How can a company get featured in the AI Chat recommendations?

The modern digital marketing landscape is undergoing its most far-reaching structural transformation since the early days of the internet. The traditional search model, in which users entered simple keywords and received a list of blue links, is rapidly disappearing. In its place come AI-generated answers, powered by Google AI Overviews, large language models and specialised generative search engines. In this new ecosystem, a brand’s visibility depends not on its usual position in search results, but rather on whether and how the brand is cited and integrated into AI-generated summaries.   

This transition has given rise to new disciplines — Generative Engine Optimisation (GEO) and Answer Engine Optimisation (AEO). Although the theoretical basis of these concepts may seem straightforward, their practical and successful implementation requires such specific technological, analytical and infrastructural knowledge that companies’ attempts to do this in-house often result in wasted resources. This study explores the mechanisms of artificial intelligence-powered search and explains why the optimisation of these systems should be entrusted to a professional agency.   

The clash between traditional SEO and AI search paradigms

To navigate this new landscape, it is essential to understand the difference between traditional search engine optimisation and artificial intelligence search optimisation. Traditional SEO focuses on ensuring a website ranks as highly as possible in search results by using keywords and backlinks. In contrast, artificial intelligence systems do not evaluate pages in isolation; they extract facts, assess the reliability of sources and synthesise a coherent, personalised response.   

AI-generated summaries are dramatically changing user behaviour. As users receive a comprehensive answer directly within the search engine interface, the number of clicks on standard organic links decreases, particularly for informational queries. However, the click-through traffic generated by AI-generated snippets has a significantly higher purchase intent and conversion potential, as these users have already passed the initial research phase and are looking for specific, verified solutions.   

This table clearly shows that old SEO tactics are no longer able to ensure competitiveness in an environment where decisions on content display are made not by a mathematical algorithm, but by a complex neural network.   

Technical and infrastructure pitfalls that imperceptibly block AI bots

One of the most common reasons why content created by companies themselves never appears in AI responses is linked to technical barriers that internal teams often fail to notice. If search bots are physically unable to access the content, its quality is no longer relevant.   

Firstly, many companies are unaware that their server security settings or content delivery networks, such as Cloudflare, automatically block AI crawlers by default. Programmes such as GPTBot, ChatGPT-User, PerplexityBot, ClaudeBot and Google-Extended are often identified as unwanted bots, resulting in the site being completely blocked from data extraction. Preventing such blocking requires specific management of the robots.txt file and server configurations, which can only be carried out by an experienced technical specialist.   

Secondly, technologies widely used in modern web development create huge barriers for AI bots. Most AI crawlers do not execute complex JavaScript code; they only read the plain HTML code returned by the server in response to the initial request. If a website’s content is loaded dynamically (as is typical of single-page applications or interactive elements), AI bots see only a blank page. Similarly, content hidden behind tabs, accordion-style menus or interactive price sliders remains completely invisible to AI crawlers, as they are unable to perform user actions such as clicking or dragging.   

Content architecture: How to write so that AI can extract and cite information

When optimising for artificial intelligence, content creation must be aligned with how LLMs process data. AI systems do not quote entire articles; they extract specific facts, definitions or sentences and integrate them into their response. For content to be suitable for this process, it must meet strict structural and linguistic standards.   

The readability and simplicity of the content are of crucial importance. Information must be formulated in clear, direct language (the recommended level of complexity is approximately that of an 8th-grade reader), avoiding verbosity and complex industry jargon unless absolutely necessary. Each section should begin with a direct and unambiguous main conclusion, followed only then by a detailed explanation or elaboration. Paragraphs should be short — no longer than three to four sentences — so that each one can be understood as a self-contained, semantically complete unit.   

Particular attention must be paid to optimising headings. General headings provide too little context for neural networks. Headings must accurately reflect the sub-queries that artificial intelligence performs when breaking down complex user queries into smaller parts.   

AI search engines highly value data presented in tables or lists, as these formats are the easiest to parse and compare with other sources. Furthermore, the recency of the data is of crucial importance. Data shows that content which has not been updated for more than three months experiences a sharp decline in citation frequency, as LLM systems give clear priority to the most recent information available.   

To help search engine bots accurately link concepts and recognise the structure of the information, a website must incorporate detailed structured data.   

The deployment and maintenance of such technical microformats cannot be carried out through the automatic installation of standard content management system (CMS) plugins. It requires precise code development, which is usually only possible for professional programmers.   

Analysis of search scenarios and question-answer structure

To better understand how artificial intelligence search engines process user queries, it is worth examining three different scenarios that illustrate the modern search environment.

Scenario A: Making a complex B2B purchasing decision

A company representative is looking for a solution to automate accounting processes and asks the artificial intelligence a detailed question.

Question: What are the best accounting software programmes for medium-sized companies in Latvia that support direct integration with banks, and how cost-effective are they?

AI analysis mechanism: The artificial intelligence will not simply search for websites that repeat the keyword ‘accounting software’. It will break this query down into several sub-queries:   

1. Which accounting software programmes are licensed on the Latvian market?

2. Which of them provide API integration with local banks?

3. What are the maintenance costs for these programmes, depending on the number of users?

For the company’s website to be used as a source, the content must be structured as a direct response to these questions.

Integration of accounting systems with banks:

• Compatibility: Solutions available in Latvia provide automatic data exchange using the ISO 20022 standard.

• Cost structure: Average costs for a medium-sized company range from 80 to 250 EUR per month, depending on the modules required and the number of transactions.   

• Key finding: The time saved through integration reduces the volume of manual data entry by 70% on average, ensuring a return on investment within the first three months.   

If a company has not structured this information and presents it as unformatted text without specific figures, the AI engine will select a competitor’s page where the data is clear and easily extractable.   

Scenario B: Selection of a local service and geographical trust signals

A user needs urgent dental treatment in a specific area of the city outside normal working hours.

Question: Where in central Riga is a 24-hour dental service available for acute pain, and what are the reviews regarding the quality of the service?

AI analysis mechanism: In this case, the artificial intelligence system prioritises trustworthiness and real-world experience. SGE searches not simply for promotional articles, but for structured data and third-party opinions:   

1. NAP consistency: Are the company’s name, address and telephone number identical across at least 50 authoritative directories and social media platforms?   

2. Local relevance: Does the website’s content mention specific neighbourhoods, the nearest public transport hubs or well-known landmarks?   

3. Review analysis: What is the average rating on Google My Business and other platforms, and how does the business respond to negative reviews?   

In this case, a hyperlocal link-building strategy – attracting links from regional media or local authority websites – is three times more effective than standard, general links. It is practically impossible to carry out synchronisation and authority building on this scale using in-house resources alone.   

Scenario C: Gathering information and defending the brand’s position

A user wants to find out the best approach to buying a property — using a mortgage or savings.

Question: In the current economic climate, is it more advantageous to buy a flat with a mortgage or to wait and save up the full amount?

AI analysis mechanism: For complex questions where there is no single correct answer, artificial intelligence seeks ‘Information Gain’. This means that AI compares the opinions, research and calculations of various experts to provide an objective summary. For a brand to be included in this answer, it must offer unique calculation models, downloadable calculators or in-depth market analysis that cannot easily be replaced by generic AI-generated text.   

The content must be created in such a way that it offers real added value and clear authorship (E-E-A-T), by engaging certified financial analysts and referencing reliable macroeconomic data.   

Next-generation KPIs: How to measure results in a changing environment

As the search landscape undergoes a transformation, traditional SEO performance metrics no longer provide a complete and accurate picture. Search engine rankings and a website’s overall click-through rate (CTR) are losing their primary significance, as users can learn about a brand and make a decision without even visiting the website itself. To assess the impact of optimisation efforts on business metrics, new metrics need to be introduced.   

This metric cannot be tracked using standard, free tools. It requires a complex analytical infrastructure, regular prompt testing and query simulation across various LLM platforms, which can only be carried out by specialised agencies with access to the appropriate software solutions.   

Why attempting to do this in-house is doomed to failure

Many business leaders believe that having a strong in-house content team or a single all-round marketing specialist is sufficient to successfully enter the AI search market. However, in reality, GEO and AEO are not simply a matter of writing content — they are highly technical engineering processes that require a multidisciplinary approach and a continuous investment of resources.   

In-house teams typically face several fundamental limitations:

1. Lack of expertise and fragmentation: To ensure comprehensive optimisation, constant collaboration is required between system developers (who tackle server and JavaScript rendering issues), data scientists (who analyse the behaviour of neural networks) and digital PR specialists (who ensure brand authority on external platforms). A single marketing specialist cannot combine all these areas of expertise at a high level.   

2. Rapid technological development: AI models and their indexing algorithms change almost every month. What worked a quarter ago may be considered an outdated tactic today. Specialised agencies invest significant resources in ongoing research and tool development, ensuring that their clients are always one step ahead of market changes. In-house teams usually have neither the time nor the methodology to carry out such in-depth research.   

3. The need for expensive and specialised tools: To carry out prompt simulations, analyse the inclusion of competitors in LLM responses and monitor sentiment across different languages, specialised software solutions are required, the monthly maintenance costs of which are not financially viable for a single company. Agencies use these tools centrally, ensuring maximum efficiency at optimal cost. 

4. Third-party authority building: GEO is not possible without a strong external reputation. Artificial intelligence analyses what industry media, forums and independent review sites are writing about the company. Professional agencies have established long-term contacts with the media and digital PR platforms, enabling them to quickly and effectively generate the necessary trust signals, which are not available to the average in-house employee.   

Conclusions: Strategic choices and the path to digital dominance

The integration of artificial intelligence into search processes has fundamentally changed the rules of digital marketing. For a company not only to survive but also to thrive in this new era, it must stop viewing SEO as a mere technical add-on or a matter of creative copywriting. It must be established as a critical piece of infrastructure that requires engineering precision and continuous adaptation.   

Attempts to manage this complex process in-house most often lead to fragmented results, technical pitfalls and an inability to track the actual return on investment. Working with a specialist digital marketing agency provides a business with the necessary assurance, access to the latest technologies and a multidisciplinary team of experts capable of ensuring sustainable brand visibility and high conversion rates in the age of AI-powered search. Choosing a professional partner is a strategic move that secures the company’s place in tomorrow’s digital ecosystem.