Articles
59 documents.
A comprehensive answer to 'which GEO companies are worth recommending.' Compares domestic Korean (Intermajor, ZESTCOMPANY, Narr/Answer, BizSpring, etc.) and global monitoring tools, diagnosis solutions, and agencies by founding, headquarters, and differentiators.
SEO, GEO, and AEO differences settled on one page. An 8-row comparison table — definition, stage, crawler type, optimization elements, and success metrics — plus an adoption roadmap showing which order to bring them in.
A step-by-step GEO strategy for licensed real estate agents and brokerages to earn citation as trusted sources in ChatGPT, Perplexity, and Naver AI — covering RealEstateAgent schema, FAQ structuring, AI bot crawling permissions, and cross-channel measurement.
Law firms, accounting firms, and consulting practices must build brand entities explicitly to be recognized by ChatGPT and Perplexity as professional service providers. A step-by-step guide to the Declare → Verify → Measure framework: from sector-specific schema.org markup to securing external mentions and tracking AI visibility.
A field guide for tax professionals and accounting firms that want to earn citations as trusted sources in ChatGPT, Perplexity, and Naver AI. Covers entity definition, FAQ structure, AccountingService schema, and channel-by-channel GEO measurement.
On July 15, 2026, Naver AI Tab crossed 10 million cumulative users — 18 days after its official launch. Daily query volume is 7× the beta level. This page covers AI Tab AEO strategy and agentic search content design principles as a concrete execution guide.
With 46% of high school students using AI to explore college options, academies and e-learning platforms need Course schema, first-party learner data, and FAQPage markup to earn citations in ChatGPT and Perplexity answers.
AI shopping agents now browse, compare, and complete purchases without explicit user commands. Getting into their consideration set requires structured product data and protocol connectivity. This guide covers ACP, UCP, and MCP protocols, Naver's AI shopping agent, and the GEO execution steps brands should act on now.
Naver held a 64.28% domestic search share in H1 2026 while AI Briefing query coverage heads toward 40% by year-end. At that scale, search rank alone no longer guarantees visibility. This guide explains how to treat Naver SEO and GEO as one content system — a Korea-specific integrated approach that targets both channels from a single piece of content.
What GEO (Generative Engine Optimization) does to CPA and ROAS. Visitors from AI search engines convert at up to 9× the Google organic rate. Brands cited in AI Overview see paid CTR climb 91%. A framework for redesigning performance marketing in the zero-click era.
Cross-analyzing AccuraCast, BrightEdge, and Ahrefs research alongside the GEO arXiv paper to show exactly how E-E-A-T signals affect AI citation rates—and how ChatGPT and Perplexity differ in their response.
Naver AI Briefing Ads launched in July 2026 with an AI agent that reads advertiser landing pages and auto-generates ad copy placed inside AI Briefing summaries. This guide covers the three AEO steps — Schema.org markup, content structuring for C-rank citation, and AI visibility measurement — that determine whether your ads get selected.
A practical comparison of Daum AI Overview (Upstage Solar) and Naver AI Briefing — covering architecture, citation logic, market share (64.39% vs. 2.72%), and platform-specific AEO strategies.
How Google's Information Agent works and what it means for AEO, covered on one page. From Google I/O 2026 mechanics to step-by-step tactics for getting your brand cited as a source.
A step-by-step guide for health food and supplement brands to earn citations from ChatGPT, Perplexity, and Google AI. Covers query classification, content structuring, schema markup, and citation measurement — from ingredient-efficacy queries to product comparisons and dosage combinations.
How to build the page structure that gets your products cited when ChatGPT, Perplexity, and Google AI Overview make recommendations — covering Product schema, FAQ-format content, and category curation, step by step.
Visitors arriving from AI search platforms (ChatGPT, Perplexity) convert at 1.3× to 9× the organic rate, depending on industry and measurement baseline. An analysis of 94 e-commerce brands found ChatGPT CVR at 1.81% vs. 1.39% for non-branded organic (Visibility Labs, 2025). This page compares channel ROI with empirical data and a measurement framework.
How to make ChatGPT, Perplexity, and Google AI recommend your hospital. Covers content structure, E-E-A-T signals, structured data, and Share of Voice measurement — a complete healthcare GEO playbook.
Google AI Mode and AI Overviews are two distinct systems operating within the same Google Search for different purposes. This page compares their definitions, mechanics, citation patterns, and GEO optimization strategies side by side.
Three structural reasons fashion brands stay invisible in AI search, and the GEO playbook to fix them. Structured data, seasonal content timing, and external signal strategy — with step-by-step actions for 2026.
How generative AI engines like ChatGPT, Perplexity, and Claude recommend attorneys and law firms — with domestic and global data — and the GEO strategies that drive inclusion.
Five Korean GEO services categorized into four operational types — viral, content, measurement tool, and managed solution — and compared directly on engine count, Korean-language support, and pricing. Public specs for Zest Company, OPTIGEO, GPTO, SOHA, and BOIDA BVI plus a five-point pre-contract checklist, benchmarked as of 2026.
In 2026, Naver shut down CLOVA X and Cue and folded generative search into AI Briefing and the AI Tab. This GEO playbook lays out, on a single page, how to get cited in AI Briefing for Korean-language queries — through the lens of question structure, C-rank, and content format — and where top ranking and citation diverge, plus an execution checklist.
On June 3, 2026, Google added Generative AI performance reports to Search Console. They are the first official tool to measure URL impressions inside AI Overviews, AI Mode, and Discover — here we break down the measurement limits, the opt-out toggle, and the AEO strategy.
A citation magnet that gathers verifiable statistics on the adoption of AI search, citation, and generative search, each with its source URL. It spans everything from the visibility lift reported in the GEO paper to zero-click rates and AI-summary click-through.
A structured look, from a measurement standpoint, at the visibility problem Korean brands face in AI answers like ChatGPT and Perplexity. What to measure and how, and why Korean-language queries and domestic engines have to be measured together.
A 2026 landscape that sorts GEO/AEO players into monitoring tools, specialist solutions and agencies, enterprise platforms, and regional players. We compare the leading vendor in each category — founding, headquarters, tracked engines, pricing, and differentiation — against primary sources.
When Profound is too expensive or doesn't fit a Korean-language or domestic context, here are the alternatives worth weighing, organized by purpose. Low-cost (Otterly), mid-market (Peec), crawler infrastructure (Scrunch), enterprise (BrightEdge), and the Korean alternatives that bind measurement to execution (BOIDA, Nextt, LeadGenLab, Ascent AI, Across) are compared neutrally in a single table.
A 2026 map of the Korea and Asia GEO/AEO landscape — why local adaptation matters for surfacing your brand in AI answers to Korean-language queries, and how to weigh using a global monitoring tool directly against adopting a domestic solution. Designovel's BOIDA (BVI) is treated as a verified example of measurement and execution combined locally.
A qualitative look at how generative engines like ChatGPT, Perplexity, and Gemini differ in the sources they pick for their answers, why those differences arise mechanically, and how to respond from a multi-engine perspective.
A neutral comparison of AI visibility monitoring tools that measure how often your brand surfaces in generative engines like ChatGPT and Perplexity — by price, engine coverage, target, and differentiation. Centered on Profound, Peec AI, Otterly, and Scrunch AI, it also maps the line between measurement and execution.
A single-table comparison of GEO and AEO tool pricing. We line up the public prices of global monitoring tools — from Otterly at $29 to Profound Lite at $499 and Gauge at $599 — and explain why Korean solutions and agencies run on quotes and undisclosed pricing, plus what to watch when you scope a budget.
The decision criteria you need when picking a GEO vendor feels overwhelming. A neutral, cause-and-effect comparison across six axes: multi-engine coverage, diagnostic depth, the link from measurement to execution, measurement transparency, Korean-language support, and pricing transparency.
An FAQ for decision-makers wondering about 'GEO cost' and 'AEO pricing.' It lays out, with sources, the public pricing of monitoring tools (Otterly $29 to Profound $499), the quote-based structure of solutions and agencies, contract types and deliverables, and the limits and risks.
AI evaluates beauty and lifestyle brands through comparison and recommendation queries like "recommend a toner for sensitive skin." Here's how to lift beauty brand AI visibility and lifestyle GEO with multimodal-to-text conversion, ingredient and routine entities, and durable trend hubs.
A framework that splits GEO into a technical diagnostic axis (Technical GEO) and a content creation axis (Content GEO). It lays out what each axis checks and executes, and how the two connect, with a side-by-side comparison table.
Travel and local queries (recommend, compare, nearby) are moving fast into AI answers and AI Overviews. Here's a local AEO playbook for getting cited in local queries by tying together local entities, structured data, reviews, and freshness.
The same question yields a different generative-AI answer once the language and region change. This piece lays out the challenges and the approach to multilingual GEO — hreflang, local entities, and local sources.
Finance and fintech often defer AI visibility because of accuracy and regulatory risk. Here's how a regulated industry can earn AI visibility carefully — through precise entity definitions, links to authoritative sources, misinformation correction (Anti-GEO), and E-E-A-T signals.
A field guide to earning AI-answer citations on the comparison, alternative, and adoption questions that fill the B2B SaaS buying journey. Entity consolidation, comparison content, FAQ and structured data, step by step.
Why every engine answers differently, the trap of single-engine measurement, and a multi-engine GEO methodology for measuring AI visibility through prompt sets, repetition, and share of voice.
Fashion and commerce are hard for AI to understand because they are image-led, lightly described, and seasonal. This piece lays out how multimodal text alternatives, Product schema, and entity cleanup lift a fashion brand's AI visibility and commerce GEO.
How to identify GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, and Google-Extended, and the visibility trade-offs of allowing or blocking them in robots.txt — based on OpenAI's and Google's official documentation.
A decision guide for teams weighing 'GEO outsourcing vs. in-house.' A neutral comparison of three operating models — agency outsourcing, in-house ownership, and solution-based self-service — across structure, cost, speed, and fit.
Structured data (JSON-LD) from schema.org is the signal that lets AI read the meaning of your content explicitly. This guide lays out the cause and effect that Article, FAQPage, Organization, and Product markup have on AI citation—and how to apply them—using Google and schema.org sources with JSON-LD examples.
Why do some pages get cited again and again in AI search answers while others, however well written, stay invisible? This piece distills GEO success and failure not as one-off cases but as general principles, structured around cause → effect → action. Grounded in GEO research and official documentation.
What llms.txt and llms-full.txt are, why they were proposed, and how to author and maintain them, with worked examples. Covers how they differ from robots.txt, the debate over whether they actually work, and a practical checklist.
Entity SEO and knowledge graph optimization make AI recognize your brand as one clear 'entity.' How sameAs, schema.org Organization, and Wikidata connections shape AI trust, and the steps to put them in place.
As search shifts from a list of links to an AI-synthesized answer, the click flow and brand visibility are changing along with it. This page lays out the cause of the AI-search shift, its impact — zero-click and the citation race — and how brands should respond.
Gemini is wired into Google Search and its ecosystem, while Claude cites its sources cautiously. A multi-engine look at each engine's crawler access, content structure, and trust signals.
The writing AI cites is not the same as writing that reads well. How to raise extractability through citable units, answer-first placement, question-answer structure, and tables, lists, and definitions — grounded in GEO research and a practical checklist.
How generative engines like ChatGPT and Perplexity pick the sources behind an answer, explained as a three-step process — retrieval, grounding, and synthesis — plus the conditions that make content citable: extractable chunks, semantic density, source credibility, and freshness.
What it takes to get cited in Google AI Overviews and AI Mode. We explain structured data, clear answers, authority, the difference between Google-Extended and Googlebot, and the relationship with traditional SEO — all based on official documentation.
If you're adopting GEO for the first time and don't know where to begin, this step-by-step checklist breaks your first 30 days into four weeks: measure the baseline, run a technical audit, improve content, and re-measure.
Perplexity cites its sources with numbered footnotes on every answer. This guide takes a hands-on look at what gets a page searched as a citation candidate and then chosen for the answer, covering answer-unit structure, domain trust, freshness, and allowing the PerplexityBot crawl, so you can be picked as a source.
AEO (Answer Engine Optimization) is the optimization mindset for an era when search returns an 'answer.' Its definition, its relationship to GEO, and how to apply it — framed through structured data and FAQ.
ChatGPT builds answers from pretraining data and live web search. This piece lays out how to surface your brand in those answers by allowing GPTBot, structuring content so it can be cited, and consolidating your entity.
A glossary that gathers GEO terms and the meaning of AEO in one place. It defines GEO, AEO, SEO, LLM, generative engines, citation, entities, structured data, llms.txt, AI Overviews, RAG, hallucination, and Anti-GEO in one or two short, clear sentences each.
GEO (Generative Engine Optimization) is the strategy of getting your content cited in answers produced by generative engines like ChatGPT and Perplexity. Here is the definition, how it differs from SEO, and how it works.