AI Visibility Index: Digital Marketing Agencies in Georgia 2026

Comprehensive Research Report by RankCaster

Abstract

In August 2026, RankCaster conducted a study of the visibility of digital marketing agencies in Georgia across leading AI systems. The research was carried out using RankCaster AI, which combines automated measurement of AI Presence Rate (APR) with technical analysis across AEO, AIO, and GEO dimensions.

The study analyzed responses from Claude, ChatGPT, Google AI Overview, and DeepSeek to 20 English- and Georgian-language prompts covering agency selection, tourism, real estate, fintech, e-commerce, and SaaS.

The findings indicate that an agency’s AI visibility is not determined directly by backlink volume, domain age, or a high technical score. The strongest explanatory factor was the presence of content that matches user intent and can be used by an AI system as a direct answer to a query.

1. Introduction

Generative AI systems are becoming an independent channel for discovering and selecting service providers. Potential clients increasingly formulate queries as selection tasks rather than keyword combinations:

• Which agency is best suited to an international company?
• Which agency specializes in hospitality marketing?
• Which agency has experience in fintech?
• Which company can manage e-commerce growth?
• Which agencies are most trusted in the local market?

In these scenarios, brand visibility is not determined solely by search-engine presence. An AI system must identify the agency, associate it with the relevant geography, service, and industry, and then select it as a relevant recommendation.

This is the purpose of the AI Visibility Index used in the RankCaster study.

The research examined:

• Which digital marketing agencies appear most frequently in AI-generated answers.
• Whether visibility differs between local and international markets.
• Which industry verticals generate the highest concentration of visibility.
• How strongly technical indicators correlate with AI visibility.
• Which types of content and external sources increase the likelihood of recommendation.

2. Research Methodology

2.1. Research design

The study was conducted on August 16–17, 2026, using RankCaster AI.

English-language prompts were treated as an indicator of global visibility, particularly among international clients. Georgian-language prompts were used to evaluate local visibility in the Georgian market.

2.2. AI Presence Rate

AI Presence Rate is a normalized metric reflecting an agency’s presence in AI-generated answers, taking into account both the frequency and position of mentions.

For this study, the AI Visibility Index was calculated as the average APR across 20 prompts and four AI providers.

The following dimensions were calculated separately:

• Global: results from English-language prompts.
• Local: results from Georgian-language prompts.
• Vertical visibility: visibility within specific industry scenarios.

The index is intended for comparative analysis within the research sample. It does not represent the absolute probability that an agency will be recommended by every AI system in every user scenario.

3. Main Findings

3.1. Overall ranking

Marketing House, Web Features, and MediaHub occupy the top three positions and together account for 57.5 percentage points of the combined index. This indicates a high concentration of AI visibility at the top of the market.

The gap between first and fourth place is approximately 5.1 times. Marketing House’s index is also approximately 12.3 times higher than that of the agency ranked tenth.

This concentration should be interpreted as a concentration of measured visibility rather than a direct share of all AI recommendations. The index is a normalized research metric.

3.2. Market structure

The results indicate three levels of AI presence:

• Leadership tier: Marketing House, Web Features, and MediaHub.
• Middle tier: Multiplayer Agency, Infinity Solutions, WATSON, Redberry, BLH, Digital Matters, and Clipart.
• Fragmented lower tier: agencies with an index of 0.25–0.75% that appear only in individual scenarios.

The leadership of the top three agencies is not explained by a single factor. Marketing House and Web Features primarily dominate local queries, while MediaHub has a more balanced profile and is the only top-three agency with comparable global visibility.

4. Local and Global Visibility

4.1. The language-market gap

The most significant finding is the difference between visibility in Georgian- and English-language queries.

Marketing House and Web Features demonstrate strong local AI visibility but are entirely absent from the English-language scenarios. This does not indicate low agency quality; rather, it reflects the localized structure of their publicly available content.

MediaHub, by contrast, demonstrates the most balanced model, maintaining visibility in both English- and Georgian-language prompts.

4.2. Geographic disambiguation

In a global AI context, the word “Georgia” may refer either to the country or to the U.S. state. Consequently, the phrase “digital marketing agency in Georgia” may not identify the intended market with sufficient precision.

The English-language MediaHub page uses the following H1:

> “Digital Marketing agency in Tbilisi, Georgia”

This wording performs a geo-disambiguation function by eliminating ambiguity and associating the agency with Tbilisi and the country of Georgia.

RankCaster considers explicit geographic identification one of the most practical elements of international AI positioning for agencies.

5. Visibility by Vertical

AI systems do not produce a single agency ranking for every business need. An agency may be visible for a general query but absent from a specific industry category.

The findings indicate that vertical specialization is becoming a distinct dimension of AI visibility.

For example:

• Marketing House leads in fintech and real estate.
• Web Features achieves the highest visibility in e-commerce and SaaS.
• MediaHub leads in tourism and shares first place in real estate.

These differences are likely related not only to agencies’ actual experience but also to how clearly that experience is represented in documents accessible to AI systems.

6. Technical Audit and AI Visibility

6.1. Technical audit results

All agencies in the top ten received high technical scores.

6.2. Correlation analysis

The negative correlation does not mean that technical optimization reduces AI visibility. It is explained by the limited range of the sample: the ranking already includes websites that have achieved high technical scores.

This creates a survivorship effect. Websites that failed to meet the basic threshold for accessibility and content extraction may not have entered the research sample at all.

RankCaster therefore interprets the results through a two-level model:

Technical accessibility and structured content
                    ↓
          Basic visibility threshold
                    ↓
      Inclusion in the AI index and retrieval
                    ↓
  Relevance, authority, freshness, and context
                    ↓
             Position in the AI answer

Technical optimization is a necessary condition. However, once a basic threshold has been reached, it is no longer sufficient to explain the differences between agencies.

7. Content as the Primary Differentiator

7.1. Pages that became answer sources

The most frequently cited pages in the study were:

The common characteristic of these pages is topic-intent match: the content corresponds closely to the user’s underlying objective.

These pages do not merely describe a company. They function as standalone documents that an AI system can use when constructing an answer.

7.2. Contrast with a typical homepage

A typical agency homepage primarily answers questions such as:

• What is this company?
• Which services does it offer?
• How can it be contacted?

However, an AI user query usually requires a more specific conclusion:

• Which agency is the best choice?
• Which agency is suitable for a particular industry?
• Which agency works with international clients?
• Which agency has experience with a particular type of business?

If agency content does not make these connections explicit, the AI system may not classify the company as a relevant candidate.

8. External Mentions and Link Profiles

8.1. Mention volume is not sufficient

The comparison between external mentions and AI visibility did not reveal a positive linear relationship.

The impact of external mentions depends on their context.

8.2. Context of external mentions

RankCaster identifies three main types of contextual mismatch:

• Corporate description instead of recommendation.

Mentions of Redberry in B2B intelligence databases confirm the company’s existence and characteristics but do not answer the question of which agency should be selected for a specific marketing objective.

• Irrelevant industry context.

Leavingstone is frequently mentioned in relation to advertising awards and creative achievements. These signals may be useful for queries about creative agencies but may not be activated in digital marketing agency selection scenarios.

• Low-quality sources.

A substantial number of Performa mentions are concentrated on content farms and aggregators. The volume of these mentions does not compensate for their low authority and lack of recommendation value.

MediaHub, by contrast, receives external signals from portfolio platforms, LinkedIn, and B2B directories with explicit geographic context. These sources confirm not only the company’s existence but also its professional category.

8.3. Domain-level and page-level links

Another finding is the absence of a direct relationship between external links to a specific page and that page’s AI visibility.

No external referring domains were identified for Marketing House — Digital 10, yet the page became the most frequently cited source among the materials analyzed.

This suggests that an individual page can achieve AI visibility through:

• close alignment with user intent;
• substantive independence;
• accessibility and indexability;
• inclusion in training or retrieval corpora;
• clear topical structure.

Links can support discovery and trust, but their presence does not guarantee inclusion in an AI answer.

9. Freshness, Authorship, and Structured Data

The Web Features — Digital Marketing Guide achieved strong visibility through a combination of signals:

• a recent publication date;
• `BlogPosting` markup;
• an identified author;
• an organizational entity;
• an educational format;
• a direct connection to the Georgian market and startups.

The MediaHub — Digital Marketing Agency in Tbilisi page uses Article markup, author information, and an English-language page with an unambiguous geographic H1.

The key Marketing House — Digital 10 page does not contain Schema.org markup, yet it performs strongly because of its close alignment with the query. This is methodologically important: structured data can improve extractability, but it cannot replace a thematically relevant document.

Based on the materials analyzed, RankCaster identifies the following practical signals:

• a current publication date;
• a date of last update;
• an author with an identifiable profile;
• an `Article` or `BlogPosting` entity linked to `Person` and `Organization`;
• explicit language and geographic signals;
• a heading aligned with the subject of the user query;
• substantive content rather than a simple service list.

10. Four Variables of AI Visibility

Based on the findings, RankCaster identifies four groups of factors.

The most promising model is to create content that is not merely about the company but can function as a standalone answer:

• rankings and industry overviews;
• guides for defined audiences;
• pages such as “agency for [vertical] in Georgia”;
• case studies with specific objectives and outcomes;
• comparison pages;
• public research and benchmark reports.

11. Practical Recommendations

11.1. Short-term actions

During the first one to four weeks, agencies should consider:

• Creating English-language pages using the phrase “in Tbilisi, Georgia”.
• Adding vertical landing pages for tourism, real estate, fintech, and e-commerce.
• Implementing Article or BlogPosting structured data.
• Specifying the author, publication date, and last-modified date.
• Reviewing the H1, title, and above-the-fold content for alignment with commercial queries.
• Adding specific case studies that identify the industry, geography, and outcome.

11.2. Medium-term strategy

Over the following one to three months, agencies should:

• publish guides for specific industries;
• create English-language materials for international clients;
• describe services through client problems rather than internal agency structures;
• develop profiles on Behance, LinkedIn, and relevant B2B platforms;
• secure external mentions in recommendation-oriented and industry-specific contexts;
• update outdated articles and service pages.

11.3. Long-term strategy

Over a three- to twelve-month horizon, agencies should:

• conduct original research;
• publish market data and industry benchmarks;
• create primary sources that can be cited by media and AI systems;
• build a consistent association between the brand and selected industry verticals;
• measure AI visibility regularly across an expanded prompt set;
• maintain separate local and global content strategies.

Particular attention should be paid to the quality of external context. Broad presence in directories and databases is not equivalent to editorial recommendation.

12. Research Limitations

RankCaster considers these results a research snapshot rather than a definitive classification of the entire Georgian agency market.

The main limitations are:

• A limited sample of 20 prompts.
• Measurement at a single point in time: August 16–17, 2026.
• Incomplete coverage of industry verticals.
• Differences between the algorithms and source-selection mechanisms of AI providers.
• Possible influence of DeepSeek on local-language results.
• Survivorship bias in the technical audit.
• Dependence of the final index on the selected APR calculation method.
• Potential instability of AI answers when the same prompts are repeated.

AI visibility is a dynamic metric. Model updates, changes in indexing, publication of new content, and the emergence of new external sources may materially change the results within several months.

Conclusion

The RankCaster study demonstrates that AI visibility for digital marketing agencies in Georgia is becoming a distinct competitive environment.

Its main characteristics are:

• a high concentration of visibility among a small number of agencies;
• a substantial difference between local and global AI demand;
• technical quality as a necessary but insufficient condition for leadership;
• backlink volume unable to replace topical relevance;
• external mentions working only when they appear in the right professional context;
• vertical specialization requiring explicit representation in content;
• fresh, authored, and structured materials receiving additional advantages;
• answer-oriented content remaining the strongest visibility factor.

Marketing House and Web Features demonstrate the effectiveness of locally focused content designed for Georgian-language queries. MediaHub presents a more balanced model by combining local relevance with English-language geographic positioning.

From RankCaster’s perspective, an agency’s objective in AI search is not simply to “enter the index.” It must create documents and external signals that enable an AI system to answer three questions with confidence:

• What kind of company is this?
• Which task and industry is it suitable for?
• Why should it be recommended to the client?

Agencies that systematically answer these questions through their own content will be best positioned to gain a durable advantage in the emerging AI recommendation channel.