The Avant-Garde of Visibility: A Strategic Playbook for Corporate Presence in the Age of Generative AI

Section 1: The New Information Gatekeepers: Understanding the Knowledge System of Generative AI

The digital landscape is in the midst of a paradigm shift, driven by the emergence of large language models (LLMs) such as OpenAI's ChatGPT and Google's Gemini. These systems are rapidly evolving from novel tools to the primary interfaces through which billions of users access, aggregate, and interact with information.¹For companies, understanding the inner workings of these AI systems is no longer an academic exercise, but a strategic imperative. The future visibility of a brand, product, or service no longer depends solely on its placement in a list of blue links, but rather on whether it is integrated as a trusted source of information into the answers generated by the AI.

This fundamental section unpacks how LLMs acquire and process knowledge. It establishes the crucial distinction between their static, trained knowledge and their dynamic, real-time skills. This understanding forms the foundation upon which all subsequent strategies are built.

1.1 The two ways of thinking of an LLM: Distinguishing between static pre-training and dynamic real-time knowledge

At their core, LLMs like ChatGPT and Gemini operate with a dual knowledge system. The first is their fundamental, pre-trained knowledge base. This is built by processing massive amounts of text and code data, allowing the model to learn patterns, facts, logical relationships, and linguistic nuances.¹While this knowledge is immense, it is inherently static—it is a snapshot of the knowledge at the time the training data was collected. This explains why early versions of ChatGPT could not provide information about current events; their knowledge horizon was frozen in the past.²

The second, and far more critical to corporate strategy, is the models' ability to access and integrate external information in real time. This dynamic capability is enabled by mechanisms such as web search plugins and, most importantly, an architecture called Retrieval-Augmented Generation (RAG).¹This technology bridges the gap between the static knowledge of the model and the ever-changing digital world. It is the key mechanism that makes LLMs a continuously evolving source of information and thus the primary target for companies' visibility efforts.

1.2 The fundamental layer: How the open web shapes basic understanding

The composition of the massive datasets used for pre-training provides insight into the fundamental "worldview" of an LLM. These models learn from a corpus comprising terabytes of data, drawn from a variety of sources.²The main sources include:

• Publicly accessible websites:Large swathes of the open internet, captured through datasets such as Common Crawl and the refined RefinedWeb dataset, provide the broadest baseline.¹
• Wikipedia:Due to its structured, fact-based, and well-edited nature, Wikipedia is a high-quality source for training.¹
• Books:Licensed or public domain books offer depth, variety in writing style, and comprehensive knowledge on specific topics.¹
• Scientific papers and news articles:Licensed datasets from these areas provide specialized and up-to-date knowledge.¹
• Code-Repositories:Platforms like GitHub are crucial for teaching programming skills to models.¹

A crucial point is that the LLM doesn't store or copy this data verbatim. Instead, it learns statistical patterns and the relationships between words, phrases, and concepts by analyzing these vast amounts of data.¹The model adjusts its internal parameters, called "weights," to reflect these patterns. This means that for content to become part of this basic training, it must be publicly available, easily discoverable by search engine crawlers, and ideally licensed under permissive licenses like Creative Commons that allow data sharing.¹Content behind paywalls, registrations, or restrictive licenses is invisible to this phase of knowledge acquisition.

1.3 The action-oriented layer: A deep dive into Retrieval-Augmented Generation (RAG) as the key to future visibility

Retrieval-Augmented Generation (RAG) is perhaps the most significant technological development for companies seeking visibility into AI. It is the process that enables an LLM to "look up" information from an external, authoritative knowledge base before generating an answer. This overcomes the limitations of its static training data and mitigates problems such as outdated information or "hallucinations" (the fabrication of facts).⁷

The RAG workflow can be divided into several steps:

1. Retrieval:A user query triggers a search in an external data source. This source can be a curated database, a document repository, or, in the case of search engines, a web index.⁸
2. Augmentation:The most relevant retrieved information is then "inserted" into the prompt sent to the LLM, adding additional, up-to-date context to the user's original query.¹¹
3. Generation:The LLM uses this expanded prompt—the combination of the original question and the freshly retrieved facts—to generate a well-founded, accurate, and timely answer.⁸

This process allows LLMs to provide citations and reference current information, making them far more reliable for fact-based inquiries.⁷For companies, the implication is clear: The primary goal must be to become part of this "external knowledge base" that is queried by RAG systems. The strategic priority has shifted: It's no longer just about creating content, but about building a machine-readable, proprietary knowledge base. Companies no longer just publish information for human consumption; they curate data sets for ingestion by AI systems.

1.4 The Mechanics of Relevance: How Semantic Search and Vector Databases Determine Which Information is Retrieved

To understand how to become a source for RAG, you need to understand how the "retrieval" step works. It is not based on traditional keyword searching, but onsemantic search.¹³

The process of semantic search is highly sophisticated:

1. Vector embeddings:Both the external documents (e.g. web pages, articles, product descriptions) and the user’s query are converted into numerical representations, which are used asVector embeddingsbe referred to.¹²These vectors are not random numbers; they capture the
   Meaningand thecontextof the text in a high-dimensional space. Words and sentences with similar meanings are represented as vectors that are close to each other in this space.
2. Vector database:These vectors are stored in a specializedVector databasestored and indexed. These databases are optimized for extremely fast similarity searches in huge, high-dimensional datasets.¹³
3. Similarity search:The search process becomes a mathematical operation. The system converts the user's query into a vector and then searches the vector database for the document vectors that are "closest" to this query vector. "Closeness" is often measured using metrics such asCosine similaritywhich measures how similar the orientation of two vectors is, regardless of their length.¹²
4. Relevance ranking:The text chunks whose vectors have the highest semantic similarity to the query vector are classified as the most relevant and forwarded to the LLM to generate the response.¹⁵

An AI doesn't "read" a website in real time like a human. Instead, it queries a pre-indexed, vectorized version of that page's content. To be retrieved consistently, a company's information must therefore be structured, clear, and semantically rich. It must be optimized not only for human readability but also for efficient vectorization and rapid retrieval. A company's public website—its blogs, documentation, FAQs—thus becomes an external, queryable database for the world's AIs and thus a critical part of the company's data infrastructure.

Section 2: The AI-Powered SERP: Mastering Visibility in Google's AI Overviews

This section focuses on the most immediate and impactful manifestation of generative AI for most companies: Google's AI Overviews (formerly known as Search Generative Experience, or SGE). We analyze them as a case study of how RAG is being used on a global scale and what this means for digital visibility. The insights from this dominant ecosystem point the way for interacting with generative AI systems in general.

2.1 Anatomy of an AI Overview: The path from the complex query to the synthesized snapshot

The user experience with AI Overviews marks a significant departure from the traditional search engine results page (SERP). For complex, information-oriented, or ambiguous queries, Google generates a summary answer, a so-called "snapshot," which is prominently placed at the top of the results page, ahead of the organic and paid results.¹⁷

This snapshot answers the user's question directly by synthesizing insights from multiple high-quality sources and presenting them in natural, conversational language.³The format can vary and include step-by-step instructions, bulleted lists, or concise definitions. Crucially, this feature reduces the need for users to click through multiple web pages to find a comprehensive answer, which is especially useful for exploratory searches or comparisons.¹⁷

The system is also designed to be interactive. It allows for follow-up questions while maintaining the context of the original search and often suggests related topics or next steps.³This positions search as an "AI search assistant" rather than a pure results engine. The technology driving this is a sophisticated combination of Google's LLMs (such as PaLM 2 and Gemini) and a RAG architecture that leverages Google's massive web index as its knowledge base.³

2.2 The Source Selection Algorithm: Why E-E-A-T is the new cornerstone of digital authority

The most critical question for companies is:How does Google select the sources it cites in its AI Overviews?The answer lies in its established E-E-A-T framework:Experience, Expertise, Authoritativeness and Trustworthiness.¹⁷

Google faces the immense challenge of generating trustworthy AI answers at scale while avoiding hallucinations and misinformation.²¹Instead of developing a completely new system for assessing trust, it relies on its proven and refined E-E-A-T framework, which was originally developed to combat web spam and improve the quality of organic search results.¹⁷

Websites that demonstrate deep expertise, offer firsthand experience (e.g., through authentic product reviews or case studies), are widely recognized as authorities in their field, and enjoy general trust are far more likely to be cited as a source in AI Overviews.¹⁷This means that the signals Google has prioritized for years—like high-quality backlinks from relevant sites, clear author credits with verifiable credentials, and in-depth, helpful content—are now the ticket to being considered a reliable source for its generative AI.¹⁷

This development establishes a causal chain: High E-E-A-T signals lead to high organic rankings, which in turn dramatically increases the likelihood of being selected as a source for AI Overviews. E-E-A-T is thus no longer just a "ranking factor," but a fundamentalIngestion filterfor Google's generative models. This creates a powerful feedback loop: Companies that have invested in genuine authority and high-quality content will see their advantage amplified in the AI age. Conversely, those that rely on low-quality, keyword-driven tactics will become invisible to traditional search queries and AI-generated summaries alike. The cost of poor content quality has increased exponentially.

2.3 Data-based insights: Analysis of the most frequently cited sources and their common characteristics

The selection of sources for AI Overviews is not arbitrary. Analyses reveal clear patterns that allow for strategic conclusions:

• Correlation with organic rankings:In the vast majority of cases (91%), at least one of the sources cited in an AI Overview comes from the top 10 organic search results for the corresponding query.²²An average snapshot contains 8-11 links from 4 unique domains, indicating a consolidation of information from a small group of top sources.²²
• Request type:Longer, more complex, and more conversational queries are more likely to trigger an AI Overview.²²This underscores AI's focus on satisfying deep information needs that go beyond simple factual queries.
• Importance of user-generated content:Especially in e-commerce, the system shows a strong preference for content containing genuine user or expert reviews. Product comparison queries are almost exclusively answered with references to reviews, "Top X" lists, and articles with clear "pros and cons" sections, while standard product pages are almost never used as a source.²²This signals a clear preference for authentic, experience-based content over pure marketing material.
• Further sources:In addition to websites, Google's own products such as Google Maps/Local and YouTube are also common sources, which underscores the importance of a holistic presence in the Google ecosystem.²²

2.4 The Evolution of Optimization: From SEO to AIO

The transformation from an algorithmic search engine to an AI-powered answering system requires a corresponding evolution of optimization strategies. The following table compares traditional search engine optimization (SEO) with the new discipline of AI optimization (AIO) and serves as a concise summary of the required strategic change. It translates the abstract concepts discussed into a clear, comparative framework that executives can use to review their current digital strategy.

Section 3: The AIO Playbook: How to Become a Trusted Source for Generative AI

This section translates the analyses from the previous chapters into a concrete, two-part action plan for companies. Part A focuses on the content strategy optimized for an AI audience, while Part B covers the underlying technical infrastructure essential for assimilation by AI systems. Following this playbook is crucial for transforming from a passive website to an active, cited source in the new information ecosystem.

Part A: Content and semantic strategy for an AI audience

3.1 Beyond Keywords: Optimizing for Concepts, Intent, and Complex Queries

The era of pure keyword optimization is over. AI systems understand the intent and context behind a query, not just the exact words.¹⁷Companies must therefore shift from a keyword-centric to a topic-centric model. This requires creating comprehensive topic clusters that fully cover a subject area. Using synonyms, related concepts, and contextually relevant terms builds semantic depth, allowing AI to recognize the website's expertise.¹⁷

The focus should be on answering the long, conversational questions that users are increasingly asking AI assistants.²⁰Content must be structured to directly address queries like "How do I...", "What is the best way to...", and "Compare X to Y." Using tools to identify these questions, such as the "People Also Ask" boxes in Google Search or specialized tools, is crucial for creating content that meets users' real information needs.¹⁷ 

3.2 Structuring for Intake: The Crucial Role of Summaries, FAQs, and Scannable Layouts

AI models, especially in the context of RAG, must be able to extract key information quickly and efficiently. This requires content to be highly structured and easily scannable by machines. A dense, unstructured wall of text is just as unsuitable for AI as it is for human readers.

Actionable tactics include:

• Clear hierarchy:The use of logical and descriptive H2 and H3 headings to organize content into semantic sections.¹⁷
• Concise summaries:Providing a short, concise summary (a kind of "executive summary") at the beginning of longer articles. AI models prefer these concise key statements.¹⁷
• Lists and step-by-step instructions:The use of bulleted and numbered lists to present information in an easily digestible format. This is especially important for instructions and comparisons.¹⁷
• FAQ areas:The creation of dedicated FAQ (Frequently Asked Questions) sections on relevant pages, addressing common questions in a clear question-and-answer format, directly reflects the conversational nature of AI queries.¹⁷

3.3 The power of authenticity: Leveraging user-generated content, reviews, and first-hand experiences

AI Overviews' analysis has shown a strong preference for authentic, experience-based content.²²This is a direct result of the emphasis on the "experience" aspect in the E-E-A-T framework. For e-commerce and service companies, this means that generating and prominently displaying genuine customer reviews must be a top priority.

Content should demonstrate real-world experience and expertise. This can be achieved in several ways:

• Case studies and application examples:Detailed descriptions of how a product or service was used in practice to solve a problem.
• Expert authors:Content should be written by qualified authors whose credentials are clearly documented through author biographies and links to professional profiles (e.g., LinkedIn).¹⁷
• Summary of user feedback:Aggregating and summarizing user reviews into helpful "pros and cons" lists is an extremely effective tactic for being considered for comparison requests.²²

Part B: The technical mandate: Ensuring an AI-enabled digital infrastructure

A world-class content strategy is useless if technical barriers prevent AI from accessing and processing that content. Technical performance is no longer just a factor in user experience; it's a critical gateway for AI adoption.

3.4 The Sub-500ms Imperative: Why Page Speed and Server Response Time Are of Paramount Importance

Speed is a crucial factor. Data strongly suggests that websites with server response times above 500 milliseconds are significantly less likely to be cited in AI Overviews.²²This is likely because RAG systems access information in real time, and slow sources would slow down the response generation process.

This requires a relentless focus on technical optimization:

• Server configuration:Optimizing server hardware and software for fast response times.
• Caching:Aggressive use of caching mechanisms at various levels (server, CDN) to minimize loading times.²²
• Content Delivery Network (CDN):Use a CDN to bring content closer to the end user and reduce latency.

3.5 The Accessibility Principle: Minimizing JavaScript dependency to ensure content inclusion

One of the most critical technical insights is that AI retrieval systems strongly prefer content that exists in raw HTML code and largely ignore content that requires JavaScript (JS) to render.²²This poses a major challenge for modern websites that rely heavily on JS frameworks (such as React, Angular, or Vue.js) for dynamic and interactive features.

This creates a strategic tension between creating rich, interactive user experiences (often JS-heavy) and ensuring maximum machine readability for AI ingestion (preferably static HTML). A highly interactive product configurator may be great for users, but it's an opaque black box for an AI trying to understand product features for a comparison query.

Companies must ensure that their core, informative content is rendered server-side (server-side rendering) or available in a static form that is immediately accessible to crawlers without requiring JavaScript execution. The role of the technical SEO expert is thus evolving into that of an "AI ingestion architect," working closely with developers to bridge the gap between human-centered design and machine-centered accessibility.

3.6 Indexing and Crawlability: Basic Checks for AI Visibility

The fundamentals of technical SEO are more important than ever. If a page has crawling or indexing issues, it simply doesn't exist in the knowledge base that RAG systems query.²²Companies need to have a clean page architecture, the correct use of

Ensure a robots.txt file and regular crawlability audits to eliminate any barriers to inclusion. Ensure that all relevant content, including user-generated content such as comments and reviews, is indexable by search engines and not obscured by pagination or JS loading.²² 

Section 4: From passive supplier to active participation: Direct integration strategies

While AI optimization (AIO) aims to create a trustworthyThosefor general AI requests, this section examines proactive strategies to leverage a company’s unique data, services, and functionalitiesdirectlyinto AI ecosystems. This marks the transition from a passive to an active role, where a company not only provides information but becomes an integral tool that AI can use on behalf of the user. This approach requires a strategic decision that goes beyond marketing and affects the business model itself: Is the company's primary value the shared expertise (information) or the service provided (benefit)?

4.1 Way 1: The API-First Approach – Using the OpenAI and Gemini APIs

This path involves leveraging the powerful application programming interfaces (APIs) offered by OpenAI and Google to create customized applications or enhance existing workflows.²³Instead of waiting for AI to discover a company's public content, the company proactively uses AI as a component in its own systems.

The use cases are diverse and cross-industry:

• Sales & Marketing:Automate the creation of personalized sales materials, campaign briefs, and social media content. For example, a sales rep could use AI to create a customized proposal draft in seconds based on customer data and product information.²⁶
• Customer service:Developing intelligent agents that design personalized email responses to customer inquiries or power sophisticated chatbots that can understand and solve complex problems.²⁴
• Internal processes:Creating tools for the human resources department to create job descriptions, summarize meeting minutes, or analyze internal knowledge databases to increase employee efficiency.²⁶

This approach offers the highest degree of control and customization, allowing a company to deeply integrate AI into its proprietary systems and data, creating unique, difficult-to-copy competitive advantages.

4.2 Way 2: The advantage of custom GPTs – development of specialized AI agents

This strategy goes beyond pure API usage and involves creating a specialized version of a GPT that is trained or fine-tuned on a company's own proprietary data.²⁸This is comparable to hiring and training a new employee who absorbs all of the company's internal knowledge.²⁹

The main advantages are:

• Specialization & Accuracy:A custom GPT trained on internal documents, product manuals, past customer interactions, and legal frameworks delivers far more accurate and relevant answers than a generic model. It understands the specific terminology and nuances of the business area.²⁸
• Internal efficiency:Such an AI agent can act as an internal "super-expert." Employees can use it to quickly find information in dense knowledge databases, assist developers with programming, or automate the creation of standardized reports and work instructions.²⁹
• Improved customer experience:Deployed externally, a custom GPT can serve as a highly competent, customer-focused chatbot that understands the company's products and services down to the smallest detail, thus providing high-quality, personalized advice.³⁰

4.3 Way 3: The plugin ecosystem – direct connection of services to user workflows

Plugins allow a company to make its services available directly within the user interfaces of platforms such as ChatGPT.³²If a user's request is relevant to a plugin's capability, the LLM may decide to call that plugin's API to handle the request.

This is an extremely effective method for gaining visibility at the exact moment it's needed. For example, a travel company's plugin could be called when a user asks ChatGPT to plan a trip. The plugin could then retrieve live data on flights and hotels and allow the user to book directly within the chat. The company thus becomes a service rather than a source of information.

The development process essentially comprises three steps:

1. Creating an API:The company must provide an API for its service.
2. Creating a manifest file:A file that describes to the LLM what the plugin does, how it is called, and what authentication methods are required.³³
3. Creating an OpenAPI specification:A detailed technical description of the API endpoints that the LLM can use.³⁴

Through this approach, a company's service becomes a "tool" that AI can use on behalf of the user, leading to direct transactions and a strong brand presence within the AI ecosystem.

Choosing between these three paths isn't a purely technical decision, but a fundamental business strategy one. A media company whose value lies in its expertise and content might focus 90% of its resources on AIO. A SaaS company whose value lies in its functionality might invest 90% in API and plugin development. For many, a hybrid approach will be necessary, but the strategic distinction is crucial for effective resource allocation and positioning in the AI-native market of the future.

Section 5: Strategic Imperatives and the Future of AI-Driven Visibility

This concluding section synthesizes the report's findings into an overarching strategic framework. It provides guidance for business decisions, promotes the necessary organizational adaptation, and looks ahead to the next evolutionary stage of AI to future-proof companies.

5.1 A framework for action: Prioritizing AIO, API, and plugin strategies

The decision as to which of the presented strategies to pursue depends on a company's specific goals, resources, and business model. To help executives prioritize, the following table serves as a comparative analysis of the strategic paths. It evaluates the options based on key business metrics and enables a data-driven approach focused on goals such as brand awareness, lead generation, customer retention, or operational efficiency.

This matrix serves as a boardroom-ready summary, allowing a leadership team to quickly compare the costs, benefits, and requirements of each path. It transforms the report from a purely informational document into a practical decision-making tool, significantly increasing its strategic value.

5.2 Organizational convergence: Promoting collaboration between marketing, content, and IT

Gaining visibility in the AI age is not an isolated function of marketing or IT. As the AIO playbook demonstrates, a brilliant content strategy (marketing) is worthless if the technical infrastructure (IT) prevents AI from absorbing it. Likewise, a technically perfect website without high-quality, E-E-A-T-compliant content is irrelevant to AI systems.

Success therefore requires breaking down traditional departmental silos and forming cross-functional AI Visibility teams. Within these teams, content creators, SEO specialists, and web developers must work closely together. Their shared goal is to meet the dual needs of human users and AI crawlers. This organizational convergence is not an option, but a prerequisite for success in the new digital landscape.

5.3 The next stage: Preparing for multimodality, proactive AI agents and a post-search world

The strategies described in this report are fundamental, but technology is evolving rapidly. A forward-thinking organization must prepare for the next waves of innovation:

• Multimodality:Models like Gemini are multimodal by design, meaning they can seamlessly process text, images, audio, and video.³⁶In the future, it will be crucial for visibility to optimize all media types for AI. This includes providing descriptive alt text for images, transcripts for videos, and structured data for all media formats. For example, a company offering an instructional video must ensure its content is also available in machine-readable text so that AI can understand it and summarize it in a single response.
• Proactive Agent:The future of AI lies not only in answering user questions, but in proactive AI agents that autonomously perform tasks on behalf of users.⁹A user could tell their agent: "Book me a flight to Berlin next week for under €200 and a hotel near the conference center." The agent would then independently interact with APIs and plugins from various providers to complete the task. The API and plugin strategies described in Section 4 are the first step toward making a company's services "accessible" to these future agents.

The ultimate goal for companies is to transform from a mere "destination" on the web to an indispensable "service hub" in a decentralized, AI-driven network of information and capabilities. Those who lay the foundations of AIO today while simultaneously investing in direct integration paths will not only be visible in the current generation of AI systems but will also form the vanguard shaping interactions in the AI-native world of tomorrow.

Works cited

1. How LLMs Source Content for Optimum Visibility - The Bubble Co., accessed on August 18, 2025, https://thebubbleco.com.au/how-llms-source-content/
2. Google Gemini vs ChatGPT: Which LLM is Right for You? | by The Tech Platform | Medium, accessed on August 18, 2025, https://thetechplatform.medium.com/google-gemini-vs-chatgpt-which-llm-is-right-for-you-d4f555072372
3. Google's Search Generative Experience (SGE) Guide - Meaning, Benefits, AI Tools Comparison - PRNEWS.io, accessed on August 18, 2025, https://prnews.io/blog/googles-search-generative-experience-sge.html
4. LLM Training Data: The 8 Main Public Data Sources - Oxylabs, accessed on August 18, 2025, https://oxylabs.io/blog/llm-training-data
5. Open-Sourced Training Datasets for Large Language Models (LLMs) - Kili Technology, accessed on August 18, 2025, https://kili-technology.com/large-language-models-llms/9-open-sourced-datasets-for-training-large-language-models
6. How ChatGPT and our foundation models are developed - OpenAI Help Center, accessed on August 18, 2025, https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-language-models-are-developed
7. What is Retrieval-Augmented Generation (RAG)? - Google Cloud, accessed on August 18, 2025, https://cloud.google.com/use-cases/retrieval-augmented-generation
8. What is RAG? - Retrieval-Augmented Generation AI Explained - AWS, accessed on August 18, 2025,https://aws.amazon.com/what-is/retrieval-augmented-generation/
9. What is retrieval augmented generation (RAG) [examples included] - SuperAnnotate, accessed on August 18, 2025, https://www.superannotate.com/blog/rag-explained
10. What is Retrieval Augmented Generation (RAG)? | A Comprehensive RAG Guide - Elastic, accessed on August 18, 2025, https://www.elastic.co/what-is/retrieval-augmented-generation
11. Retrieval Augmented Generation (RAG) in Azure AI Search - Microsoft Learn, accessed on August 18, 2025, https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview
12. Semantic Search and RAG: Key Differences and Use Cases - Signity Solutions, accessed on August 18, 2025, https://www.signitysolutions.com/blog/semantic-search-and-rag
13. Retrieval Augmented Generation (RAG) and Semantic Search for GPTs, accessed on August 18, 2025, https://help.openai.com/en/articles/8868588-retrieval-augmented-generation-rag-and-semantic-search-for-gpts
14. Vector Search Embeddings and Retrieval-Augmented Generation - Perficient Blogs, accessed on August 18, 2025, https://blogs.perficient.com/2025/07/16/vector-search-embeddings-for-rag/
15. Embedding Technologies in RAG: Vector Embeddings and Semantic Search Techniques | by Subash Palvel, accessed on August 18, 2025, https://subashpalvel.medium.com/embedding-technologies-in-rag-vector-embeddings-and-semantic-search-techniques-dddc3b6e78f0
16. Semantic Search and RAG - a Powerful Combination by Seth Carney - EQengineered, accessed on August 18, 2025, https://www.eqengineered.com/insights/semantic-search-and-rag-a-powerful-combination
17. Google SGE: What is Search Generative Experience?, accessed on August 18, 2025, https://www.wsiworld.com/blog/what-is-sge-search-generative-experience-and-how-can-it-impact-your-business
18. An overview of Google's Search Generative Experience (SGE) - Aqueous Digital, accessed on August 18, 2025, https://www.aqueous-digital.co.uk/articles/an-overview-of-googles-search-generative-experience-sge/
19. AI Overviews Explained: The Ultimate Guide to Google's Search Generative Experience (SGE) | ResultFirst, accessed on August 18, 2025, https://www.resultfirst.com/blog/ai-seo/ai-overviews-explained-the-ultimate-guide-to-googles-search-generative-experience-sge/
20. Google SGE: Google Search Generative Experience Explained - Semrush, accessed on August 18, 2025, https://www.semrush.com/blog/google-sge/
21. The top 100 tech media sources that matter for AI search, accessed on August 18, 2025, https://www.rlyl.com/uk/tech-media-sources-for-chatgpt-2/
22. Everything we know about Google SGE (Search Generative ... - Reddit, accessed on August 18, 2025, https://www.reddit.com/r/SEO/comments/18zyqvr/everything_we_know_about_google_sge_search/
23. OpenAI compatibility | Gemini API | Google AI for Developers, accessed on August 18, 2025, https://ai.google.dev/gemini-api/docs/openai
24. Your Ultimate Guide to Gemini API vs. OpenAI API: Making the Right Choice, accessed on August 18, 2025, https://www.aibusinessasia.com/en/p/your-ultimate-guide-to-gemini-api-vs-openai-api-making-the-right-choice/
25. OpenAI for Business, accessed on August 18, 2025, https://openai.com/business/
26. AI Tools for Business | Google Workspace, accessed on August 18, 2025, https://workspace.google.com/solutions/ai/
27. Real-world gen AI use cases from the world's leading organizations | Google Cloud Blog, accessed on August 18, 2025, https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
28. What is a Custom GPT? | Key Features and Best Practices for Custom GPTs | Lumenalta, accessed on August 18, 2025, https://lumenalta.com/insights/what-is-a-custom-gpt-key-features-benefits
29. A Custom GPT Can Be Whatever Your Company Needs It to Be - ProfitOptics, accessed on August 18, 2025, https://www.profitoptics.com/blog/a-custom-gpt-can-be-whatever-your-company-needs-it-to-be
30. Top 5 Benefits Of Building ChatGPT With Your Business Content - CustomGPT.ai, accessed on August 18, 2025, https://customgpt.ai/top-5-benefits-of-building-your-own-chatbot-for-your-business/
31. 5 Key Benefits of Custom ChatGPT for Companies - Relevance AI, accessed on August 18, 2025, https://relevanceai.com/blog/5-key-benefits-of-custom-chatgpt-for-companies
32. ChatGPT Plugin Development: Features and Benefits for Business - Agente, accessed on August 18, 2025, https://agentestudio.com/blog/chatgpt-plugin-development
33. ChatGPT plugins - OpenAI, accessed on August 18, 2025, https://openai.com/index/chatgpt-plugins/
34. How to Create Your Own ChatGPT Plugin - GeeksforGeeks, accessed on August 18, 2025, https://www.geeksforgeeks.org/blogs/how-to-create-chatgpt-plugin/
35. How to make a ChatGPT plugin - Pluralsight, accessed on August 18, 2025, https://www.pluralsight.com/resources/blog/software-development/how-make-chatgpt-plugin
36. I Tested Gemini vs. ChatGPT and Found the Clear Winner - G2 Learning Hub, accessed on August 18, 2025, https://learn.g2.com/gemini-vs-chatgpt
37. An Introduction To LLMs Like ChatGPT, DeepSeek and Gemini - YouTube, accessed on August 18, 2025, https://www.youtube.com/watch?v=svEQQ_dkjLs
38. Large language model - Wikipedia, accessed on August 18, 2025, https://en.wikipedia.org/wiki/Large_language_model
39. Is it still worth it to develop and maintain GPTs? - GPT builders - OpenAI Community Forum, accessed on August 18, 2025, https://community.openai.com/t/is-it-still-worth-it-to-develop-and-maintain-gpts/935193