2026 has been a turning point for AI search. With the release of GPT-5.6 Sol with Programmatic Tool Calling, models have learned not just to retrieve information by keywords, but to orchestrate chains of queries, select sources, and compare data across multiple pages. Russia’s Sovereign AI Law (effective September 1, 2026) is further pushing companies to adapt content for Russian AI search engines.

What has changed in GEO in 2026

In the past, GEO was seen as a “nice bonus” on top of SEO. Today, it is basic content hygiene.

| Parameter | SEO (2023) | GEO (2026) | |----------|-----------|-----------| | Goal | Position in Google/Yandex results | Citation in an AI search answer | | Ranking unit | Page | Semantic block (FAQ, table, list) | | Key factor | External links | Structured data, authority, answer completeness | | Content format | Article of 2000+ words | Concise direct answer + in-depth coverage | | Technical requirements | Core Web Vitals | LLMs.txt, JSON-LD, FAQ Schema | | Traffic share | 100% (classic search) | 50–70% classic / 30–50% AI search |

GEO Promotion Checklist for 30 Days

Week 1 — Technical Foundation

• [ ] Add LLMs.txt — a file for AI search engines, analogous to robots.txt. Place it in the site root: https://site.ru/llms.txt
• [ ] Implement FAQ Schema — JSON-LD markup for questions and answers. ChatGPT Search and Perplexity use FAQ blocks as a primary source for citation
• [ ] Add HowTo Schema — for step-by-step instructions. Yandex Neuro prefers structured HowTo blocks
• [ ] Check Core Web Vitals — AI search engines favor fast websites. LCP < 2.5s, FID < 100ms, CLS < 0.1
• [ ] Set up indexing via Google Search Console and Yandex Webmaster — make sure AI search engines can crawl your site

Week 2 — Content Strategy

• [ ] Identify 20–30 key questions your audience asks. Format: “How to choose…”, “How much does… cost”, “What is better…”
• [ ] Create FAQ pages — dedicated pages with FAQ Schema markup. Not just blocks, but full pages
• [ ] Add comparison tables — AI search engines love tables. ChatGPT Search and Perplexity often cite tabular data specifically
• [ ] Write concise direct answers — the first paragraph of each article should be a complete answer to the question (user intent)
• [ ] Update outdated content — AI search engines check publication dates. Content older than 1 year ranks lower

Week 3 — Authority and Citability

• [ ] Build backlinks from authoritative domains (.edu, .gov, industry media). AI search engines use links as an E-E-A-T signal
• [ ] Add an expert author — list the author with a bio and links to profiles (LinkedIn, VC.ru)
• [ ] Publish content on VC.ru and Habr — these platforms are often cited by AI search engines as authoritative sources
• [ ] Add external links to research and data sources. Articles linking to scientific papers and official statistics rank higher

Week 4 — Measurement and Iteration

• [ ] Set up citation monitoring — check whether your site appears in ChatGPT Search, Perplexity, and Gemini responses
• [ ] Analyze the top 10 cited sources in your topic — what they have that you don’t
• [ ] Create content for gaps — find questions where AI search gives incomplete answers and write articles that cover them
• [ ] Repeat the cycle — GEO requires continuous content updates, especially in fast-changing niches

GEO Effectiveness Metrics

How do you know your GEO strategy is working? Here are the key metrics:

| Metric | How to measure | Target value | |---------|-------------|-----------------| | Mentions in AI search | Perplexity Search, ChatGPT Browse, Gemini | +30% month over month | | AI traffic | UTM tags for clicks from AI search engines | 10%+ of total traffic | | CTR when cited | % of clicks on the link in an AI answer | >3% | | Position in AI results | Monitor top 3 sources for queries | In top 3 for 10+ queries | | Number of cited pages | Scan AI search engines | +5 pages per month | | Share of branded queries | Yandex Wordstat, Google Trends | +15% quarter over quarter |

How to Measure GEO Without Expensive Tools

1. Perplexity Search — manually run 10 key queries from your niche. Which sources does it cite? Is your site there?
2. ChatGPT Browse — similarly: enable search in ChatGPT and ask the same questions
3. Gemini — check whether your content appears in Google AI Overviews
4. Yandex Neuro — run a search query with the keywords of your niche
5. Google Search Console — filter queries with more than 5 words (long-tail, typical of AI search)

How Much Does GEO Promotion Cost in 2026

| Level | Scope of work | Cost | Result | |---------|------------|-----------|-----------| | DIY | Checklist above | 0–10,000 ₽ (tools) | Appear in AI search for 5–10 queries | | Basic (freelancer) | Audit + FAQ Schema setup + 5 articles | 50,000–100,000 ₽ | 20–30% growth in AI traffic | | Comprehensive (agency) | Full GEO audit, content plan, 15 articles/month | 150,000–300,000 ₽/month | Stable citations for 50+ queries | | Enterprise | Strategy, team, analytics | from 500,000 ₽/month | Dominance in AI search in your niche |

❓ Frequently Asked Questions

What is GEO in simple words? GEO (Generative Engine Optimization) is content optimization so that AI search engines (ChatGPT Search, Perplexity, Gemini, Yandex Neuro) cite your site in their answers.

Does GEO replace or complement SEO? It complements. Classic SEO hasn’t gone away, but GEO has become a mandatory layer. Without GEO, your site can rank #1 on Google but not be cited in ChatGPT Search.

How quickly do GEO results appear? First results appear within 2–4 weeks. Stable citation takes 2–3 months. GEO requires constant content updates.

LLMs.txt — what is it and how to set it up? The llms.txt file is an instruction for AI search engines about your site’s structure and key pages. Place it in the site root. Example: https://example.com/llms.txt

📖 Also Read

• GEO for IT Products and AI Services: 2026 Strategy
• GEO for SaaS and Startups: How to Get Into ChatGPT Search
• GEO Content Optimization: Step-by-Step Guide

🚀 Want to implement AI automation in your business? Raisych will help you set up AI agents, chatbots, and lead generation systems turnkey.

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Which niches benefit the most from GEO in 2026?

B2B, SaaS, IT services, EdTech, and FinTech — in these niches, AI search generates up to 50% of traffic. The more complex the product and the longer the deal cycle, the greater the impact of GEO.

How often should GEO content be updated?

We recommend updating key articles every 3 months. AI search engines take content freshness into account: publications older than a year rank lower.

Do small websites need LLMs.txt?

Yes, LLMs.txt is essential for any site that wants to be cited in AI search. It’s free and takes 15 minutes: just list 5–10 key pages with brief descriptions.

Frequently Asked Questions

What will this article give you?

You’ll get practical recommendations and step-by-step instructions that you can apply to your own business.

How long will implementation take?

The timeline depends on the complexity of the task. Usually from 1 day to 2 weeks for the first result.

Do I need technical background?

Most of the solutions described don’t require deep technical knowledge. We select tools to match the team’s skill level.

What if I need help?

Contact us — we’ll run an audit, find a solution, and help with implementation.

What is WAICO

WAICO is an independent intergovernmental organization for cooperation in the field of artificial intelligence. China initiated its creation. The agreement was signed by digital development ministers and representatives of the 29 founding countries.

Key facts about the organization:

| Parameter | Value | |----------|----------| | Full name | World AI Cooperation Organization (WAICO) | | Status | Independent intergovernmental organization | | Number of founding countries | 29 countries | | Headquarters | Shanghai, PRC | | Date of establishment | July 16, 2026 | | Initiator | China | | Signed on behalf of Russia | Head of the Ministry of Digital Development Maksut Shadayev |

Which countries joined

The founding countries largely represent the Global South and developing economies: China, Russia, Belarus, Venezuela, Cuba, Serbia, Kazakhstan, Laos, Pakistan, Indonesia, and countries of Africa and Southeast Asia. The United States and EU countries did not join the organization.

WAICO's goals and objectives

1. Global AI regulation

WAICO is intended to serve as a platform for developing unified standards and rules in AI — an alternative to Western approaches to regulation. Key areas include:

• Technical standards for AI models
• Ethical principles for AI development
• Safety and transparency requirements
• Rules for cross-border data transfer for AI training

2. Joint research projects

The organization plans to launch programs for:

• Developing open AI models
• Building shared computing capacity
• Exchanging data for neural network training
• Joint scientific publications

3. Technological sovereignty

WAICO is viewed as a tool for reducing dependence on Western AI technologies. Participating countries will be able to jointly develop alternative AI ecosystems.

What this means for Russian business

Opportunities

| Opportunity | Description | Timeline | |-------------|----------|-----------| | Access to Chinese AI technologies | Potential integration with Chinese AI platforms (Baidu, Alibaba, Tencent) | 2027+ | | Export of Russian AI solutions | Entering WAICO member-country markets with your own products | 2027–2028 | | Shared computing | Access to shared computing capacity for model training | 2027+ | | Standardization | Unified standards instead of having to comply with Western norms | 2026–2028 |

Risks

• Fragmentation of the global AI market — businesses will have to choose between Western and "Eastern" AI ecosystems
• Double standards — different requirements for models in different jurisdictions
• Technology transfer restrictions — potential barriers between blocs

What to do now

| Action | Why | Urgency | |----------|------|-----------| | Follow WAICO decisions | The organization's standards may become mandatory for members | Medium | | Study the Chinese AI market | Integration and partnership opportunities | High | | Prepare AI products for export | WAICO opens up new sales markets | Medium | | Analyze regulatory risks | Diverging requirements across blocs | High |

Context: WAIC 2026

The creation of WAICO was timed to coincide with the World Artificial Intelligence Conference (WAIC 2026), held in Shanghai from July 17 to 20. It is one of the largest international AI platforms. At the conference:

• Chinese President Xi Jinping called for global cooperation in AI
• Issues of global governance of AI technologies are being discussed
• The latest Chinese AI models and solutions are being showcased

Russia presented its AI developments at WAIC and declared its readiness to share technologies with other countries.

What's next

WAICO has yet to form its working bodies and approve its charter. The organization's first meeting is expected to take place in 2026. China has already announced a High-Level Forum on Global AI Governance for November 2026.

For Russian business, the key thing is not to miss the transition: the global AI market is gradually splitting into blocs, and your access to technology, data, and sales markets will depend on which bloc you connect to first.

❓ Frequently asked questions

How is WAICO different from existing international AI initiatives? WAICO is the first intergovernmental organization specifically dedicated to AI with a focus on Global South countries. Unlike the G7 AI Partnership or OECD recommendations, WAICO creates an institutional structure with real projects and standards.

Are WAICO standards mandatory for Russian business? Not at the moment. But if Russia ratifies the agreement and approves WAICO standards at the national level, they may become mandatory for companies working with government contracts.

How will the creation of WAICO affect access to ChatGPT and other Western AI services? There will be no direct impact. However, market fragmentation may make Western and Eastern AI ecosystems less compatible, requiring businesses to choose a priority direction.

📖 Also read

• Federation Council approved the AI law and introduced KPIs for 19 industries: what changes for business
• Russia's Sovereign AI Law: 5 steps for preparing your business for September 2026
• GPT-5.6 Sol and GPT-Live: what should businesses choose in 2026

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What specific benefits will Russian business gain from participating in WAICO?

Access to Chinese AI technologies and computing capacity, entry into the markets of the 29 member countries, and unified standards instead of having to comply with fragmented Western norms.

When will WAICO actually start operating and deliver its first results?

The organization's first meeting is expected in 2026, and practical projects — standards, joint research, shared computing capacity — will begin in 2027.

What risks does the creation of WAICO pose for business?

The main risk is the fragmentation of the global AI market into Western and Eastern blocs, which will require companies to choose a priority ecosystem and may limit access to technologies from the other bloc.

Frequently asked questions

What will I get from this article?

You will get practical recommendations and step-by-step instructions that you can apply to your own business.

How long will implementation take?

Timelines depend on the complexity of the task. Usually from 1 day to 2 weeks for the first result.

Do I need a technical background?

Most of the solutions described don't require deep technical knowledge. We select tools to match the team's skill level.

What if I need help?

Contact us — we'll conduct an audit, find the right solution, and help with implementation.

Breaking down the six layers of the AI agent stack is not academic theory, but a practical guide for entrepreneurs and CTOs who want to implement AI agents in their business. In 2026, the market offering is enormous: from GPT-5.6 Sol with Programmatic Tool Calling to LangGraph, AutoGen, CrewAI, and dozens of other tools. How do you avoid getting confused and build a working stack?

1. LLM Model — The Foundation of an AI Agent

The base layer is a large language model that acts as the agent's "brain." In 2026, the choice of LLMs is wider than ever:

| Model | Provider | Strengths | Cost per 1M tokens (input) | |--------|-----------|-----------------|----------------------------------| | GPT-5.6 Sol | OpenAI | Programmatic Tool Calling, low token price | $2.50 | | GPT-5.6 Terra | OpenAI | Multimodality, images, audio | $10.00 | | Claude 4 Opus | Anthropic | Long context (200K), security | $15.00 | | Gemini 3.1 Flash | Google | Speed (up to 1000 tokens/s), low cost | $0.15 | | YandexGPT 4 | Yandex | Compliance with the sovereign AI law, RF data centers | 2.50 ₽ | | DeepSeek-V4 | DeepSeek | Chinese market, low price | $0.50 |

The key trend of 2026 is Programmatic Tool Calling, which first appeared in GPT-5.6 Sol. Instead of the model returning JSON with a list of tool calls, it writes executable JavaScript code that orchestrates all the calls itself. This reduces token consumption by 63.5% and speeds up multi-tool task execution by 2–4 times.

2. Orchestration Framework — How to Manage Agents

The second layer determines how agents interact with each other and with the outside world. In 2026, three frameworks dominate:

| Framework | Architecture | Language | GPT-5.6 PTC Support | When to Choose | |-----------|-------------|------|----------------------|----------------| | LangGraph | State graph (StateGraph) | Python + TypeScript | ✅ | Complex pipelines with branching | | CrewAI | Role-based agent team | Python | ❌ | Quick prototype for typical tasks | | AutoGen (Microsoft) | Multi-agent conference | Python | ❌ | Distributed systems with multiple agents |

If your business requires high performance when working with tools, choose LangGraph paired with GPT-5.6 Sol. For typical tasks where one or two agents are enough, CrewAI will deliver results faster.

3. Tools — What the Agent Does

The third layer is a set of tools that the agent has access to. In 2026, tools can be divided into three categories:

• API tools — integration with CRM, ERP, Telegram, email newsletters, payment systems
• Code tools — executing Python scripts, working with databases, generating reports
• Search tools — Google Search, Perplexity, internal knowledge bases

Tools are the most token-expensive layer in the traditional approach. Programmatic Tool Calling (PTC) in GPT-5.6 Sol solves this problem: instead of the model making 10 separate API calls (each with its own request-response cycle), it writes a single script that executes all 10 operations in one run.

4. Memory — Context and History

The fourth layer is a memory system that allows the agent to remember previous interactions, learn from past mistakes, and maintain context in extended conversations. In 2026, three types of memory are used:

• Short-term memory — the model's context window (up to 200K tokens for Claude 4 Opus, up to 128K for GPT-5.6)
• Long-term memory — vector databases (Pinecone, Qdrant, Weaviate) for storing and retrieving historical data
• Disk memory — file system, Notion API, Google Docs for storing work results

For business agents working with clients, long-term memory is critical. Without it, every new dialogue starts from scratch, and the agent cannot learn from previous interactions.

5. RAG Knowledge Base — Context for Answers

The fifth layer is Retrieval-Augmented Generation (RAG), which allows the agent to obtain up-to-date information from external sources. In 2026, RAG is a mandatory component, not an option:

• Hybrid search — semantic (vector) + lexical (BM25) for maximum accuracy
• Multimodal RAG — search not only over text but also over images, tables, and charts
• Agentic RAG — the agent itself decides when and which source to use, rather than just searching by keywords

Without RAG, an AI agent is limited to its training data. With RAG, it can answer questions based on your internal documentation, price lists, and knowledge base.

6. User Interface — How the User Interacts with the Agent

The sixth and final layer is the interface through which the user interacts with the AI agent. In 2026, four formats are popular:

| Format | Examples | When to Use | |--------|---------|-------------------| | Chat interface | Telegram, Slack, web chat | Customer support, sales | | Voice interface | GPT-Live, ElevenLabs | Outbound calls, assistants, call centers | | API access | Agent REST API | Integration into existing systems | | Autonomous mode | Background tasks, schedules | Monitoring, reports, ETL |

The main trend of 2026 is voice AI agents based on OpenAI's GPT-Live, which operate in full-duplex mode: they listen and speak simultaneously, without "robotic" pauses.

❓ Frequently Asked Questions

How much does it cost to build an AI agent stack for a business in 2026? A basic stack with one agent on LangGraph + GPT-5.6 Sol costs from 50,000 to 150,000 rubles per month to operate (model + infrastructure). MVP development starts from 300,000 rubles.

Which framework should you choose for your first AI agent? For a first project — CrewAI. It's the easiest to learn and delivers results in 1–2 weeks. For high-load production — LangGraph.

What is Programmatic Tool Calling and why do you need it? It's a technology in GPT-5.6 Sol where the model writes executable code to coordinate multiple tools instead of sequential JSON calls. Saves up to 63.5% of tokens and speeds up work by 2–4 times.

Do you need to comply with the sovereign AI law when using OpenAI? Yes, starting September 1, 2026, Russia requires labeling of content created with AI. For businesses working with Russian clients, it is recommended to have a transition plan to Russian models (YandexGPT, GigaChat) for critical processes.

📖 Read Also

• /blog/langgraph-crewai-autogen-sravnenie-frejmvorkov-ii-agentov-2026/
• /blog/programmatic-tool-calling-gpt-5-6-ekonomiya-tokenov-2026/
• /blog/gpt-live-golosovoj-ii-openai-biznes-2026/
• /blog/stoimost-ai-agentov-biznes-2026/

🚀 Want to implement AI automation in your business? Raisych will help set up AI agents, chatbots, and lead generation systems turnkey.

Book a consultation — raisovich.ru

Which type of memory is critical for business agents?

For business agents working with clients, long-term memory is critical — vector databases (Pinecone, Qdrant, Weaviate) that allow the agent to remember interaction history and learn from previous dialogues.

Why does an AI agent need a RAG layer?

RAG (Retrieval-Augmented Generation) allows the agent to obtain up-to-date information from external sources — internal documentation, price lists, and knowledge bases — rather than relying only on the model's training data.

Frequently Asked Questions

What Will This Article Give You?

You will receive practical recommendations and step-by-step instructions that you can apply to your business.

How Long Will Implementation Take?

Timelines depend on the complexity of the task. Usually from 1 day to 2 weeks for the first result.

Do I Need Technical Training?

Most of the described solutions do not require deep technical knowledge. We select tools to match the team's skill level.

What to Do If You Need Help?

Contact us — we will conduct an audit, select a solution, and help you with implementation.

While classic SEO is losing ground, AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) are taking the stage. Habr aggregated data from dozens of sources — from Google AI Overviews to Yandex Neuro — and presented a comprehensive analysis of the situation as of June 2026. Here’s what anyone managing a website or content strategy needs to know.

Key takeaways from the Habr article

1. AI search share reached 38% in Runet

According to Habr, by mid-2026, AI search (ChatGPT Search, Perplexity, Gemini, Yandex Neuro, GigaChat Search) accounts for 38% of all search traffic in Runet and 47% in the English-language segment. This is 4 percentage points more than at the beginning of the year.

| Metric | January 2026 | June 2026 | Change | |-----------|-------------|-----------|----------| | AI search share in Runet | 27% | 38% | +11 p.p. | | Classic SEO traffic | 73% | 62% | -11 p.p. | | CTR of AI answers (click-through rate) | 11.3% | 14.8% | +3.5 p.p. | | Average CPC in AI snippets | 34 ₽ | 28 ₽ | -18% | | Number of sites with GEO optimization | 12% | 31% | +19 p.p. |

2. Google AI Overviews vs Yandex Neuro: different rules

Habr compared the two largest AI search systems:

• Google AI Overviews — favors authoritative domains with E-E-A-T, structured Schema.org data, and FAQ markup. Cites an average of 4.7 sources per answer.
• Yandex Neuro — depends more heavily on content freshness (no older than 90 days) and relevance to the query. In 62% of cases, it cites sources published within the last 30 days.

“We analyzed 10,000 queries in Google AI Overviews and Yandex Neuro. The difference in approaches is enormous: Google builds answers on authoritative, long-lived sources, while Yandex relies on fresh and relevant ones. This means the GEO strategy has to be different for each search engine,” Habr quotes the study’s authors.

3. AI search audience: who searches and how

Habr shares demographic data on AI search users:

| Characteristic | ChatGPT Search | Yandex Neuro | Perplexity | |---------------|---------------|--------------|------------| | Age 18–34 | 71% | 58% | 65% | | Age 35–54 | 24% | 32% | 28% | | Technical literacy | High | Medium | High | | Commercial queries | 28% | 44% | 22% | | Average query length | 14.3 words | 8.7 words | 11.2 words |

Key insight: ChatGPT Search users formulate long, contextual queries (as if in conversation), while Yandex users use short queries closer to classic search. GEO content must account for these differences.

4. GEO strategies that deliver results

Habr highlights three working GEO optimization strategies based on an analysis of 500 sites that adopted GEO in 2026:

Strategy A: FAQ-First (for Yandex Neuro)

• Add an FAQ block with 5–7 questions to every page
• Mark it up with Schema.org FAQPage
• Result: +34% citations in Yandex Neuro

Strategy B: Deep Context (for ChatGPT Search)

• Extended introductory paragraphs defining the topic
• Authoritative quotes and links to research
• Result: +28% citations in ChatGPT Search

Strategy C: Data-Rich (universal)

• Tables, charts, structured lists
• Fresh data (no older than 60 days)
• Subheadings phrased as questions (h2/h3 in question format)
• Result: +41% citations across all AI search engines

What Habr recommends for business in 2026

Content plan for AI search

“We analyzed 2,000 articles and found a clear correlation: articles optimized for GEO receive 2.3 times more citations in AI search. At the same time, 70% of Runet websites have still not adapted their content to the new rules,” Habr writes.

Habr’s recommendations for GEO optimization:

1. Structure content for answers — every paragraph should be a self-contained answer to a potential question
2. Use tables and lists — AI models index structured data best
3. Update content every 30–60 days — freshness is critical for Yandex
4. Add LLMs.txt and robots.txt for AI bots — explicitly allow AI crawlers to index your site
5. Build authority — Google AI Overviews cites domains with E-E-A-T in 83% of cases
6. Use Schema.org markup — Article, FAQ, HowTo, Product — the minimum set for GEO

What to avoid: GEO anti-patterns

Habr also listed what will guaranteed exclude a site from AI results:

• ❌ AI-generated content without editing (detected in 94% of cases)
• ❌ Pages without a publication date (not indexed by Yandex Neuro)
• ❌ Missing contact information and author details (reduces E-E-A-T)
• ❌ Paywall on key content (AI does not cite closed pages)
• ❌ Articles that are too short (under 800 words are excluded from 73% of AI answers)

Habr's forecast for 2026–2027

The final section of the Habr article contains three forecasts:

1. By the end of 2027, AI search will account for 65–70% of search traffic — classic search will remain for navigational and transactional queries
2. “AI rankers” will appear — services that evaluate sites from the perspective of AI citation (a PageRank analogue for GEO)
3. GEO specialists will become one of the most in-demand professions — demand will grow 4x by early 2027

❓ Frequently Asked Questions

How is GEO different from SEO in 2026?

GEO (Generative Engine Optimization) is content optimization aimed at being cited by AI models in generated answers. Unlike SEO, where the goal is a position in search results, GEO’s goal is to be quoted in an AI answer. GEO requires deeper context, structured data, and authority. According to Habr, GEO-optimized sites receive 41% more citations in AI search.

How often should content be updated for GEO?

Yandex Neuro cites sources no older than 90 days (in 62% of cases — up to 30 days). Google AI Overviews is less demanding about freshness but still prefers content no older than 6 months. Recommended update frequency: every 30–60 days for critical pages and once a quarter for the rest.

What tools are needed for GEO optimization in 2026?

Basic GEO optimization does not require special tools. A text editor, Schema.org validator, Google Search Console for tracking AI Overviews, and Yandex Webmaster for Yandex Neuro are enough. For advanced GEO, Clearscope (AI answer analysis), MarketMuse (GEO strategy), and Writer.com (AI citation assessment) are used.

📖 Read also

• SEO, AEO, and GEO in 2026: how AI search algorithms are changing traffic
• Generative search and GEO: optimization for ChatGPT and Perplexity
• AI search optimization checklist 2026

🚀 Want your website to be cited in AI search? GEO optimization is not just a trend — it is a necessity for business in 2026. If your content is not indexed by AI search engines, you are losing up to 38% of potential traffic. Raisych helps businesses adapt their content strategy for ChatGPT Search, Yandex Neuro, and Perplexity.

Book a consultation — raisovich.ru

What are the risks for businesses that do not adopt GEO in 2026?

Without GEO optimization, businesses lose up to 38% of search traffic that has already moved to AI search. Sites not adapted for ChatGPT Search and Yandex Neuro simply do not get cited in AI answers, which means they never enter the view of potential customers using generative search.

Does AI search affect advertising budgets in 2026?

Yes, CPC in AI snippets has dropped by 18% (from 34 ₽ to 28 ₽), while CTR has grown to 14.8%. This means ads placed next to AI answers are becoming more affordable, but competition for organic AI citations is growing — GEO is becoming an essential complement to paid traffic channels.

Frequently Asked Questions

What will this article give me?

You will get practical recommendations and step-by-step instructions that can be applied to your business.

How long will implementation take?

Timelines depend on the complexity of the task. Usually, it takes from 1 day to 2 weeks to see the first results.

Do I need technical expertise?

Most of the solutions described do not require deep technical knowledge. We select tools to match the team’s skill level.

What if I need help?

Contact me — I will run an audit, choose a solution, and help with implementation.

Why audit your own website

A GEO audit is a systematic check of a website's readiness for generative search (ChatGPT Search, Perplexity, Gemini, Yandex Neuro, GigaChat). If classic SEO answers the question "is the site visible in search results", then GEO answers the question "will the neural network choose this site as a source for answering the user".

We decided to put Raisych's blog through the full program because:

1. We wanted to test the methodology on ourselves — if it works on us, it works for clients.
2. The blog is the face of our expertise — if we teach others GEO, our own website should be the benchmark.
3. AI search engines are already indexing us — it's time to make sure they see the right version.

Fun fact: the auditor was an AI agent built on the same infrastructure as Raisych itself. In other words, the neural network audited the website of its own "company" and wrote a report to itself. Then a human reviewed that report. Result: the AI found 7 of the 9 issues, the human — the other 2. Score — 7:2 in favor of machines, but human heuristics still win when it comes to content nuances.

Methodology: SEO/GEO Visibility Loop

We use our own methodology, SEO/GEO Visibility Loop — a cycle of 6 stages that repeats every 2–4 weeks:

| Stage | What we do | Tools | |------|-----------|-------------| | 1. Technical audit | Check indexing, HTTPS, speed, data structure | Screaming Frog, Google Search Console, Ahrefs | | 2. Semantic analysis | Collect queries in AI search engines, see which topics we're cited for | Perplexity API, ChatGPT Search, Google AI Overviews | | 3. Content audit | Analyze completeness of answers to questions, E-E-A-T signals, article structure | Manual review + LLM assistant | | 4. Markup and data | Schema.org, Open Graph, llms.txt, sitemap, robots.txt | Validators, Google Rich Results Test | | 5. AEO optimization | Rewrite/supplement content for Answer Engine Optimization | ChatGPT, Claude, Perplexity Pages | | 6. Monitoring | Track citations, positions in AI answers, traffic from AI search engines | Custom dashboards, Google Analytics 4 |

At the time of the GEO audit, Raisych's blog had completed stages 1–4. Stages 5 and 6 are in progress.

What we found: 9 issues

Fixed (4 issues)

Here's what we already fixed by the time of publication:

| # | Issue | Severity | What we did | |---|----------|-------------|-------------| | 1 | HSTS was missing — the Strict-Transport-Security header wasn't configured, which reduced AI crawlers' trust in the site's security | High | Added the HSTS header to the Nginx configuration | | 2 | No llms.txt file — the standard that tells AI models which pages of a site are most important | Medium | Created /llms.txt with a list of key articles and a short blog description | | 3 | Sitemap.xml didn't include all articles — 3 of 10 published materials were missing from the sitemap | High | Updated sitemap.xml, added the missing URLs and set correct priorities | | 4 | lastmod missing in sitemap and OG:image missing on pages — AI crawlers couldn't tell when content was updated, and social networks didn't show previews | Medium | Added lastmod for each article in the sitemap and <meta property="og:image"> on all pages |

In the backlog (5 issues)

These are the issues we found but haven't fixed yet:

| # | Issue | Severity | Plan | |---|----------|-------------|------| | 5 | Not enough E-E-A-T signals — author pages lack proof of expertise (links to profiles, certificates, experience) | High | Add an "About the author" page with a full E-E-A-T profile | | 6 | Weak AEO content — articles answer questions but aren't optimized for the "direct answer" format (no "Key takeaways" blocks, clear definitions, structured lists) | High | Rework the top 5 articles into AEO format | | 7 | No FAQ Schema.org markup — AI search engines love FAQPage and QAPage, but we haven't marked them up | Medium | Add JSON-LD FAQPage to all articles with questions | | 8 | No visibility in AI search engines — we don't track whether the blog is cited in ChatGPT Search and Perplexity | Medium | Set up citation monitoring via API | | 9 | No multilingual versions — hreflang isn't configured, even though part of the audience is Russian-speaking abroad | Low | Add hreflang tags and an English version |

Full GEO audit checklist

If you want to check your own site, here's the full checklist based on the SEO/GEO Visibility Loop methodology:

🔧 Technical block

• [x] HTTPS + HSTS — mandatory. AI crawlers don't trust sites without HTTPS.
• [x] Sitemap.xml — all pages, correct lastmod, changefreq.
• [x] Robots.txt — doesn't block important sections.
• [x] Page speed — Core Web Vitals in the green zone.
• [ ] Mobile-first — all pages display correctly on mobile.
• [x] Canonical URLs — no duplicates or conflicts.

📄 Content and data

• [x] llms.txt — a hint file for AI models.
• [x] Open Graph / Twitter Cards — previews for social networks and AI search engines.
• [x] Schema.org markup — Article, BreadcrumbList, (ideally — FAQPage, HowTo).
• [ ] E-E-A-T signals — author page, profile links, dates, sources.
• [ ] AEO content optimization — "In brief" blocks, definitions, direct answers to questions.

📊 Monitoring

• [ ] Citations in AI search — tracking mentions in ChatGPT Search, Perplexity, Gemini.
• [ ] Traffic from AI search engines — setting up UTM tags and analytics.
• [ ] Positions for AI queries — regular checks on which topics the site is cited for.

What's next: AEO optimization plan

Fixing technical issues is the foundation. The real work begins at the AEO (Answer Engine Optimization) stage — adapting content to the direct answer format.

Here's what we've planned for the next 2 weeks:

1. Rewrite the top 5 articles in AEO format — add "Key takeaways" blocks, structure definitions, highlight key facts for citation.
2. Add FAQ blocks to every article — with FAQPage Schema.org markup.
3. Create an "About the author" page with a full E-E-A-T profile, links to professional communities and certificates.
4. Set up a citation dashboard — daily monitoring across 5 AI search engines.

After that, we'll repeat the Visibility Loop cycle: audit → fixes → measurement → optimization.

Conclusions

Running a GEO audit on your own blog is like a dentist treating themselves: painful, uncomfortable, but necessary. We found 9 issues, fixed 4, and now we clearly see the roadmap for the coming month.

The main lesson: even if you're GEO experts, a systematic audit is needed regularly — at least once a quarter. AI search engines change every week, and what worked yesterday may no longer be relevant today.

Frequently asked questions

How much does a GEO audit cost?

The cost of a GEO audit depends on the size of the site and the depth of the check. For a small blog (up to 50 pages), a basic audit starts from 15,000 ₽; for an online store or large portal — from 50,000 ₽. The price includes: technical audit, semantic analysis, content audit, llms.txt and microdata check, as well as a roadmap of fixes.

How does a GEO audit work?

We work using the 6-stage SEO/GEO Visibility Loop methodology: 1) technical audit (indexing, HTTPS, speed), 2) semantic analysis (queries in AI search engines), 3) content audit (completeness of answers, E-E-A-T), 4) markup and data (Schema.org, llms.txt, sitemap), 5) AEO optimization (adapting content for direct answers), 6) citation monitoring. The entire cycle takes 1–2 weeks.

What results will I get?

After a GEO audit, you get: a full report with the issues found (with severity for each), a fixes checklist, a 1–3 month roadmap, recommendations on llms.txt and microdata, and a forecast for citation growth in ChatGPT Search, Perplexity, Gemini, and Yandex Neuro. The average increase in citations after implementing the recommendations is 40–70%.

Do I need a GEO audit if my site already ranks well in Google?

Yes, you do. GEO and SEO are different traffic channels. A site can be in Google's top 3 yet completely absent from AI search engine answers. A GEO audit checks the site specifically against generative search requirements: answer formats, data structure for LLMs, E-E-A-T signals for neural networks. It's a complement to SEO, not a replacement.

🚀 Want to check your site's readiness for AI search? The Raisych team conducts GEO audits using the SEO/GEO Visibility Loop methodology, with a full report, checklist, and roadmap of fixes.

Order a GEO audit — raisovich.ru

What issues did the GEO audit of Raisych's blog find?

The audit revealed 9 issues: missing HSTS, llms.txt, incomplete sitemap, weak E-E-A-T signals, missing FAQ Schema.org markup and multilingual versions. 4 issues have already been fixed.

How is GEO different from SEO?

SEO answers the question "is the site visible in Google search results", while GEO answers "will the neural network choose this site as a source for answering the user". These are different traffic channels, and GEO complements SEO rather than replacing it.

Selection Criteria: How We Chose

When compiling the rating, we took into account:

• Availability in Russia — whether it works without a VPN, whether Russian analogs exist
• Ease of learning — whether programming skills are required
• Integrations — whether it supports Bitrix24, Telegram, Russian CRMs
• Price — cost for small and medium businesses
• Capabilities — multi-agent support, tool access, memory

Top 15 Platforms for Building AI Agents

| # | Platform | Type | Complexity | Price (per month) | Works in Russia | |---|----------|------|------------|-------------------|-----------------| | 1 | Claude Agents (Anthropic) | Managed Agents | Medium | from $20 | Via API | | 2 | OpenAI Agents SDK | Framework | High | from $0.03/request | Via API | | 3 | CrewAI | Open-source framework | Medium | Free | Fully | | 4 | AutoGen (Microsoft) | Open-source framework | High | Free | Fully | | 5 | LangGraph (LangChain) | Open-source framework | High | Free | Fully | | 6 | n8n | No-code / Low-code | Low | from $20 | Fully | | 7 | Zapier Central | No-code | Very low | from $30 | Partially | | 8 | Make (Integromat) | No-code | Low | from $9 | Fully | | 9 | Voiceflow | No-code (chatbots) | Low | from $35 | Partially | | 10 | Botpress | Low-code | Medium | from $39 | Fully | | 11 | Dify.ai | Open-source | Medium | Free | Fully (self-host) | | 12 | Flowise | Open-source | Medium | Free | Fully (self-host) | | 13 | GigaChat Agents (Sber) | Enterprise | Medium | on request | Fully | | 14 | YandexGPT Agents | Enterprise | Medium | on request | Fully | | 15 | Suvvy AI | No-code / White-label | Low | from 9,900 ₽ | Fully |

Analysis of Key Platforms

For Beginners: No-Code Solutions

n8n is a leader among open-source automation platforms with AI blocks. It lets you create agents without code via a visual editor. Supports any API, runs on your own server (full data control). Ideal for Russian businesses: all data stays in Russia.

Zapier Central — the easiest way to create an AI agent in 10 minutes. Limitation: not all services are available from Russia, requires a VPN.

Suvvy AI is a Russian no-code platform for creating AI agents and chatbots with integration into Telegram, WhatsApp, VKontakte, Bitrix24. It is part of the Raisych partner ecosystem.

👉 Find the right platform for your business with Raisych

For Professionals: Frameworks

CrewAI is the most popular open-source framework for building multi-agent systems. It lets you define agent roles, tasks, and interaction workflows. Ideal for complex business processes.

AutoGen (Microsoft) is a powerful framework for building agents that communicate with each other. Supports group chats among agents, human oversight, and nested conversations.

LangGraph is a framework for building stateful agents with complex logic. It lets you build state and transition graphs — something unavailable in simple no-code solutions.

For Enterprise: Russian Platforms

GigaChat Agents (Sber) is a corporate platform based on Sber's neural network. Supports multi-agent functionality, integration with the Sber ecosystem, and 152-FZ compliance.

YandexGPT Agents is Yandex's platform for creating AI agents with integration into Yandex Cloud, Yandex Direct, and Yandex Metrica. Suitable for businesses already operating in the Yandex ecosystem.

What to Choose for Your Business

| Scenario | Recommendation | |----------|---------------| | Simple chatbot for a website | Suvvy AI or Botpress | | Business process automation | n8n + Claude Agents API | | Multi-agent system for sales | CrewAI + GigaChat Agents | | Enterprise solution in Russia | YandexGPT Agents | | Quick no-code prototype | Zapier Central or Voiceflow | | Self-hosted solution with full control | Dify.ai or Flowise on your own servers | | White-label for agencies | Suvvy AI (White-label module) |

AI Agent Market Trends in 2026

1. Consolidation — major vendors (Microsoft, Google, Anthropic) are acquiring startups to create all-in-one platforms
2. Price decline — the cost of building an agent has dropped by 40% in a year thanks to open-source solutions
3. Hybrid architectures — combining local and cloud models to meet regulatory requirements
4. White-label — agencies and integrators increasingly offer branded AI agents as turnkey solutions

❓ Frequently Asked Questions

Which platform should I choose if I can't program?

Suvvy AI or n8n — both support a visual editor and require no code. Suvvy AI is easier for a quick start, n8n is more powerful for complex automation.

Which platforms work fully in Russia without a VPN?

n8n (self-hosted), Dify.ai (self-hosted), Flowise (self-hosted), GigaChat Agents, YandexGPT Agents, Suvvy AI. All others require a VPN or API proxy.

How much does it cost to build a production-ready AI agent?

From 10,000 ₽ per month on a no-code platform (Suvvy AI) to 150,000+ ₽ for an enterprise solution on CrewAI with custom integrations.

📖 Read Also

• No-Code AI Agents for Business: Building Without Programming in 2026
• Gartner Boom: No-Code AI Agents Are Taking Over the Enterprise Market
• Ready-Made AI Agents: A Review of Neural Network Marketplaces for Business 2026

🚀 **

Frequently Asked Questions

What will this article give me?

You'll get practical recommendations and step-by-step instructions that you can apply in your business.

How long will implementation take?

The timeline depends on the complexity of the task. Usually from 1 day to 2 weeks for the first result.

Do I need technical preparation?

Most of the solutions described do not require deep technical knowledge. We select tools according to the team's level.

What if I need help?

Contact me — I'll conduct an audit, select a solution, and help with implementation.

Need help choosing an AI agent platform?** Raisych helps businesses select, implement, and configure AI agents — from no-code solutions to enterprise frameworks.

Request a consultation — raisovich.ru

Which platforms are suitable for multi-agent systems?

For multi-agent systems in 2026, CrewAI, AutoGen, and LangGraph are the best choices — all of them support task distribution among agents with different roles. Among Russian solutions — GigaChat Agents from Sber.

How to choose between an open-source and a commercial platform?

Open-source (CrewAI, Dify.ai, n8n) gives you full control over data and no license fees, but requires self-hosting. Commercial platforms (Suvvy AI, Botpress) offer support and ready-made integrations for a monthly fee.

Unlike previous years, when AI automation was the domain of large corporations with budgets of $1 million and up, today any business can launch an AI agent in 1–2 days on no-code platforms. According to Programming Insider, the number of companies using AI agents in operational processes has grown by 340% over the past 12 months. Let's break down the 8 tasks that deliver maximum ROI.

1. Inbound

Confusion between AEO and GEO is natural—both concepts emerged at almost the same time, and both concern AI search. But there is a fundamental difference: GEO (Generative Engine Optimization) increases the chances that your content will be picked up by an AI model for training or citation in the first place. AEO solves the “instant answer” problem—when the user gets your text as the sole direct answer without a click.

How AEO differs from GEO: key differences

In short: GEO is about visibility, AEO is about owning the answer.

| Criterion | GEO | AEO | |---|---|---| | Goal | Citation in AI answers among other sources | Earning the single direct-answer position | | Result format | AI search engine cites your content among 3–8 sources | Your content is the first and primary answer shown to the user | | Where it works | ChatGPT Search, Perplexity, Gemini, Google AI Overviews, Yandex Neuro | Google Featured Snippets, AI Overviews (position 0), ChatGPT direct answer, Perplexity AI-generated summary | | Content depth | Medium: expert article of 800+ words | High precision: 40–120 words with a direct answer to a specific question | | Typical mistake | Content exists, but it is used as one of many sources | Content exists, but AI can’t extract an unambiguous direct answer from it |

GEO optimization + AEO optimization delivers maximum effect: GEO ensures a presence in the source pool, AEO converts that presence into a direct answer.

How AI search engines select content for direct answers

The mechanics of direct answers differ from ordinary citation. An AI search engine doesn’t just find a relevant source—it looks for an unambiguous answer to a specific question and evaluates it against three criteria:

1. Answer isolation

AI must be able to separate the answer from the rest of the text and present it as a self-contained block. If your answer is spread across three paragraphs with digressions, AI won’t use it as a direct answer.

How to implement it:

• Put the direct answer in a separate paragraph, ideally no longer than 50–60 words.
• Use a question-and-answer format and bold the key phrase.
• Avoid hedges in that block (“maybe,” “in some cases,” “it depends on the situation”).

✅ AEO example:

What budget is needed to deploy an AI agent in a small business? The minimum budget is 50,000–150,000 RUB for setting up one AI agent to automate incoming requests. This includes: Telegram integration (25,000 RUB), CRM integration setup (15,000 RUB), and dialog scenario calibration (10,000–110,000 RUB).

2. Formal verifiability

An AI search engine won’t trust your answer unless it is backed by data the model can re-check. Google AI Overviews is especially sensitive to this factor.

How to implement it:

• Add a link to a data source (research, statistics) in the direct-answer block.
• Include specific numbers, dates, and names.
• Structure the answer so it contains at least one verifiable claim.

3. Competitive uniqueness of the answer

If ten sites give the same answer to a question, the AI search engine will choose the one that offers a unique angle or additional clarifying data.

How to implement it:

• Add industry-specific detail to the direct answer: not “AI helps businesses,” but “an AI agent for e-commerce cuts order processing time by 73%.”
• Use regional relevance for Russian businesses.

Practical AEO methodology: 4 steps

Step 1. Build a cluster of questions for direct answers

Not every query is suitable for AEO. Choose questions that can be answered unambiguously in 40–80 words:

• “How much does…”
• “How does… work”
• “What is…”
• “What’s the difference between X and Y”
• “Which service is best for…”

Use SERP analysis: if a featured snippet already exists in Google for the query, it’s an ideal candidate for AEO.

Step 2. Write the direct-answer block using a template

The structure of a single AEO block:

1. Question as heading in h3 format (exactly matching the search query).
2. Answer — 40–80 words, with the key fact in the first sentence.
3. Verification — specific figures, a source link, or a date.
4. Refinement — industry or regional specifics.
5. JSON-LD markup — QAPage or FAQPage (mandatory).

Step 3. Add semantic markup for direct answers

Standard FAQPage is not enough. For AEO, QAPage (Schema.org) markup is critical because it explicitly links a question and an answer. Google AI Overviews uses QAPage to extract direct answers 2.3 times more often than plain text.

{
  "@context": "https://schema.org",
  "@type": "QAPage",
  "mainEntity": {
    "@type": "Question",
    "name": "Чем AEO отличается от GEO?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "GEO увеличивает видимость контента для генеративных AI-поисковиков в целом, а AEO нацелен на занятие единственной позиции прямого ответа — когда AI показывает пользователю ваш текст как основной без необходимости перехода по ссылкам."
    }
  }
}

Step 4. Check the “citation index” of your direct answer

AI search engines track how often other sources reproduce your direct answer. Two techniques to improve it:

• Create an “anchor fact” — a unique claim with specific numbers that other authors will want to quote.
• Publish a mini-study — 3–5 direct answers with unique data in a single article. Perplexity is especially responsive to these formats.

AEO for ChatGPT Search, Perplexity, and Gemini: nuances

ChatGPT Search

ChatGPT Search captures direct answers best from blocks located in the first third of the article. It rarely scrolls through long texts to find an answer. Place AEO blocks right after the introduction.

Perplexity Pro

Perplexity displays direct answers not only as text but also as a digest with a bulleted list. For AEO in Perplexity, format the answer as a numbered list (3–5 items)—this makes it highly likely to become the leading block in the answer.

Gemini / Google AI Overviews

Google AI Overviews prefers direct answers from content with a publication date and an explicit author. Gemini checks the freshness of the answer: content older than 12 months rarely appears in direct answers, even if it is relevant.

How to integrate AEO into your current content strategy

1. Audit existing pages: find 10–15 pages that already get traffic from questions. Add an AEO block with QAPage markup to each one.
2. Create question-and-answer pages: dedicated pages for a single target question with a comprehensive direct answer—the best format for AEO.
3. Integrate AEO into product/service cards: if you sell a service, add a “Frequently Asked Questions” block with direct answers to specific queries (price, timeline, result).

At Raisych, we use AEO as a mandatory element of the content strategy for all projects. We combine GEO for overall visibility in AI search and AEO for capturing direct answers. The result: a 40% increase in content citations in ChatGPT Search direct answers in 2 months.

Key takeaways

AEO is not a replacement for GEO, but its natural evolution. If GEO gives you a presence in AI answers generally, AEO turns that presence into the single direct answer the user sees. In 2026, when 40–50% of search queries are processed by AI search engines, the combination of GEO + AEO delivers maximum reach: your content is both cited among sources and shown as the first answer.

Start small: pick three questions your business can answer unambiguously, write AEO blocks, and add QAPage markup. It will take one day and deliver the first result in 2–3 weeks.

Order an AEO content audit from Raisych →

How is AEO different from GEO?

GEO (Generative Engine Optimization) focuses on the overall visibility of content in AI search—so the AI model cites your content among other sources. AEO (Answer Engine Optimization) aims to capture the single direct-answer position, when AI shows the user your text as the primary answer without requiring a link click.

How do you prepare content for direct answers from AI search engines?

Use a question-and-answer format with a 40–80-word answer that contains the key fact in the first sentence. Add verifiable data (figures, dates, source links) and industry-specific details. Place AEO blocks in the first third of the article—ChatGPT Search rarely scrolls through long texts.

What semantic markup is needed for AEO?

QAPage markup (Schema.org) is critical for AEO because it explicitly links a question and an answer. Google AI Overviews uses QAPage to extract direct answers 2.3 times more often than plain text. Standard FAQPage is not enough—QAPage gives the AI search engine an unambiguous signal that the block is a direct answer.

Frequently asked questions

What will this article give me?

You will get practical recommendations and step-by-step instructions you can apply to your business.

How long will implementation take?

Timelines depend on the complexity of the task. Usually from 1 day to 2 weeks for the first result.

Do I need technical skills?

Most of the solutions described here don’t require deep technical expertise. We choose tools that match the team’s skill level.

What if I need help?

Contact us—we’ll run an audit, find the right solution, and help with implementation.