A complete guide to AI search platforms. How do they work and how to start generating traffic?

AI search is a new generation of user interaction with information on the web. Unlike traditional search engines (like Google), which return a list of links to crawled pages, AI search engines (e.g., ChatGPT, Perplexity, Gemini) generate direct answers, summaries, and recommendations based on real-time content analysis from multiple sources.

This is a technological breakthrough for several reasons:

• AI interprets the user’s intent, not just keyword matching.
• It operates based on conversational queries, close to real-life conversations.
• It reduces the need to click on links – the user receives the essence of the answer immediately.
• Search becomes more personalized and contextual.

For SEO specialists, this means a fundamental shift in the process of reaching the user. It is no longer enough to “rank high” – you need to be cited, referenced, and present in AI-generated responses.

The difference between classic SEO and AI-powered search

In practice, this means that traditional SEO is still the foundation, but it is not enough to generate traffic from AI search engines.

What are AI search platforms?

AI search platforms are tools based on large language models (LLMs) that respond to user queries in a conversational manner, providing direct answers, summaries, and recommendations. Unlike classic search engines (Google, Bing), which return results as a list of links, AI platforms aggregate and synthesize information to deliver a ready-made solution to the user.

Key features of AI Search platforms:

• Understanding user context and intent.
• Generative answers in the form of a conversation, rather than a collection of links.
• Citing sources and reference materials.
• Dynamic real-time search (for selected tools like Perplexity, Copilot).

From an SEO specialist’s perspective, this significantly impacts the point of focus: what matters is whether the AI “knows” your brand and recognizes it as a credible source, not just whether you occupy the #1 spot on Google.

Examples of AI Search platforms

ChatGPT (OpenAI)

• Not a classic search engine, but an LLM with a web-browsing mode.
• Generates answers based on training data and up-to-date information (depending on version and configuration).
• Used as a personal assistant for complex queries, research, and content creation.
• ChatGPT browsing increasingly cites specific sources, driving traffic to expert sites.

Perplexity AI

• A typical AI search engine with source citations.
• Provides summaries with references to websites, articles, forums (Reddit), and reviews (G2, Trustpilot).
• High-quality traffic users click on sources with the intention of exploring topics further.
• An example of a platform that actively increases referral traffic for well-cited brands.

Google Gemini (Search Generative Experience – SGE)

• Integrates AI Overviews at the top of search results.
• Changes the structure of the SERP: a generative AI response appears first, followed by traditional links.
• Limits the visibility of classic organic results.
• For brands, this is an opportunity to appear in AI answers (even without being #1 on Google).

Microsoft Copilot (Bing)

• Integration of GPT-4 with the Bing search engine.
• Answers questions, summarizes content, cites sources.
• Drives traffic to sites considered credible.
• Also used in productivity tools (Office, Teams).

Other examples

• Claude (Anthropic) – a conversational AI with an emphasis on safety and ethics.
• LLaMa (Meta) – an open-source AI model for research and commercial use.
• Grok (xAI) – Elon Musk’s AI integrated with the X platform (Twitter).

Each of these platforms impacts organic traffic in different ways, but all create a new visibility channel for brands that can adapt to AI Search Optimization.

In the context of SEO:

• AI search engines are a new visibility channel (alongside organic, paid, and social).
• Traffic from AI Search is more engaged but requires a different approach than traditional SEO.

How do AI search engines work?

The core of AI search engines is large language models (LLMs), such as GPT-4 (OpenAI), Claude (Anthropic), or Gemini (Google). These models are trained on vast datasets of textual content: books, articles, forums, technical documentation, source code, and web content.

Crucially, AI does not “search” the internet in real time in the way Google does. Instead, it generates responses predictively based on patterns observed in its training data.

• Mentions are key: it’s not backlinks, but the frequency and quality of brand mentions in authoritative sources (forums, expert blogs, case studies) that determine whether AI considers a source valuable.

AI search engines understand the semantics and context of queries, not just keyword matching. Therefore, phrases like “what are the best A/B testing platforms for SMEs” are analyzed with the intent of the question in mind, not just the keywords.

Access to real-time data

Many modern AI search platforms (Perplexity, Copilot, Gemini SGE) also utilize up-to-date data from the web to enrich their responses with fresh information.

Sources used in real-time:

• Reddit, Quora – authentic user discussions.
• Industry blogs and expert portals.
• Review platforms (G2, Capterra, Trustpilot).
• Thematic categories and evergreen articles.
• YouTube transcripts, multimedia content (Perplexity and Gemini often cite videos).

In practice, this means that your content, even if it’s not in the “Top 3 on Google,” can still be cited by AI if it is well-written, up-to-date, and present in valuable sources.

How AI search engines generate responses

The process of generating answers by AI search engines differs from traditional SEO ranking:

• Natural language query analysis – AI recognizes the user’s intent and the context of the question.
• Prediction of the most likely answers based on training data.
• Triangulation of information from current sources – verifying and updating the response.
• Synthesizing results into a summary or recommendation – AI creates a coherent and readable answer.
• Citing sources (mainly Perplexity, Copilot) – providing links to reference materials.

Example:
For the query “best A/B testing tools for SaaS”, an AI search engine will not only provide a list of tools but also offer context (e.g., SMB vs. enterprise budget), and its recommendations will be based on:

• Case studies,
• Comparisons published in industry sources,
• Mentions in discussions (e.g., Reddit, LinkedIn),
• User reviews (Trustpilot).

Unlike Google, the “pure position in the SERP” does not matter here. What counts is the substantive quality of your content and its presence in the AI knowledge ecosystem.

Practical SEO – what does it mean in practice?

• Traditional SEO alone (backlinks, keyword optimization) will not guarantee visibility in AI Search.
• The key is building contextual authority (mentions in valuable places).
• Your content must be AI-friendly – well-structured, clear, and free from unnecessary “marketing fluff.”
• FAQs, case studies, how-to guides, and listicles with real data are the types of content most often used by AI in responses.

AI search vs traditional SEO – key differences

1. Operating mechanism (algorithm vs generative AI)

In traditional SEO, search engines (Google, Bing) use ranking algorithms, analyzing hundreds of factors: backlinks, content quality, domain authority, behavioral signals (CTR, bounce rate). The result is a list of links (SERP) ordered by predicted usefulness.

In AI Search (ChatGPT, Perplexity, Gemini):

• There is no ranking of pages — instead, answers are generated directly.
• Language models (LLMs) predict the best answer based on data and query context.
• AI understands user intent, not just keywords.
• In the case of Perplexity or Copilot, responses are enriched with up-to-date sources.

In practice: Google returns a list of links. AI Search provides a ready-made answer.

2. Authority Signals (Mentions vs. Backlinks)

In traditional SEO, backlinks (quantity, quality, domain authority) are the foundation of ranking.

In AI Search:

• Mentions matter – references to a brand, product, or expert in authoritative contexts.
• AI gathers data from forums (Reddit, Quora), blogs, LinkedIn, and review platforms (G2, Capterra).
• Authority is built through quotes and contextual appearances of a brand in sources recognized by AI models.

In short: Google rewards backlinks. AI rewards mentions and the quality of context.

3. AI Search and the Buyer Journey

 The growing importance of AI Search is changing user behavior:

• AI Overviews (Google SGE) push traditional organic results further down.
• CTR for positions 1–3 drops by as much as 30–40% for top-funnel queries.
• AI provides ready-made answers — often without any link clicks (“zero-click search”).

The key takeaway:
For brands cited by AI, this means higher-quality traffic, as users reaching them are already “endorsed” by AI.

4. Practical Implications for SEO

• AI Search is a new, parallel traffic channel alongside traditional SEO.
• Visibility depends on a brand’s presence in the data feeding AI (mentions, context, content freshness).
• On-site SEO (structure, schema, crawlability) still matters – it helps AI recognize your website.

The key shift:
You’re no longer optimizing just to rank in Google – you also need to rank within AI language models.

AI search and the new buyer journey model

With the rise of AI Search, the traditional customer journey is evolving:

• Traditional SEO: Google → links → exploration → decision.
• AI Search: one query → generative answer with recommendations → decision.

How AI Search shortens the buyer journey:

• Instant answers: no need to browse through 10 links.
• Mentions as “endorsements”: a brand cited by AI gains expert recommendation status.
• TOFU → BOFU in one step: the user immediately finds content addressing their final need.

Example of the new journey:

• A query to Perplexity: “Best CRMs for small businesses 2025.”
• AI generates an answer citing sources (e.g., Insightland, Pipedrive).
• The user clicks on your CRM comparison page.
• Quick decision – AI “recommended it,” Google only verifies.

New Buyer Journey Logic

Why is traffic from AI search crucial for your business?

AI Search is emerging as a new traffic acquisition channel. Already today, it is becoming fully established, and the predictions are that by the end of 2027, it will significantly impact the business results of companies operating online. This is not an “experimental trend” or a “curiosity,” but a new layer of the search ecosystem – one that will not replace Google, but competitively captures a share of user intent.

The quality of AI search traffic

In terms of engagements and conversions, it’s worth mentioning that unlike traditional organic traffic:

• Traffic from AI Search is more intentional – users reach you because they have already received an “endorsement” from AI.
• Average time on page and conversion rates (newsletter sign-ups, lead magnets) from AI Search outperform organic traffic from Google.
• AI-driven traffic often comes from long-tail, contextual queries, which better qualify leads for the BOFU (Bottom of Funnel) stage.
• This is not “cold traffic” from generic keywords – it’s high-quality traffic driving MQLs/SQLs.

Growth of AI search traffic and popularity in 2024 and 2025

• Perplexity AI – This innovative AI search engine has seen significant interest from investors, aiming to raise 500 million PLN, which could boost its valuation to at least 14 billionPLN, compared to 9 billion PLN in December 2024. (1)
• ChatGPT Search – OpenAI has introduced an online search feature to ChatGPT, combining AI-generated responses with real-time internet data. This directly challenges Google’s dominance in online search. (2)
• Growth in Generative AI Services Traffic – Monthly traffic to generative AI services increased by 251% year-over-year between February 1, 2024, and March 1, 2025. (3)
• Growth in AI Chatbot Traffic – A recent study showed that AI chatbots experienced an impressive 80.92% year-over-year traffic increase from April 2024 to March 2025, reaching a total of 55.2 billion visits. (4)

Why should SMBs, SaaS, e-commerce, and business owners take advantage of this?

Small and Medium-Sized Businesses (SMBs):

• Opportunity to outperform larger players in AI Search through high-quality content and flexible strategies.
• Effective reach to niche queries and local keywords.

SaaS / B2B software houses:

• AI Search converts exceptionally well from TOFU (top of the funnel) to BOFU (bottom of the funnel).
• Being present in AI recommendations shortens the customer decision-making process (e.g., choosing a tool).

E-commerce:

• Products and categories featured in AI Overviews (Gemini, Copilot) increase visibility and direct sales.
• AI cites sources from buying guides and “best for X” rankings.

Business owners and management:

• A new traffic source with high ROI.
• Building expert brand positioning in a rapidly growing channel.

Key takeaways for SEO specialists and marketers

• Ignoring AI Search traffic risks losing valuable reach.
• Strategy adaptation is essential: AISO is no longer optional – it’s a necessity.
• The future of SEO lies in integrating AI Search Optimization with traditional SEO and content marketing.

AI Search Optimization (AISO) – how to start generating traffic

AI Search Optimization (AISO) is the process of optimizing content and brand presence in the context of AI search engines. It differs from traditional SEO – it’s no longer about your position in the SERP, but whether your brand and content are cited and used by AI models in their responses to user queries.

Here are 4 key areas to start with:

1. Content optimization for AI search

• Conversational language and natural phrases – AI analyzes queries phrased as questions, not SEO keyword strings. Your content should answer real user questions.
  Example: instead of “CRM systems Poland 2025” → “What is the best CRM for small businesses in Poland in 2025?”
  
• Content structure for AI:
  
  • H2-H3 headings aligned with user intent.
  • Bullet points and numbered lists (AI prefers easily parsable content).
  • Comparison tables, checklists.
  • Citing sources, statistics, and case studies.
    
• Semantics over keywords – AI looks for context, not keyword density. Answer with full sentences, showing topic understanding.

Practical rule:
Write content that AI could quote as a model, authoritative answer for users.

2. Building brand mentions

AI recommends brands it “knows” from context:

• Active participation in industry discussions on Reddit, Quora, LinkedIn.
• Being cited in rankings, comparisons, and guest articles.
• Reviews on G2, Capterra, Trustpilot – crucial for platforms like Perplexity and Gemini.

Mentions > Backlinks in the world of AI Search.
Your brand must “exist in the data” used by language models.

Pro tip:
Regularly monitor where your brand is mentioned – and identify where it’s missing.

3. Technical Optimization for AI Crawlers

Although AI doesn’t operate exactly like Googlebot, website crawlability and technical structure are crucial:

• Loading speed and mobile-first – AI prefers pages that are easy to parse.
• Schema.org / structured data – use formats like FAQPage, HowTo, Product, Review.
• XML sitemaps and internal linking – help AI discover related content.
• Accessibility for GPTBot, ClaudeBot, Google-Extended – ensure proper robots.txt configuration.

Guiding principle:
Your website must be easy to read for both humans and machines – with clear structure, semantics, and well-organized data.

4. Video and Multimedia as Citation Sources

Video is becoming a key source for AI Search:

• YouTube is regularly cited by Perplexity and Gemini.
• Tutorials, reviews, and tool comparisons are favored by AI.

Video optimization for AI:

• Transcripts divided into clear chapters.
• Titles and descriptions phrased as natural-language questions.
• VideoObject schema for embedded videos.
• Videos embedded on landing pages and help center articles.

Example:
“How to implement A/B testing in a small business?” – Perplexity will rank a video with a clear process description and transcript higher than plain text content.

Practical SEO: what does this mean in practice?

AISO is the combination of expert content, PR activities, and technical SEO within the context of AI Search. The key is contextual quality: AI doesn’t need your link, but rather a quote, mention, or your presence in valuable discourse.

Why is measuring AI search traffic more challenging than traditional SEO?

Unlike traditional SEO, where we have precise data from Google Search Console, measuring AISO results faces several barriers:

• Lack of standardized AI Search traffic data in GA4 and Google Search Console.
  
• Traffic from Perplexity, ChatGPT, or Copilot may appear as “Direct” or “Referral” without clear attribution.
  
• Often, this is so-called dark traffic – we know it’s coming, but it’s harder to track accurately.
  

This requires using alternative and complementary monitoring methods.

Key metrics for monitoring AISO

Referral traffic from AI domains
Track traffic from domains such as:

• chat.openai.com (ChatGPT browsing)
• perplexity.ai
• copilot.microsoft.com
• claude.ai

In GA4, configure custom channel groups to capture traffic from these sources separately.

Branded search volume (Google, Bing)
An increase in branded search volume (“Insightland pricing,” “Insightland AI SEO”) is often a result of first contact with your brand through AI Search. Monitor this data in Google Search Console.

Lead forms with self-reported attribution
Add a question like: “How did you hear about our brand?” with an option: “AI Search (ChatGPT, Perplexity, Gemini).” This is currently the most reliable way to measure leads from AI Search.

Visibility in AI overviews and GenAI results

• Manual analysis: test queries in ChatGPT, Perplexity, Gemini.
• Document brand presence (screenshots, change tracking).
• SEO tools (Ahrefs, Semrush) are beginning to track appearances in AI Overviews.
  

New Backlinks from AI-surfaced Content
Some AI tools (e.g., Perplexity) link directly to sources. Track new backlinks using Ahrefs/Majestic in the context of AI citations.

Emerging metrics – new performance indicators

• AI Search Visibility Score – a metric available in new platforms (e.g., Dark Visitors), showing how often your brand appears in AI-generated responses.
• Content Gap Analysis AI – analysis of topics where AI does not mention your brand, even though it should.
• AI Misrepresentation Monitoring – auditing how AI describes your brand (errors, hallucinations).

Data interpretation – what are we really measuring?

• AISO is a qualitative channel, not a quantitative one – numbers will be smaller than Google Organic, but with better conversion rates.
• What matters is traffic quality (time on site, CTR, conversions), not just the volume of visits.
• A key focus is on a growing trend over the long term (3–6 months).

SEO for AI Search requires a shift in analytical mindset: It’s no longer about “rankings,” but about contextual visibility and authoritative citability.

Key takeaways for SEO and marketing specialists

• Without regular monitoring of AI Search traffic, you won’t be able to measure AISO effectiveness.
• You need to integrate data from multiple sources (GA4, GSC, SEO tools, manual AI checks).
• New metrics (AI visibility, mentions, misrepresentation correction) are becoming just as important as “Google rankings.”

For more technical details on measuring traffic coming form AI platforms, check out our article on Measuring data from generative chatbots.

Best AISO practices for 2025 – company checklist

1. Content optimization for AI search (format, structure, intent)

• Create content that answers specific user questions – avoid phrases like “offer” and focus on questions such as: “How to choose the best tool for…”, “What is AI Search Optimization?”
• Use a clear H1-H3 structure:
  
  • H1 – answers the main question.
  • H2 – breaks the topic into subtopics.
  • H3 – details, case studies, data.
    
• Use numbered and bullet-point lists – AI loves “easy-to-quote” knowledge blocks.
• Comparison tables, diagrams, charts – frequently referenced by Perplexity, Gemini, and Copilot.
• Natural, conversational language – AI seeks content that can be used in dialogue, not artificial SEO jargon.

2. Mentions-driven PR & content outreach

• Earn mentions on forums, blogs, and industry media (Reddit, Quora, LinkedIn, guest articles).
• Ensure your brand appears in “best of” lists and tool comparisons – even if you’re not ranked #1, being cited matters.
• Build topical authority through consistent publications within your niche – AI associates brands better with specialized expertise.
• G2, Capterra, Trustpilot – collect reviews with real case studies (avoid vague feedback like “I recommend it”).

3. Technical foundations of AISO

Optimization for AI crawlers (GPTBot, ClaudeBot, Google-Extended):

• Check your robots.txt file and ensure AI bots have access to valuable content.
• Block private areas (dashboards, login pages).

Schema.org & structured data:

• Use formats like HowTo, FAQPage, Product, Review, VideoObject.
• This helps AI better understand your content and quote the correct sections.

Internal linking:

• Build strong topic clusters (content hubs).
• This makes it easier for AI to understand your content hierarchy.

Mobile-first and core web vitals optimization: AI prefers fast-loading, readable pages with clear layouts.

4. Video and multimedia in AISO strategy

• Regularly publish YouTube videos answering customer questions:
  
  • “How our product works”
  • “Common problems and their solutions”
  • “Comparison of solution X vs Y”
    
• Provide transcripts, chapters, and optimize with VideoObject schema.
• Embed videos in guide articles – AI is more likely to cite pages that include relevant multimedia content.

5. Monitoring and iterating AISO efforts

• Manually check your brand’s presence in AI Search (ChatGPT, Perplexity, Gemini).
• Track referral traffic from AI in GA4 and analyze branded search volume.
• Correct errors (AI misrepresentation) with dedicated pages containing up-to-date brand information.
• Experiment with content formats – if guides aren’t performing, test case studies, checklists, and landing pages.

6. Combining AISO with traditional SEO and evergreen content

• Do not abandon traditional SEO – organic traffic remains essential.
• Integrate AISO with evergreen content – AI often cites “always relevant” materials (guides, definitions, checklists).

Create content hubs that combine:

• AI search intent (AISO),
• Traditional SEO traffic,
• Link building,
• Sales support (BOFU).

AISO Checklist 2025

AI search: not a trend, but the new SEO standard

AI-powered search engines (ChatGPT, Perplexity, Gemini, Copilot) are no longer an experiment or a “gimmick.”
They represent a new, fully-fledged organic traffic channel that:

• Changes the customer buyer journey.
• Impacts the CTR of traditional Google organic results.
• Opens up new opportunities for acquiring high-quality traffic – directly from AI.

Summary & recommendations

Companies that implement an AI Search Optimization (AISO) strategy in 2025 will gain a real competitive advantage:

• They will be cited by AI as an authoritative source.
• They will acquire valuable traffic without “fighting for SERP positions.”
• They will build topical authority not only in Google’s eyes but also in the eyes of LLM models.

Key takeaways for your business:

• AISO requires a different approach than traditional SEO – mentions matter, not just backlinks.
• Your content must be clear, conversational, and expert-level – written with the goal of being quoted by AI.
• Technical SEO fundamentals (crawlability, schema, content structure) are still critically important.
• Regularly monitoring your presence in AI Search is essential – Google Search Console alone is not enough.

Resources:

1. https://www.barrons.com/articles/perplexity-google-stock-ai-search-engine-5d7b8ea3
2. https://www.ft.com/content/ac18a85e-c529-4829-9d99-037957d37cdf
3. https://blog.cloudflare.com/global-expansion-in-generative-ai-a-year-of-growth-newcomers-and-attacks/
4. https://onelittleweb.com/ai-chatbots-vs-search-engines/