AI Optimization (AIO) for SEO & ChatGPT

FAQ

Find answers to common queries about how AIO revolutionizes Search Visibility.

Why are AI search and AI SEO important for my business, in addition to organic SEO ?

Consumers are increasingly using AI assistants rather than traditional search engines to research products. If your brand doesn't appear as a recommendation in AI responses, you risk losing high-intent customers to your competitors.

Who should use AI search and AI SEO?

Marketing, SEO, content, and product teams can all benefit—whether to monitor brand mentions, optimize content for AI search, or demonstrate ROI to leadership.

Why optimize your visibility on generative AI engines?

SEO is entering the generative era. As AI tools like ChatGPT and Gemini redefine search, visibility now depends on content that language models recognize as credible and structured.

Questions replace keywords. Content strategies built around real customer questions perform better in AI-driven discovery than those relying on traditional keyword tactics.

Authority and authenticity prevail. AI favors content from identifiable experts, supported by real data, multimedia context, and human expertise—signals that reinforce trust.

Why adapt your web architecture and content so AI brings you high-intent users directly?

AI-powered tools such as ChatGPT, Google Gemini, and Claude are transforming how users find and interact with information.

As a result, visitors now land directly on targeted pages (similar to detailed product pages), instead of going through the homepage or navigation menu.

While this disruption of the traditional journey disturbs the usual site flow, it also creates strategic opportunities for brands that know how to position themselves.

What is AIO ? - Definition, structure, and pillars

AIO (Artificial Intelligence Optimization) is the optimization for Artificial Intelligence.

It combines traditional SEO, web search, and artificial intelligence to optimize visibility in search engines and AI assistants. It's the evolution of SEO in the generative AI era. It refers to optimizing your content/presence to be well-ranked by generative AIs (ChatGPT, Perplexity, etc.).

AIO is therefore the optimization of digital visibility in AI (AI Visibility) and includes 2 complementary blocks:

SEO (Search Engine Optimization)

AI referencing

AIO is structured around 3 fundamental pillars and integrates:

Pillar 1: Optimization for answer engines (AEO: Answer Engine Optimization)

Pillars 2 and 3: Content structuring (SEO: Search Engine Optimization + GEO: Generative Engine Optimization)

The formula to understand AIO could be:

AIO = AEO + SEO/GEO

The goal of AIO is therefore to maximize "AI visibility" on AI engines and platforms, by combining SEO best practices, semantic structuring, and understanding AI indexing patterns.

How do I know if my company or brand is indexed on AI ?

Here's a checklist of 5 preliminary questions to ask before launching a positioning and referencing project. These questions are accompanied by rationale and examples to explain, illustrate, and help you realize the importance of each point addressed:

Is your content structured to be recognized as an authority source by language models?

The change is significant. Companies and brands must now meet the expectations of both traditional search engines AND language models.

It's no longer enough to simply appear in results. Content must be recognized by AI systems as a high-quality source worth citing.

Are your professional expertise and real experience evident and directly exploitable by AI systems?

A growing misconception is that AI platforms can be manipulated with auto-generated content. However, language models are already trained to evaluate content with greater rigor. Indicators such as tone, authority, and specificity will potentially have growing influence. These factors reflect the same principles that have long defined effective content marketing.

Thoughtful, in-depth content created by experienced professionals is more likely to stand out. For example, a video of a product leader discussing implementation challenges or lessons learned will carry more weight than anonymous text repeating generalities. Content demonstrating concrete experience generally performs better, not through polish, but through substance.

Is your content strategy aligned with the three essential pillars for AI performance (questions, expertise, multimedia context)?

As AI systems evolve, brands must take concrete steps to strengthen their visibility and authority.

The approaches contained in this FAQ offer a roadmap for building content that resonates with both human readers and language models.

Have you identified and documented the real questions your audience asks AI tools?

Humans (audiences, targets, customers) never enter isolated keywords into AI tools. They ask questions like "What's the best way to reduce compliance risk in financial services?" or "How do manufacturers streamline employee training?"

Creating content that directly answers these types of queries increases the likelihood of being surfaced in AI-generated summaries.

Examples: A bank could publish a detailed FAQ addressing customer concerns about digital security. A healthcare provider could produce a blog series around common patient questions, supported by case studies.

Structuring content this way makes it more conversational and more aligned with how people search today.

Does your content/multimedia strategy provide AI with the contextual signals needed to understand and cite you properly?

AI-driven search is no longer limited to text. Platforms like Google allow multimodal queries, where a user can upload a photo, combine it with a written question, and receive an enriched response.

To support this type of interaction, brands must pair their textual content with high-quality images, diagrams, and videos.

A home improvement retailer, for example, could provide step-by-step written instructions for installing a wall sconce, accompanied by annotated photos of each step.

These elements make content more useful for human readers while giving AI systems additional data points to interpret, index, and present in search results.

What is the objective of AIO ?

The AIO objective is to multiply the presence, citation, and use of your content by AIs and hybrid engines, beyond simple web ranking (SEO), to ensure maximum visibility in "Search 3.0". That's why SEO and AI referencing are complementary.

The goal of AIO is thus to maximize a brand or company's "AI visibility" on AI engines and platforms, by combining SEO best practices, semantic structuring, and understanding AI indexing patterns.

What is the structure (simplified text format) of AIO ?

Simplified text format:

text

AIO (Global Discipline) │ ├── OFF SEARCH → Pillar: OFF SEARCH (7 steps, 12 weeks) │ ├── WEB SEARCH → Pillar: WEB SEARCH (16 steps, 14 weeks) │ └── DEEP REASONING → Pillar: DEEP REASONING (8 steps, 4 weeks)

What is the AIO process schema ?

Simplified text format:

text

Step 1: User Query ↓ Step 2: Pass through LLM ↓ Step 3: Need for Web Search? ├── No → Use pre-training data └── Yes → Two possible strategies: ├── Standard web search (synthetic queries based on initial query) └── Deep web search (Deep Reasoning, enriched queries, cross-referenced with pre-training data) ↓ Step 4: RAG (Retrieval Augmented Generation) uses SERPs if web search ↓ Step 5: Response synthesis (synthesized answer)

What methodological sequence to apply for AIO? How to implement and execute the AIO Roadmap ?

The methodological sequence I recommend is based on 3 fundamental foundations:

OFF SEARCH: Pre-training Data. The objective is to build the model's internal knowledge base.

WEB SEARCH: Standard Web Search. The objective is to implement classic web search capabilities and basic RAG.

DEEP REASONING: Standard Web + Pre-training Data (setup, then operational). The objective is to activate deep reasoning combining both sources.

The logic is as follows:

Pre-training data creates the foundation
Standard web search adds data freshness, and
Deep reasoning intelligently orchestrates both for complex cases

What AIO methodological roadmap to use for optimizing AI search and AI referencing ?

Based on the recommended methodological sequence (1. Off Search, 2. Web Search, 3. Deep Reasoning), we developed our own methodological roadmap from our experience in organic (SEO) and paid (SEM) referencing, combined with results obtained for our clients on AI positioning and AI referencing.

Our AIO roadmap is structured in 4 stages:

Stage 1 - Analyze: AI Visibility Report, for an analysis of your brand's positioning

Stage 2 - Identify: Insights identification, segmentation and cross-analysis of data

Stage 3 - Amplify: Implementation* of your "AIO Roadmap" structured in action categories we perform for you >> 1. Off Search, 2. Web Search, 3. Deep Reasoning

Stage 4 - Accelerate: Referencing and placement monitoring to optimize your positioning continuously

*Zoom on implementation

Implementation (stage 3 "Amplify") is structured in action categories we perform for you on each of the 3 fundamental foundations:

Off Search (7 steps)
Web Search
Deep Reasoning

These action categories are divided into verification and production checkpoints, including:

Technical rationale
Detailed action
Validation

How to start and accelerate with AIO to optimize AI search and AI referencing ?

How to start with AIO?

We begin with an AI Visibility report, for an analysis of your brand's positioning (stage 1 - Analyze).

This analysis provides you with a report directly accessible in the platform and connected to your results: topics, prompts and queries, sources, competitors (positioning matrix), recommendations. The AI Visibility report allows us to identify insights and proceed with data segmentation and cross-analysis (stage 2 - Identify).

How to accelerate with AIO?

Once the AI Visibility report is completed, we transform Insights into improvements (stage 3 - Amplify).

Our methodology is structured in an "AIO Roadmap" of 6 action categories we perform for you:

Infrastructure preparation
Content structuring and enrichment
Formatting for AEO (Answer Engine Optimization)
Formatting for GEO visibility (Generative Engine Optimization)
Global technical optimization
Monitoring, testing and iteration

Once improvements are made and content is created and improved, we monitor referencing and placement to optimize your positioning continuously (stage 4 - Accelerate).

How do I track my AI referencing ?

How to measure my brand's performance in AI search?

You have a client dashboard that includes your visibility score, competitive rankings, and the sources that large language models use to generate their responses.

Can I see where and how my competitors are positioned?

Yes, absolutely. Competitive ranking features highlight exactly which prompts and keywords lead to competitor mentions in AI search. You also get recommendations on how to close the gap, which we can implement together.

Does the platform connect to traffic and conversions?

Yes, as a standalone search analysis, our platform is integrated for SEO (Search Engine Optimization) and AI referencing. You can thus connect visibility in traditional search and AI search to your pages and products to manage customer behavior, conversion, and revenue.

What are examples of outputs and visible results for AIO ?

SEO (Search Engine Optimization)

SERP ranking, Featured Snippet, sitelinks

AEO (Answer Engine Optimization)

Answer displayed in Google AI Overview, Bing, Copilot

GEO (Generative Engine Optimization)

AI source citation in ChatGPT Search, Gemini, Perplexity

What are the components of AIO ?

Lexicon and definitions (SEO, AEO, GEO).

SEO (Search Engine Optimization)

Traditional ranking on classic search engine results pages (SERP) of Google, Bing, etc.
Organic traffic from blue link positions

AEO (Answer Engine Optimization)

Content presence as instant answer or direct answer in Google AI Overview, Google SGE, Bing Chat, etc.
Answers cited at the top of conversational or voice queries (assistant, mobile, voice search)
AI summaries displaying the brand/link as source, often without user click but with high visibility

GEO (Generative Engine Optimization)

Citation of content or brand in responses generated by ChatGPT, Gemini, Perplexity, Claude, etc., sometimes without the site being #1 traditional SEO
Explicit mention ("According to [tipoussin.fr]...") in complex AI responses, guides or syntheses generated according to user context
Increased authority and brand recognition as reliable source for AIs, potentially generating qualified traffic or strengthening awareness even without immediate click

What is "AI visibility"? What differences between "AI visibility" and "AI Search" ?

"AI visibility" refers to a brand or content's ability to be present, cited, or recommended by generative AIs and next-generation search engines (ChatGPT, Gemini, Perplexity...) in their responses and direct results.

AIO (Artificial Intelligence Optimization) solutions allow you to track and improve your brand's presence in AI-generated responses on ChatGPT and Google AI Overview notably.

This goes beyond simple SEO ranking: it's about being mentioned, cited, or even summarized by AI in a conversation or synthesized response, whether through a link, summary, or brand mention.

"AI visibility" is the most generic, evocative term oriented toward brand positioning. AI visibility also implies tracking and measuring these appearances, allowing brands to adjust their strategy to remain visible in the face of gradual decline of classic organic links.

"AI search" is more specific, suited to a technical focus on search and user experience on the engine side. "AI search" refers to the search experience itself: it's the discipline aimed at optimizing one's presence in AI-powered search tool responses. This implies an intention focused on how AI platforms select and present information, but remains more technical and engine-centered.

What formats to structure for optimizing AIO ?

Main AI Summary (AI Overview):
An AI-generated block at the top of results, synthesizing multiple sources and providing a complete, "multi-line" or hierarchical response depending on the query.
Example: Multi-source synthetic response

Bulleted lists or steps:
Response presentation in numbered lists or bullets (practical steps, recipes, checklists) to facilitate user execution.
Example: Actionable, structured response

Structured paragraphs / explanation blocks:
Long paragraphs explaining a notion or concept in detail, sometimes citing sources integrated contextually.
Example: Long informative block, argued

Instant FAQ:
Related question proposals ("People also ask") redirected in AI's conversational style, often integrated after main block.
Example: AI-generated associated questions

Cited sources (inline or right side):
Contextual hyperlinks to sources used to generate response, integrated in text or as right panel on desktop.
Example: Citation in text or side panel

Interactive responses (AI Mode):
For certain users and countries, switch to "AI Mode": 100% generative search experience with conversational responses; possibility to ask follow-up questions and get in-depth results, ChatGPT-style.
Example: AI Mode (search by continuous exchange)

Illustrations and dynamic content:
In certain contexts: integration of AI-generated images, carousels or visualizations related to query.
Example: Generated illustration or diagram

YouTube video summaries:
Tests of explanation syntheses of YouTube videos, integrated in AIO zone.
Example: Integrated video synthesis

Step-by-step guides:
"How-to" or synthetic guides generated in visual formats or structured lists from multiple web sources.
Example: Multiformat AI how-to

This panel evolves rapidly, but these formats form the basis of AIO outputs observed since the 2025 deployment.

What are the technical specifications (size, MIME) for each AIO format ?

AI Summary / Generated Text / Paragraphs / FAQ

Format: Standard HTML text with HTML tag structuring (p, ul, li, h2, h3, etc.), sometimes encapsulated in Google Search proprietary interface blocks
MIME type: text/html or text/plain (for constrained output) depending on rendering
Size: Up to 8,192 tokens for responses, limited by Gemini/Vertex AI models. 1 token ≈ 4 English characters
Language: Multilingual supported (French, English, Spanish...)

Images and AI illustrations

Format: JPEG (image/jpeg), PNG (image/png), WEBP (image/webp), HEIC, HEIF less commonly
Size: Max: 7 MB per image. Common resolution for web display: between 400 x 400 px and 1200 x 800 px, resized for SERP
Quantity: Up to 3,000 images per Gemini technical prompt, but for Google display, rarely more than 3-5 visuals at once

Videos (summaries or YouTube carousels)

Format: MP4 (video/mp4), WebM (video/webm)
Max size (for processing): compressed video, 100 MB common for indexing, not displayed full in SERP
Sampling: 1 frame/second for contextual analysis by Gemini

Linked documents/files

PDF: application/pdf, max 50 MB, ≤ 1,000 pages
Source link (favicon, article): HTML box with favicon (PNG or ICO format), name, URL

Structured data

Integrated schemas: JSON-LD for rich snippets (application/ld+json), schema.org (HowTo, FAQ, Article, etc.)
FAQ, tables, lists: standard HTML structuring (ul, li, table, tr, td tags)

Note: These values are from Gemini/Vertex AI API; in Google Search, displayed outputs are optimized and compressed for web, typically less than 100 kb per image and base64 encoding or CDN.

What is an "LLM" ?

An LLM, or large language model, is a type of artificial intelligence trained on enormous amounts of textual data to understand and generate natural language.

It uses deep learning techniques, such as neural networks, to perform tasks such as answering questions, translating texts, summarizing documents, and writing content.

These models have become essential for many AI applications, from chatbots to virtual assistants.

What are the main characteristics of LLMs?

Natural language processing: LLMs are designed to understand the subtleties and complexities of human language, unlike simpler algorithms.

Deep learning: They rely on complex neural networks and are trained on massive text corpora (books, articles, web pages) to acquire knowledge and language skills.

Text generation: Their ability to generate realistic and contextually relevant text is one of their best-known functions.

Wide range of applications: LLMs are used for varied tasks such as translation, sentiment analysis, content creation, and automated customer support via chatbots.

How do LLMs work?

LLMs use architectures like "Transformers" that allow them to process large amounts of text in parallel, which accelerates training compared to older recurrent models.

The learning process includes a general pre-training phase, followed by finer tuning (fine-tuning) for specific tasks, allowing them to be used in various fields, such as law, medicine, or finance.

Why can an LLM answer without doing a "web search" ?

If the question relates to facts, reasoning, or content already massively covered in its "pre-training data," the AI can answer confidently without consulting the web.

Generic, timeless, or theoretical questions don't require real-time updating (e.g., definitions, general concepts, basic mathematics...).

Doing a search if the model "already knows" would needlessly slow the process, without improving response quality on stable facts.

How to train an LLM without "web search" ?

In practice, it's important to follow these steps and update them regularly based on feedback obtained, positioning results, and LLM user queries:

Build a broad and relevant corpus of examples covering target domains
Clean, deduplicate, and annotate this data if needed
Convert different formats into exploitable texts (normalization, vectorization)
Use these corpora to train the model with completion, Q&A, translation tasks, etc., until convergence

The model won't have post-training web access, but can effectively answer questions covered in this corpus.

Practical checklist (for this flow):

Pre-training corpus validated (breadth, quality, diversity)
Formats converted and harmonized (text, HTML...)
Cleaning and annotation pipeline (optional)
Training protocols configured (GPU, batches...)
Tasks adjusted according to use-cases (Q&A, completion...)
Coverage tests to verify "knowledge" areas
Data provenance documentation for traceability

Note: This approach is a synthesis of a process that can be much more complex, depending on objectives and needs specific to each brand and company. For any specific topic, [you can ask your questions here](contact link).

What are "Pre-training data", "Deep reasoning search", "Retrieval-Augmented Generation" (RAG) ?

What is "Pre-training data"?

This is the data corpus used during the LLM's initial training phase before any online interaction with users. It's a heterogeneous and massive set of documents: Web pages (Wikipedia, reference sites, public forums), Books and academic articles, Public structured datasets, Institutional documents, Various archives.

This includes articles, books, web pages, specialized datasets, etc., on which the model learns structures, facts, and reasoning before being deployed.

The model feeds on this "static" knowledge to respond if a web search isn't necessary or possible.

Data formats used are: Plain text, HTML, PDFs, JSON, CSV in some cases during preprocessing.

What is "Deep reasoning search"?

Here, the model goes beyond simple single query. If web search is activated, the "deep reasoning" option means the AI will perform chains of reasoning, plan intermediate queries, analyze different results, and cross-reference information (multi-step search with active reflection).

It's the opposite of simple snippet copy-paste: the LLM "thinks," refines searches, and adjusts its strategy in real-time for complex queries.

What is "RAG" and how does it use "SERPs"?

RAG stands for "Retrieval-Augmented Generation." This mechanism combines retrieval of external documents (e.g., content from pages listed in SERPs) and AI generation.

"RAG uses SERPs" means the LLM formulates queries, examines search engine results (SERPs), extracts essential text passages, then injects them as context in response generation. This ensures freshness, accuracy, and factual support of responses. It also allows aligning the response with reliable and up-to-date sources.

What is a "Synthesized answer"?

The AI assembles ("synthesizes") a final response by combining all explored information sources (pre-training, RAG/search results, reasoning).

The goal is a coherent, argued, and contextualized response, not a simple juxtaposition of text pieces.

How do "Off Search" and "Deep Reasoning Search" integrate into the AIO Roadmap ?

Both components integrate into the AIO process complementing "Standard Web Search" according to their positioning in the decision flow.

"Off Search" (Pre-training Data) constitutes the model's foundation, to be developed upstream or in parallel with the WEB SEARCH roadmap, as it feeds the LLM's basic knowledge.

"Deep Reasoning Search" combines this internal base with enriched web searches to handle complex queries requiring multi-step reasoning and in-depth exploration.