark 1.3 Released: Coding Capabilities Outperform GPT-5.6 Sol, Price Remains UnchangedPublished in Latest AI NewsTime :Sep 3, 2026Read :4minuteMeta Platforms released its strongest AI model, Muse Spark 1.3, on September 2, 2026, specifically designed to extend agent workflows and enhance coding capabilities. Alexandr Wang, Meta’s Chief AI Officer, stated that the model surpasses OpenAI’s GPT-5.6 Sol in coding ability and matches the performance of Anthropic’s Claude Fable 5.1.Cleaner Code, Fewer Tokens, Long-Term Agent Tasks as the Main BattlefieldCompared to Muse Spark 1.2, version 1.3 reduces unnecessary code iterations, making the code cleaner and the overall coding style more clear: tool calls are reduced by about 20%, and the number of tokens needed to complete the same task is reduced by 25%.Muse Spark 1.3 is optimized for long-term agent tasks, enabling collaboration on multiple workflows within a single long thread, proactively correcting plan gaps, and tracking learning outcomes. When faced with ambiguous instructions, it actively asks for clarification; when encountering obstacles, it requests user assistance; and before performing critical operations, it seeks confirmation first.In addition, the reliability of the new model in handling complex long instructions has significantly improved, retaining detailed requirements and not losing constraints in multi-step tasks; multi-task processing capabilities have also been enhanced, allowing more accurate mapping of new instructions to the correct task within a single thread. The model has a stronger understanding of its own abilities and limitations, thereby reducing hallucination outputs.Pricing Remains the Same as the Previous Version, Will Be Gradually Integrated into Instagram, FacebookIn terms of pricing, Muse Spark 1.3 maintains the same price as its predecessor, Muse Spark 1.2, and offers paid access to developers through the Meta Model API: $1.25 (approximately 8.4 RMB) per 1 million tokens input, $0.15 (approximately 1 RMB) per 1 million tokens cache hit input, and $4.25 (approximately 28.6 RMB) per 1 million tokens output.Wang revealed that the adoption of the API platform is strong, with some developers using up to trillions of tokens per week. The new model will be gradually integrated into Instagram, Facebook, and the Meta AI assistant.

AI NEWSLatest AI NewsArticleTmall Officially Launches AI Space Station: Alibaba Cloud, Zhipu, Kimi, and MiniMax Jointly JoinPublished in Latest AI NewsTime :Sep 3, 2026Read :5minuteThe consumer-level popularization of domestic large model services has reached an important milestone. On September 3, Tmall officially launched a new AI space station (Token recharge center), for the first time fully integrating standardized subscription services from multiple leading large model manufacturers into the mainstream e-commerce ecosystem, allowing ordinary consumers to purchase AI products as conveniently as buying daily mobile phone data or recharging phone credit.The newly launched AI recharge center integrates core resources from leading domestic large model manufacturers such as Alibaba Cloud, Zhipu, Kimi, and MiniMax. In terms of specific product matrices, the platform not only includes flexible period-based subscription plans such as Token Plan and Coding Plan, but also provides recharge schemes that cater to different usage intensities, and fully supports two efficient and convenient delivery methods: card codes or direct recharge. The day before, Zhipu announced its official entry onto Tmall by opening an official flagship store and launching related subscription packages; on the first day of opening, the search volume for related products surged by an astonishing 40 times compared to the previous period.For ordinary users, the threshold for accessing and using large models is being lowered in an unprecedented way. Users just need to open the Taobao App, enter “token” in the search bar, and click “Buy Now” to directly reach the dedicated venue, or find the “AI Model” entry through the “More” option in the “Recharge Center” to make a purchase. This approach of bringing large model services to traditional e-commerce platforms marks that AI commercialization is accelerating from a single developer direct sales and official website subscriptions to a broader national-level consumer market.Industry analysts point out that the collective entry of large model manufacturers into mainstream e-commerce platforms goes beyond simply adding an online sales channel. E-commerce ecosystems represented by Tmall flagship stores and centralized recharge centers have successfully transformed originally complex AI services into standardized, self-purchasable, and auto-renewable mature products. This model not only makes the purchasing process extremely intuitive, but also provides mainstream mass consumers with an opportunity to access all-in-one AI tools in one place.In addition to virtual model services and token recharge businesses, Tmall’s layout in the field of physical AI smart hardware is also continuously accelerating. Currently, the platform has been rapidly launching new computer models pre-installed with Agent intelligences, smart learning machines equipped with the Qwen large model, smart speakers, and AI glasses among several cutting-edge categories. By deeply integrating online large model token services with offline smart hardware, Tmall is committed to creating a comprehensive one-stop AI consumer experience covering both software and hardware.

AI NEWSLatest AI NewsArticleMusk Enters the Copilot Arena: Grok Bot Deeply Takes Over Microsoft AccountsPublished in Latest AI NewsTime :Sep 8, 2026Read :3minuteGrok Bot has recently undergone a major upgrade and officially announced full integration with Microsoft accounts. Through dedicated plugins, it connects to core office components such as Outlook, Calendar, and OneDrive, enabling it to comprehensively assist users with complex daily office tasks, including sending and receiving emails, creating, modifying, and replying to meeting schedules, as well as browsing and uploading files to cloud storage.Actually, the underlying connectors were already available back in May of this year. The key highlight of this update is adapting these connectors into a persistent cloud PC plugin for Grok Bot. This change marks a significant leap from Grok Bot’s previous “passive response” mode, allowing it to actively handle work tasks, such as automatically reading emails and drafting replies in advance.In terms of industry ecosystem and technical approach, major technology giants and open-source communities are actively developing intelligent agents based on Microsoft accounts. Currently, Microsoft Copilot, Anthropic, and open-source projects like OpenClaw are all competing in this space. Notably, Microsoft has adopted a relatively open standard licensing process for third parties, while Google has imposed more restrictions on automated login processes.Compared to other similar products, the biggest advantage of Grok Bot is that users do not need to configure complex local devices or servers. After authorization, it can run continuously in the cloud 24/7. To ensure security and controllability, the system requires user approval before executing any critical operations, ensuring that the final decision-making power always remains with the user themselves.

AI NEWSLatest AI NewsArticleArm CEO: AI Can Help Humanity Conquer Cancer in Our Lifetime, But Chip Supply Is the Biggest BottleneckPublished in Latest AI NewsTime :Sep 8, 2026Read :6minuteArm’s CEO said that artificial intelligence might help humanity cure cancer within our lifetime. However, before achieving this goal, the entire industry first needs to address a practical issue: producing enough chips to provide the required computing power for AI.“I have always believed that the truly disruptive application areas of AI are in healthcare,” said Arm CEO Rene Haas in a BBC interview video released on Tuesday. “AI can not only shorten the time required for drug development but also the time needed for drug testing.““First, we need more wafer fabrication plants"Haas joined Arm in 2013 and became CEO in February 2022, previously working at NVIDIA for seven years as Vice President of Computing Products. In the interview, he stated that AI could potentially solve some problems that humans alone would find difficult to overcome: “I believe that AI will help humanity cure cancer during our lifetime.“However, he pointed out that this requires massive computing power, and the chips needed for cancer research and humanoid robot development are being consumed in large quantities by AI companies planning to build data centers with gigawatt-level capacity. “Currently, computing power supply is indeed very limited. Before we can build data centers in space, we first need more wafer fabrication plants, and I can tell you that clearly.“Haas said that the demand for chips and memory from AI is at a level the semiconductor industry has never seen before, and building new wafer fabrication plants cannot quickly solve the problem—building one may require hundreds of billions of dollars, and the construction period usually takes two to three years. He said the semiconductor industry is currently in an “absolute environment of supply constraints,” and the shortage will continue for some time.Curing cancer is not just about computing power, and expanding data centers has also sparked oppositionEven if the chip bottleneck is solved, it does not guarantee that AI will cure cancer in the next few years. Medical researchers point out that cancer is not a single disease but a large group of more than 100 diseases, and there are significant differences between patients’ cancers. AI can indeed help find potential drug targets and predict drug effects, but evidence of its actual impact on clinical medicine remains limited, and the vast number of variables within the human body make it difficult for models to reliably predict the effectiveness of candidate drugs.Other chip executives have also issued similar warnings. Manish Bhatia, Chief Operating Officer of Micron, said in January this year that the current memory shortage “is indeed unprecedented,” with HBM consuming a large amount of capacity, causing “significant supply shortages” for traditional products such as phones and PCs; Qualcomm CEO Cristiano Amon said in a February earnings call that “the memory shortage and price increases across the industry” will determine the scale of the smartphone market this year.In recent years, the expansion of data center construction in the United States has been rapid, with over 1,400 data centers completed or approved for construction by the end of last year. As data centers continue to enter communities, their impact on air quality and water resources has sparked controversy. A Gallup survey in May this year showed that 70% of 1,000 American adults oppose building data centers for AI in their local area, with nearly half expressing “strong opposition.”

AI NEWSLatest AI NewsArticleMicrosoft Releases Project Opal: Target AI Works for Hours to Deliver the Final ProductPublished in Latest AI NewsTime :Sep 8, 2026Read :4minuteSatya Nadella, CEO of Microsoft, posted a tweet on the X platform on September 7, announcing a new AI feature called Project Opal for Microsoft 365 Copilot. The feature is designed to allow users to plan and execute complex, long-term tasks in a controlled and secure environment using AI agents.Microsoft states that the core of Project Opal is to let AI gradually move from handling quick issues to taking on delegated work and long-running automated tasks within a controllable and traceable environment. After receiving a task, it can work continuously for hours or even days, automatically completing the entire process of “finding materials, creating tables, generating PPTs, and sending them to colleagues.“A Case Study: Analyzing One Month of Outdoor Camera Footage and Delivering the Full ResultsIn the case demonstrated by Nadella, the feature can analyze one month of outdoor camera footage: the AI will browse the materials, identify all scenes where animals appear, and extract the best clips to create a highlight video. At the same time, it will add information such as the camera source, date of capture, and species tags for each animal. Then, it will compile all records into an electronic spreadsheet for easy user retrieval and statistics. After the data is organized, Copilot will generate a PowerPoint overview of the results and share the video, table, and presentation with Teams for team members to view and review.Regarding usage, users can enter the Frontier module in the desktop or web version of Microsoft 365 Copilot, input the task requirements, and click “Start.” The system will then generate an execution plan, launch a dedicated Windows 365 Cloud PC, and use a reasoning model to control the browser to perform the required actions. Project Opal uses an advanced reasoning model to convert natural language requests into dynamic plans and adjusts the order of tool calls based on task progress.Early Applications: Saving Up to 20 Hours Weekly for Compliance AuditsMicrosoft’s early applications include quarterly compliance audits, group member requests, timesheets and leave applications, and onboarding new employees. For example, in the audit scenario, Project Opal can access approved websites, collect and organize screenshots, and verify documents. Microsoft states that its engineers have saved up to 20 hours weekly in this scenario.

AI NEWSLatest AI NewsArticleDeepSeek V4.1-Flash Beta: Stronger Visual Intelligence, Extreme Speed and Cost EfficiencyPublished in Latest AI NewsTime :Sep 8, 2026Read :3minuteOn September 8th, multiple sources confirmed that DeepSeek V4.1-Flash has officially begun its intermediate version internal testing. This version introduces a new model structure, achieving native multi-modal support while offering stronger overall capabilities, faster operation speeds, and lower calling costs.In terms of calling methods, developers can keep the base_url unchanged and simply set the model name to deepseek-v4.1-flash-expires-on-0910 to directly call it, with the current billing standards remaining consistent with DeepSeek-V4-Flash. From external feedback so far, the most significant feature of this new Flash version is “extremely fast speed,” demonstrating highly efficient pattern processing efficiency.Looking back at its technical iteration path, multi-modal capabilities were previously a relatively weak area for DeepSeek. Just on August 21st this year, the official announced the launch of the new multi-modal visual understanding model V4-Flash-Vision-Exp on the API platform. This experimental model matches the official version in pure text capabilities (including Agent, reasoning, world knowledge, etc.), but achieved a significant improvement in the visual understanding AgentBenchmark test. Its multi-modal Agent capabilities are already very close to Opus-4.8.In addition to the model’s iteration, its open-source ecosystem has also seen rapid progress. On August 13th, DeepSeek Harness v0.1 Developer Preview was made available for global developers to test and was simultaneously open-sourced under the MIT license. After its release, the project quickly sparked discussions on overseas social platforms such as X, and within less than 24 hours, it received over 70,000 GitHub stars.Notably, before this, DeepSeek V4 Pro had been officially launched, focusing on enhancing Agent capabilities. With the official release of the entire V4 series of models, the official also adjusted the API pricing system, introducing a peak-valley time-based pricing mechanism. The internal testing of V4.1-Flash marks another key step in its multi-modal and performance optimization journey.