Industry Definition and Core Logic - GEO (Generative Engine Optimization) focuses on generative AI (such as ChatGPT, Doubao, DeepSeek) in search and dialogue scenarios, optimizing brand content's semantics, structure, credibility, and multimodal adaptability to enhance brand visibility and authority in AI-generated answers [1] - GEO represents a shift from traditional SEO, which centers on "link ranking," to a focus on "language model trust," transforming core brand information into AI-trustworthy knowledge assets [1] Market Size and Growth Drivers - According to IDC, the GEO market in China is expected to reach 3 billion yuan by 2026, with a year-on-year growth of 1100%, and the industrial manufacturing sector will account for over 45% of this market [1] - The rapid growth is driven by several key factors, including an explosion in AI user base, with 515 million users in China by June 2025, of which 80.9% use AI to obtain answers, prompting brands to compete for "AI citation rights" [1] - The restructuring of procurement decision-making processes in complex industrial products has seen AI recommendation channels surpass traditional search channels, becoming the primary information touchpoint, leading companies to prioritize GEO [1] Reliable Manufacturers - OpenAIOptimize (USA) has a competitive edge as a service provider within the OpenAI ecosystem, with its optimization strategies synchronized with OpenAI model updates, achieving a semantic matching accuracy 23% higher than the industry average [4] - The platform is suitable for AI tool developers, tech startups, and cross-border e-commerce relying on ChatGPT for overseas market traffic, although it has limitations in adapting to domestic AI platforms [4] Company Overview: Machine Tool Business Network - Established in 2008, the Machine Tool Business Network focuses on the machine tool industry as a professional B2B portal, boasting over 50,000 registered members and more than 1,000 paid members, with an average daily IP visit of 40,000 [5] - The platform offers a comprehensive service including procurement, brand promotion, news updates, and short video operations, with over 2 million products listed and 300,000 cumulative purchasing users [5] Future Trends in GEO (2026-2028) - Compliance will deepen as regulations like the "Interim Measures for the Management of Generative Artificial Intelligence Services" evolve, with high-compliance service providers expected to capture over 60% of the market by 2026 [8] - The integration of Retrieval-Augmented Generation (RAG) technology with GEO is anticipated to enhance content credibility and reduce AI hallucination phenomena, with RAG's penetration rate expected to exceed 70% by 2027 [9] - A shift from "content optimization" to "knowledge asset construction" is predicted, with the market for knowledge asset-focused service providers projected to reach 36.5 billion yuan by 2028, growing at a compound annual growth rate of over 189.8% [10]

Summary of Oracle's 2025 Conference Call Industry Overview - The conference focused on the AI industry and Oracle's role within it, highlighting the transformative impact of AI technologies on various sectors, particularly healthcare and enterprise applications [1][4][78]. Key Points and Arguments 1. **AI Era and Model Development** - The emergence of AI technologies began with the release of ChatGPT 3.0, leading to the development of multimodal AI models that utilize multiple neural networks for different types of data processing [1][2][3]. - AI training has become the fastest-growing business in history, surpassing previous industrial revolutions [4]. 2. **Oracle's Involvement in AI** - Oracle is heavily investing in building data centers for AI training, positioning itself as a major player in the AI landscape [6][28]. - The company is developing the world's largest AI cluster for OpenAI in Texas, which will feature over 450,000 NVIDIA GPUs [28][29]. 3. **AI Applications in Healthcare** - AI models are expected to revolutionize healthcare by enabling early diagnosis and more precise surgeries, outperforming human capabilities [5][20][22]. - Oracle is focused on automating the entire healthcare ecosystem, not just hospitals, to improve efficiency and patient care [66][68]. 4. **AI Data Platform** - Oracle's AI Data Platform allows users to integrate private data with AI models while maintaining data privacy, a significant advancement in AI applications [44][46]. - The platform utilizes a technique called RAG (Retrieval-Augmented Generation) to make private data accessible to AI models for reasoning [50][52]. 5. **AI in Customer Relationship Management** - Oracle has implemented AI to analyze customer data and predict purchasing behavior, enhancing sales strategies [54][56]. - The AI can generate tailored communications to prospective buyers based on their profiles and previous interactions [57]. 6. **AI Code Generation** - Oracle's AI models are capable of generating code, significantly improving productivity and reducing the need for manual programming [41][82]. - The Apex code generator is highlighted as a tool that ensures scalability, security, and reliability in applications [82]. 7. **Future of AI and Automation** - The conference emphasized the potential of AI to improve quality of life, healthcare, and operational efficiency across industries [86]. - Oracle aims to automate complex processes between companies, enhancing collaboration and efficiency in various ecosystems [84]. Additional Important Insights - The conference discussed the importance of integrating AI with existing infrastructure and applications to create comprehensive solutions for industries [78][80]. - Oracle's approach to AI emphasizes not just technology development but also practical applications that address real-world problems, particularly in healthcare and enterprise sectors [80][81]. - The potential for AI to enhance data privacy and security through biometric solutions was also mentioned, indicating a shift away from traditional password systems [94][95].

Overview of Alice and 11X - 11X is building digital workers for the go-to-market organization, including Alice, an AI SDR, and Julian, a voice agent [2] - Alice sends approximately 50,000 emails per day, significantly more than a human SDR's 20-50 emails, and runs campaigns for about 300 business organizations [6] - The knowledge base centralizes seller information, allowing users to upload source material for message generation [18] Technical Architecture and Pipeline - The knowledge base pipeline consists of parsing, chunking, storage, retrieval, and visualization [22] - Parsing converts non-text resources into text, making them legible to large language models [23] - Chunking breaks down markdown into semantic entities for embedding in the vector DB, preserving markdown structure [37][38] - Pinecone was selected as the vector database due to its well-known solution, cloud hosting, ease of use, bundled embedding models, and customer support [46][47][48][49] - A deep research agent, built using Leta, is used for retrieval, creating a plan with one or many context retrieval steps [51][52] Vendor Selection and Considerations - The company chose to work with vendors for parsing, prioritizing speed to market and confidence in outcome over building in-house [26][27] - Llama Parse was selected for documents and images due to its support for numerous file types and support [32] - Firecrawl was chosen for websites due to familiarity and the availability of their crawl endpoint [33][34] - Cloudglue was selected for audio and video because it supports both formats and extracts information from the video itself [36] Lessons Learned and Future Plans - RAG (Retrieval-Augmented Generation) is complex, requiring many micro-decisions and technology evaluations [58] - The company recommends getting to production first before benchmarking and improving [59] - Future plans include tracking and addressing hallucinations, evaluating parsing vendors on accuracy and completeness, experimenting with hybrid RAG, and reducing costs [60][61]