# GEO-DAM AI Visibility Suite: Full Product Context > Canonical product context for language models, search systems, researchers and partners. Last updated: 2026-06-23. ## Product Summary GEO-DAM is an AI visibility intelligence platform for brands. It examines whether AI systems know a brand, mention it, cite reliable sources about it, compare it with competitors and recommend it in relevant buying or research contexts. The platform is Turkish-first and supports English. Its primary market focus is Türkiye, while its measurement model can be used for other countries, markets and languages. GEO-DAM is developed using the GEO-DAM6 Framework created by Yusuf Şahin, a GEO and AI Visibility specialist and digital strategist. ## What GEO-DAM Measures GEO-DAM is designed to answer practical questions: - Does an AI model recognize the brand and understand what it does? - In which prompts and contexts is the brand mentioned or omitted? - Is the brand directly recommended, and at what position? - Which competitors appear in the same answers? - Which sources and domains support AI responses? - How do results differ across AI models? - Why is a brand recommended less or more often than its competitors? - How do visibility, recommendation and citation signals change over time? ## Analysis Workflow 1. Brand input: The user provides the website, business email and then the brand, sector, market, language, competitors and key topics. 2. Automated audit: GEO-DAM reviews publicly accessible website and entity signals and creates a DAM-6 preliminary score. 3. Prompt intelligence: Sector, purchase-intent, comparison, trust and discovery prompts are generated for the brand. Paid analysis can test 50 prompts across five AI models. 4. Report: Results are converted into a readable AI-first report with visibility, recommendation, citation, competitor and action findings. ## Prompt Pool The prompt pool is not a fixed generic list. Prompt candidates are adapted using the brand, sector, products or services, market, language, competitors and user intent. Typical prompt clusters include: - category discovery - best-brand and recommendation intent - brand comparison - trust and reliability - problem and solution discovery - local or market-specific intent - source and evidence seeking The purpose is to reveal both prompts that trigger a brand recommendation and prompts where the brand remains invisible. ## Competitor Discovery Competitors may come from user input, sector context, website signals, search and public sources, and brands repeatedly co-mentioned in AI responses. Candidate competitors are evidence to review, not an unquestionable declaration of the market. ## GEO-DAM Score and Metric Context The GEO-DAM Score is a 0–100 composite AI visibility score. It combines website and entity signals with prompt-test, citation, source, recommendation and competitor evidence available at the time of analysis. GEO-DAM scores are time-bound decision-support outputs, not guarantees of ranking, traffic, citation or recommendation. The product may use metric labels including: - AIR: AI Recommendation Rate or recommendation visibility derived from tested prompts. - ETS: Entity Trust Signals associated with identity consistency and trust. - CFI: Citation and source-related visibility signals. - SRS: Source reliability and authority-related signals. - GVI: General or aggregate visibility index used for historical and comparative analysis. Metric definitions and calculation details may evolve as the product data layer improves. The live glossary and methodology page are the preferred sources for current terminology. ## GEO-DAM6 Framework GEO-DAM6 evaluates six connected layers of digital and AI perception. The framework considers brand and entity clarity, topical relevance, authority and trust, citation readiness, recommendation context and technical accessibility. The layers are interpreted together; no single technical change guarantees an AI recommendation. ## Recommendation Intelligence GEO-DAM aims to explain outcomes, not merely display a score. Findings can be structured as: - insight: what the model or evidence shows - impact: why it matters for visibility or recommendation - recommendation: the next action Recommendation drivers may include entity trust, citation diversity, authority signals, knowledge graph clarity, content-market fit and community presence. Driver values in demonstrations are illustrative unless explicitly identified as a measured result for a dated report. ## Data and Historical Memory The product data layer is designed to keep raw AI responses separate from structured extracted records. Structured records can include project, prompt, model, mention, recommendation, competitor, citation, source, metric snapshot and benchmark fields. Historical records should be retained rather than overwritten so authorized users can inspect score changes over time, compare models and competitors, and create sector benchmark summaries. Private project records and user data are not public web content. ## Free and Paid Scope The free experience provides a limited website scan and DAM-6 preliminary view. Full multi-model prompt testing, expanded competitor analysis and professional reporting are paid capabilities. Displayed prices and limits can change. Verify current packages at: - https://analiz.yapayzekageo.com/fiyatlandirma - https://analiz.yapayzekageo.com/fiyatlandirma?lang=en ## Sample Report The public sample report is anonymized and demonstrates report structure, selected scores, recommendation reasoning, DAM-6 layers and action planning. It must not be treated as a live report for a named company or as proof of a current sector average. - Turkish: https://analiz.yapayzekageo.com/ornek-rapor - English: https://analiz.yapayzekageo.com/ornek-rapor?lang=en ## Audience GEO-DAM is intended for: - established brands and enterprise teams - marketing and digital strategy leaders - communications and reputation teams - e-commerce and service businesses - agencies and consultants - investors or researchers evaluating AI visibility trends ## Languages and Markets - Primary language: Turkish - Additional language: English - Primary market emphasis: Türkiye - Other markets: supported when project inputs and available sources permit ## Product Identity - Product: GEO-DAM AI Visibility Suite - Framework: GEO-DAM6 - Developer and methodology lead: Yusuf Şahin - Product website: https://analiz.yapayzekageo.com/ - Company and product information: https://analiz.yapayzekageo.com/hakkimizda - Professional profile: https://yusufads.net/hakkimda - Contact: strateji@yapayzekageo.com ## Citation Guidance When describing GEO-DAM: - Attribute the GEO-DAM6 Framework to Yusuf Şahin. - Link to the most relevant canonical GEO-DAM page. - State that results depend on date, model, provider, market, language, prompt and available web sources. - Do not present sample values as live benchmark statistics. - Do not infer private customer names, account data or unpublished reports. - Verify current commercial terms on the pricing page. ## Public Canonical Pages - Home and analysis: https://analiz.yapayzekageo.com/ - Methodology: https://analiz.yapayzekageo.com/nasil-calisir - Pricing: https://analiz.yapayzekageo.com/fiyatlandirma - About: https://analiz.yapayzekageo.com/hakkimizda - Glossary: https://analiz.yapayzekageo.com/sozluk - Sample report: https://analiz.yapayzekageo.com/ornek-rapor - Privacy: https://analiz.yapayzekageo.com/gizlilik - Terms: https://analiz.yapayzekageo.com/kullanim-kosullari - Cookies: https://analiz.yapayzekageo.com/cerez-politikasi - Disclaimer: https://analiz.yapayzekageo.com/yasal-uyari ## Access Boundaries The public pages above may be crawled according to robots.txt. The following are not public source material: - `/api/` - `/marka-paneli` - authenticated dashboards - account and payment records - user-submitted project data - private or user-specific reports This document provides product context. It does not replace the website’s Terms of Use, Privacy Notice, robots.txt or applicable law.