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How to use Google and LLM insights to improve international SEO

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How to use Google and LLM insights to improve international SEO

Many companies expand internationally by duplicating their U.S. website, translating the language, and keeping the same architecture, navigation, and content structure across markets.

Then performance drops. International versions may convert at half the rate of the original site or struggle to gain traction altogether.

The issue usually isn’t translation. It’s assuming users in different markets search, navigate, and evaluate information the same way.

Using insights from Google SERPs and LLMs, here’s how to localize website architecture and navigation for international SEO.

How to use Google to localize content

Google’s SERP interface is localized for individual markets. Each element — menu order, topic filters, questions, tags, AI structures — reflects learned user behavior.

For example, if you search for a topic or product in the UK and Italy, you’ll get different interfaces: The Italian site might show two shopping options, while the UK site puts images at position two. These aren’t arbitrary — they’re algorithmic predictions based on observed behavior in each specific region.

Google has already done the user research. You just have to extract the signals systematically. Every SERP element is optimized through behavioral data, for example:

  • Menu order reflects click-through analysis across millions of users.
  • Topic filters represent observed refinement patterns.
  • People Also Ask (PAA) boxes aggregate real user confusion points.
  • Image tags cluster search behavior patterns.
  • AI Overviews encode entity relationship patterns that a model has learned.

9 signals to create a localization framework

Use these nine SERP interface elements to contain localization intelligence.

  • Menu order/filters reveal primary and secondary search intent. They are localized and dynamic — their order changes due to seasonalities, changes of intent, content behaviors, and breaking news.
  • Topic filters show hierarchical refinement patterns (2-3 levels deep). They are influenced by trends and seasonalities, and Google mixes classic search topics with shopping filters.
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  • People Also Ask (PAA): Three levels are enough for discovering patterns and recurring entities through clustering.
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  • People Also Search For (PASF) are similar to PAAs but are related searches showing journey connections. In this case, a three-level depth is sufficient to obtain meaningful data.
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  • Image search tags for entity search: Each tag is also an entity related to the searched entity, or an attribution of that entity. They place entity associations in a visual search context.
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  • AI Overview fan-outs are AI-predicted follow-up questions from Google.
  • AI Mode fan-outs are conversational search path predictions, ideal for exploring entities and triplets.
  • Google web guides are pillar pages that break down a topic into subtopics. It’s ideal for understanding how Google reasons around a subject.
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  • Multi-LLM comparative analyses examine how ChatGPT, Gemini, and Perplexity structure their answers. LLM answers help identify both the universal semantic core shared across regions and the region-specific entities that emerge when prompted with local context. This reveals which entities matter globally versus locally.
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Table of nine localization framework signals

SignalWhatWhyHow to (manual)How to (with tools)
1. Search Menu OrderReveals primary and secondary search intentMenu position shows how Google classifies query intent per marketOpen incognito browser, set location to target city, search query, record visible menu items in exact orderBrightLocal for location simulation
2. Topic FiltersShows hierarchical refinement patterns (2-3 levels deep)Maps directly to content hub organizationScroll below search bar to “Refine this search” section, document filter chips, click each to reveal sub-levelsTopically.io, Chrome DevTools (inspect filter elements), Python/Selenium for automation
3. People Also AskUser confusion points and anxiety aggregated from real searchesDirect blueprint for FAQ sections and pillar page H2 structureLocate PAA box, document visible questions, click each to expand and reveal related questions (2 levels deep), use incognito to avoid personalizationAlsoAsked.com (visualizes PAA trees), ValueSERP API, SerpAPI for automation
4. People Also Search ForJourney paths and related searches showing sequential behaviorReveals related entities users expect to find connected; informs internal linkingScroll to bottom of search results, document 8-12 related searches shown automaticallyTopically.io, Semrush (“Related Keywords”), Ahrefs (“Also talk about”), SerpAPI
5. Image Search TagsEntity search associations (visual and general); multi-word tags reveal co-occurring entitiesTag frequency = entity salience; informs which entities need visual contentClick Images tab, observe tag chips below search bar, document all visible tags (8-15), note multi-word tagsTopically.io, SerpAPI (image search with tags), Selenium scripts
6. AI Overview Fan-OutsGoogle’s AI-predicted follow-up questions; entity relationships the model learnedSpecifically informs Google AI Overview, AI Mode, and Web Guide structure; shows content sequencing for user journeyN/AQforia by iPullRank, Gemini API with Python/Colab
7. AI Mode Fan-OutsConversational search path predictions; multi-turn journey Google anticipatesReveals complex topic exploration paths; growing importance as Google pushes AI Mode heavilyN/AQforia by iPullRank, Gemini API with conversational context in Python/Colab
8. Google Web GuideGoogle’s editorial content organization; H2-level structure Google considers comprehensiveDirect blueprint for navigation structure (not URL paths); categories reveal information types users needPerform search, look for “Web Guide” or “Guide” SERP feature (appears ~20-30% of queries), expand sections, document H2 headingsN/A (no tools available)
9. Multi-LLM Comparative AnalysisHow ChatGPT, Gemini, Perplexity structure answers to identical queries; consensus vs. unique entitiesConsensus entities = must-have content; weak/incomplete answers = information gain opportunities; validates citation-worthy contentEnter identical query in each LLM interface, copy full responses, document response length/format/entities/citations (for Perplexity), perform in local language per marketOpenAI API (ChatGPT), Google Gemini API, Perplexity API – all via Python/Colab for batch processing and entity extraction

Scaling with international SEO

Here’s an example of a product breakdown between international sites:

  • 148 products × 6 query variants = 888 queries
  • Four markets = 3,552 combinations
  • Nine signals = 31,968 data points

However, you don’t need all 31,968 data points. Patterns emerge across 15 to 20 products, roughly 10% to 15% of the catalog. Entity relationships repeat across product categories, so sampling 15 products across factions can reveal critical localization patterns.

How to transform data into taxonomy

Let’s say there’s a hypothetical website based on the Star Wars movies called “SWLegion.com,” which sells tabletop wargaming miniatures. It has several products across factions, eras, and types.

Below is SWLegion.com’s complete URL structure across four markets.

CategoryU.S. (root)UK (/en-gb/)Italy (/it-it/)Spain (/es-es/)
STORE HOME/store//en-gb/store//it-it/negozio//es-es/tienda/
TYPE OF UNIT CATEGORIES
Accessories/store/accessories//en-gb/store/accessories//it-it/negozio/accessori//es-es/tienda/accesorios/
Battle Force Packs/store/battle-force-packs//en-gb/store/battle-force-packs//it-it/negozio/pacchetti-forza-battaglia//es-es/tienda/paquetes-fuerza-batalla/
Battlefield Expansions/store/battlefield-expansions//en-gb/store/battlefield-expansions//it-it/negozio/espansioni-campo-battaglia//es-es/tienda/expansiones-campo-batalla/
Commander Expansions/store/commander-expansions//en-gb/store/commander-expansions//it-it/negozio/espansioni-comandante//es-es/tienda/expansiones-comandante/
Core Sets/store/core-sets//en-gb/store/core-sets//it-it/negozio/set-base//es-es/tienda/sets-basicos/
Operative Expansions/store/operative-expansions//en-gb/store/operative-expansions//it-it/negozio/espansioni-operative//es-es/tienda/expansiones-operativas/
Personnel Expansions/store/personnel-expansions//en-gb/store/personnel-expansions//it-it/negozio/espansioni-personale//es-es/tienda/expansiones-personal/
Starter Sets/store/starter-sets//en-gb/store/starter-sets//it-it/negozio/set-iniziali//es-es/tienda/sets-iniciales/
Unit Expansions/store/unit-expansions//en-gb/store/unit-expansions//it-it/negozio/espansioni-unita//es-es/tienda/expansiones-unidad/
Upgrade Expansions/store/upgrade-expansions//en-gb/store/upgrade-expansions//it-it/negozio/espansioni-potenziamento//es-es/tienda/expansiones-mejora/
FACTION FILTERS
Shadow Collective/store/shadow-collective//en-gb/store/shadow-collective//it-it/negozio/collettivo-ombra//es-es/tienda/colectivo-sombra/
Mercenaries/store/mercenaries//en-gb/store/mercenaries//it-it/negozio/mercenari//es-es/tienda/mercenarios/
Galactic Empire/store/galactic-empire//en-gb/store/galactic-empire//it-it/negozio/impero-galattico//es-es/tienda/imperio-galactico/
Galactic Republic/store/galactic-republic//en-gb/store/galactic-republic//it-it/negozio/repubblica-galattica//es-es/tienda/republica-galactica/
Rebel Alliance/store/rebel-alliance//en-gb/store/rebel-alliance//it-it/negozio/alleanza-ribelle//es-es/tienda/alianza-rebelde/
Separatist Alliance/store/separatist-alliance//en-gb/store/separatist-alliance//it-it/negozio/alleanza-separatista//es-es/tienda/alianza-separatista/
TYPOLOGY FILTERS
Heroes/store/heroes//en-gb/store/heroes//it-it/negozio/eroi//es-es/tienda/heroes/
Varies/store/varies//en-gb/store/varies//it-it/negozio/varie//es-es/tienda/varios/
Infantry/store/infantry//en-gb/store/infantry//it-it/negozio/fanteria//es-es/tienda/infanteria/
Tools/store/tools//en-gb/store/tools//it-it/negozio/strumenti//es-es/tienda/herramientas/
Vehicles/store/vehicles//en-gb/store/vehicles//it-it/negozio/veicoli//es-es/tienda/vehiculos/
ERA FILTERS
All Eras/store/all-eras//en-gb/store/all-eras//it-it/negozio/tutte-ere//es-es/tienda/todas-eras/
Age of Rebellion/store/age-of-rebellion//en-gb/store/age-of-rebellion//it-it/negozio/era-ribellione//es-es/tienda/era-rebelion/
The New Republic/store/the-new-republic//en-gb/store/the-new-republic//it-it/negozio/nuova-repubblica//es-es/tienda/nueva-republica/
Fall of Jedi/store/fall-of-jedi//en-gb/store/fall-of-jedi//it-it/negozio/caduta-jedi//es-es/tienda/caida-jedi/
Reign of the Empire/store/reign-of-the-empire//en-gb/store/reign-of-the-empire//it-it/negozio/regno-impero//es-es/tienda/reino-imperio/
CONTENT SECTIONS
Lore Section/lore//en-gb/lore//it-it/lore//es-es/lore/
Rules Section/star-wars-legion/rules//en-gb/star-wars-legion/rules//it-it/star-wars-legion/regole//es-es/star-wars-legion/reglas/
Mini Painting Academy/mini-painting-academy//en-gb/mini-painting-academy//it-it/accademia-pittura-miniature//es-es/academia-pintura-miniaturas/
About Us/about-us//en-gb/about-us//it-it/chi-siamo//es-es/sobre-nosotros/

Extract entities across signals

Using the above product catalog as an example, use each product as a query seed.

Start manual, with 10-15 products to internalize patterns. Then automate with APIs/Python, and store in a CSV/JSON. Cross-reference entities to identify co-occurrence patterns.

Combine all nine signals into a unified dataset. Then, extract entities mentioned across signals.

Weighted co-occurrence analysis

Track which entities appear together across signals. This reveals which concepts users naturally connect in their thinking.

Each signal has a different reliability weight based on how directly it reflects user intent:

  • LLM mentions: 3.0 (high confidence — models trained on usage patterns)
  • Query fan-outs: 2.5 (AI predicts relationships from observed behavior)
  • PAA: 2.0 (actual user questions connecting entities)
  • PASF: 2.0 (sequential journey connections)
  • Image tags: 1.5 (visual/entity search context)
  • Topic filters: 1.0 (broad categorization)

For example, say there’s a significant variation in entity relationship complexity across markets, measured as total weighted co-occurrence scores (sum of all entity pair connections, weighted by signal reliability):

  • U.S.: 2,639.5 total weight
  • UK: 2,359.0 total weight
  • Spain: 2,266.0 total weight
  • Italy: 1,084.5 total weight

This means the U.S. and UK show 2x more entity relationship complexity than Italy, indicating more complex user journeys requiring deeper content architectures.

Cross-market entity patterns

Not all entities matter equally across markets. Your content strategy depends on recognizing three distinct patterns:

  • Universal entities (all four markets): These appear consistently across the U.S., UK, Spain, and Italy. Users everywhere expect this content.
  • Market-specific: These entities show concentrated interest in just one market based on current signal validation. Cover these entities deeply in their market of reference but maintain lighter coverage in other markets. In future quarterly re-analysis, verify if interest for these entity types has increased in other targeted markets to determine whether to expand coverage depth accordingly.
  • Regional (2-3 markets): These entities appear in most but not all markets, requiring selective deployment. Build content, deploy to 2-3 markets, and evaluate ROI before expanding.

Ontology pattern recognition

Beyond individual entities, track how different types of entities connect. This reveals what content formats work in each market.

Entities cluster into four categories: 

  • Products (actual sellable items)
  • Lore (Star Wars universe entities)
  • Rules (game mechanics)
  • Painting (techniques and processes)

Cross-ontology co-occurrence reveals which content types users expect:

  • When products and lore entities appear together frequently across signals, users think in terms of narrative context for purchases:
    • Product × Lore = Battle scenario content (example: “AT-ST” + “Battle of Hoth” = Hoth battle guide)
  • When products and painting entities co-occur, users research techniques for specific models:
    • Product × Painting = Unit-specific technique guides (example: “Clone Trooper” + “blue markings” = 501st painting tutorial)
  • When painting and lore entities connect, users want thematic aesthetic guidance:
    • Painting × Lore = Themed painting content (example: “terrain” + “Scarif” = tropical planet terrain tutorial)
  • When lore entities cluster together, users compare or navigate between story elements:
    • Lore × Lore = Era/faction comparisons (example: “Clone Wars” + “Galactic Civil War” = timeline guide)

Market-specific pattern differences

These ontology patterns vary dramatically by market, revealing which entities matter, how users think about connections, and how to optimize internal linking architecture. Here’s an example weighted co-occurrence analysis

USA: Product × Lore, weight 60.0 (highest of any market)

  • What this means: American users discover products through lore narratives — build battle scenarios linking story to miniatures.
  • Internal linking strategy: From the “AT-ST Walker” product page, prominently link to /lore/battle-of-hoth/ with anchor text emphasizing narrative context (“Deploy the AT-ST in the iconic Battle of Hoth”). From lore pages, link back to related products within battle scenario descriptions.

UK: Painting × Lore, weight 15.0 (unique to UK and U.S. only)

  • What this means: British users want battle-themed painting guides — content like “Paint a Hoth snow base” works here but is less relevant elsewhere.
  • Internal linking strategy: From /mini-painting-academy/snow-base-tutorial/, link to /lore/battle-of-hoth/ and to relevant product pages like “Snowtrooper Unit Expansion.” Create bidirectional links between painting techniques and the lore/battle contexts where those techniques apply.

Spain: Product × Lore, balanced at 27.0 each

  • What this means: Spanish users balance story interest with product focus — equal emphasis needed.
  • Internal linking strategy: Moderate internal linking between product and lore pages. From “Luke Skywalker Commander” product page, include links to both /lore/luke-skywalker/ and related products. Avoid over-emphasizing either connection type.

Italy: Product × Lore weight 10.5 (weakest)

  • What this means: Italian users don’t connect lore to products — skip elaborate battle scenarios. Focus on product specs and painting basics.
  • Internal linking strategy: Minimize product-to-lore internal links. From product pages, prioritize linking to /mini-painting-academy/ tutorials and related products by faction or unit type. Keep lore pages separate from product discovery paths.

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How to validate your framework

Entities should appear in 3+ signals to be validated. One appearance could be an anomaly or noise.

False-positive check

Signals reveal what users reference, not always what they want. For example, a site appears across multiple markets in various signals, so it’s confirmed as a universal entity in LLM responses across all markets. But its presence in Image Search tags is minimal.

  • Interpretation: Users ask about the site as a reference point but aren’t searching for images of its products extensively.
  • Strategy: Build a comparison article/FAQ, not extensive image galleries or deep informational content.
  • Validation question: Does the signal show what users want or what they’re using for context?

Coverage gap analysis

For example, let’s say signal validation reveals dramatically different entity landscapes across markets — in other words, how many distinct, validated entities appeared in 3+ signals per market:

  • U.S.: 31 entities
  • UK: 28 entities
  • Spain: 29 entities
  • Italy: 16 entities

Italy has half the entity coverage of other markets, revealing a fundamental difference in how Italian users approach this product category — a strong strategic signal. 

If Italian users show concentrated interest in fewer entities, with heavier emphasis on foundational questions (for example, PAAs) rather than deep entity exploration, they’re asking, “what is this?” and “how does this work?”

There’s an information gain opportunity here: While competitors might translate all 31 US entities to Italian, creating shallow content Italian users don’t need, you can dominate the 16 entities that actually matter to this market with comprehensive, beginner-focused content.

Actions to take:

  • Italy needs foundational 101-level content rather than deep entity exploration.
  • FAQ-driven approach matches PAA dominance in Italian signals.
  • Invest in clear product specifications, basic painting tutorials, and simple rule explanations.
  • Build comprehensive coverage of the 16 validated entities before considering the other 15.
  • Monitor quarterly. If Italy’s validated entity count grows, market maturity increases, and expand coverage accordingly.

You’re not trying to force-fit U.S. models onto Italian users, you’re serving the actual information needs for this market.

How to structure internal architecture

Maintain a consistent technical structure across all markets with canonical tags, hreflang, CMS architecture, and analytics.

For the complete structure of the SWLegion.com example, see its full architecture.

Ecommerce section:

  • U.S. (root): /store/, /store/{category}/, /store/{filter}/
  • UK: /en-gb/store/, /en-gb/store/{category}/, /en-gb/store/{filter}/
  • Italy: /it-it/negozio/, /it-it/negozio/{categoria}/, /it-it/negozio/{filtro}/
  • Spain: /es-es/tienda/, /es-es/tienda/{categoría}/, /es-es/tienda/{filtro}/

Content sections:

  • U.S. (root): /lore/{entity}/, /star-wars-legion/rules/{topic}/, /mini-painting-academy/{guide}/, /about-us/
  • UK: /en-gb/lore/{entity}/, /en-gb/star-wars-legion/rules/{topic}/, /en-gb/mini-painting-academy/{guide}/, /en-gb/about-us/
  • Italy: /it-it/lore/{entità}/, /it-it/star-wars-legion/regole/{argomento}/, /it-it/accademia-pittura-miniature/{guida}/, /it-it/chi-siamo/
  • Spain: /es-es/lore/{entidad}/, /es-es/star-wars-legion/reglas/{tema}/, /es-es/academia-pintura-miniaturas/{guía}/, /es-es/sobre-nosotros/

Slug localization:

  • Store slugs fully localized (/store/ → /negozio/ → /tienda/).
  • Content section slugs localized where natural (/rules/ → /regole/ → /reglas/, /mini-painting-academy/ → /accademia-pittura-miniature/).
  • Entity slugs within content localized for official translations (Spain: /es-es/lore/conde-dooku/ vs English /count-dooku/).

What stays consistent

  • Path structure: /lore/, /store/, /rules/ exist everywhere even if entity coverage or category emphasis differs.
  • Product inventory: Physical products remain the same across markets (same 148 SKUs), though merchandising and filtering emphasis may vary.
  • Core navigation sections: All markets have Store, Lore, Rules, Mini Painting Academy, About Us, but internal linking architecture and content depth within each section adapts to market signals.

Entity coverage

Create a master entity list flagged by market validation. This will become your strategic content roadmap, preventing duplication while ensuring comprehensive coverage where it matters.

Entities cluster into two strategic categories:

  • Universal entities validated across all 4 markets: Darth Vader, Luke Skywalker, painting, terrain, miniatures, core factions (Galactic Empire, Rebel Alliance, Separatist) — these form your foundation and users everywhere expect this content.
  • Market-specific entities showing concentrated validation in one or two markets: 501st Legion (U.S./UK only), Shatterpoint comparison (Italy only), Wookiees (Spain only) — these are your localization differentiators.

Phase 1 build: Start with universal entities. Build 12-15 cornerstone pages, translate to all four markets for 48-60 total pages. These establish a baseline coverage across your entire international footprint.

Phase 2 build: Add market-specific entities. Create 25-35 localized pages to be deployed selectively only to validated markets. A 501st Legion deep-dive may go live in the U.S. and UK but not in Italy or Spain.

Total strategic content: 73-95 pages across four markets. This is a better, more refined strategy than covering 148 product entities × four markets, adding lore/rules/painting content for all entities across all markets, which would create dozens of wasted pages. 

How to implement an AI roadmap

Building out your international SEO can present some challenges. Here are some roadblocks and strategies to do it right. 

Implementation challenges

Let’s look at some hurdles to implementing AI to search.

CMS limitations

Most CMS platforms aren’t designed for entity-level localization. What’s needed is conditional page creation based on market validation.

For example: Add a “Target Markets” custom field to your CMS with checkboxes for different markets — U.S., UK, Italy, Spain, in our example. 

Content team scaling

Creating dozens of localized pages requires subject matter expertise, native language writers, and cross-market coordination. 

Start with one market — the second-largest, not the largest, to learn with a lower risk. Build 5-10 entity pages, validate traffic and conversions, and then scale to other markets only when ROI is proven.

Maintenance 

Markets evolve, new products launch, entities gain or lose relevance, and signals need periodic re-analysis. 

Re-run an abbreviated nine-signal analysis on the top 20 entities on a quarterly basis. Look for significant shifts: If entities drop from 3+ signals to one signal, consider deprecating content.

Continuous intelligence systems

Here are some tools to help monitor AI systems:

  • Wikipedia edit monitoring: Create watchlists for 10-15 key entities per market, and set email alerts for significant edits. Major additions or edit wars signal rising interest — if that happens, review entity page content and update accordingly.
  • Reddit velocity tracking: Track comment velocity on entity mentions. Entities mentioned in 5+ threads in one week (an unusual spike) should be investigated. 
  • TikTok and Instagram trends analysis: Monitor trending hashtags and viral content patterns related to your product categories. Rising hashtag usage or viral content patterns can indicate emerging entity interest before they appear in traditional search signals.
  • Google Trends “rising” analysis: Monitor “rising” queries monthly (not absolute volume). Queries with +100% week-over-week growth signal emerging interest. 

Building a roadmap

Now that you know what roadblocks lie ahead, here’s how to implement the plan.

Month 1: Foundation

  • Choose one market for learning and prototyping. Select 10-15 products to sample and conduct a systematic nine-signal analysis.
  • Create an entity list with co-occurrence weights and 3-5 validated market-specific entities.

Months 2-3: Content creation

  • Build universal pillar pages and translate to all markets, and build market-specific entity hubs, starting with one initially. Implement internal linking based on co-occurrence weights.

Months 4-6: Validation and expansion

  • Monitor entity coverage rates, LLM topic visibility, and market-specific traffic growth.

Months 7-12: Full multi-market rollout

  • Expand to all markets. Run continuous intelligence systems, including: Wikipedia watchlists, Reddit monitoring, TikTok/Instagram trends, and schedule quarterly signal re-analysis.

How to measure success

After implementing changes and incorporating AI into your international search strategy, here’s how to determine what’s working and where to improve.

Entity coverage rate

This metric tells you if you’re covering entities that actually matter to users in each specific market, not just translating pages indiscriminately.

  • Formula: (Entity pages built / Total validated entities from signal analysis) × 100
  • Example: Your signal analysis validated 28 entities in the UK (entities appearing in 3+ signals). You built dedicated pages for 22 of these entities. Your entity coverage rate is: 22/28, or 79%.
  • Target: 70%+ coverage for each priority market.

Consider the strategic difference. For example, let’s say your UK site covers 79%, or 22 of 28 validated entities, focusing resources on entities users actually search for, ask questions about, and engage with across multiple signals. 

While a competitor translates 148 product entities, achieving “100% coverage” on paper, but wastes resources covering entities UK users show minimal interest in.

Your 21% gap (6 uncovered entities) isn’t a failure, but a strategic prioritization. 

These lower-priority entities can be added if quarterly re-analysis shows their signal validation strengthening — moving from 2 signals to 3+ or appearing in additional signal types.

Tools for tracking entity coverage:

  • Screaming Frog: Crawl your site and count entity pages by market subfolder.
  • Google Sheets: Cross-reference validated entity lists against live URL inventory.

LLM topic visibility

Track whether your site appears in LLM responses for key topics, not individual citation counts. The goal is to measure topical authority, not vanity metrics.

For ChatGPT/Gemini/Perplexity/Claude: Use WAIKay.io to systematically track your visibility across multiple LLMs. The platform allows you to:

  • Set up monitoring for specific queries across ChatGPT, Gemini, Perplexity, and other AI platforms
  • Track whether your domain appears in responses (mentions, summaries, citations)
  • Monitor visibility changes over time with historical tracking
  • Generate reports showing presence/absence per topic, per LLM

For AI Overviews/AI Mode: Use Semrush One to monitor Google’s AI-powered SERP features. Alternative tools, such as Ahrefs, Advanced Web Rankings, and SISTRIX (AI Overview presence reporting), offer similar capabilities.

Target benchmarks:

  • Universal topics: Visibility in 2+ LLMs across all markets.
  • Market-specific topics: Visibility in 2+ LLMs for a specific market’s language queries.

This validates if your content quality and entity coverage are sufficient for LLMs to consider you an authoritative source worth including in their responses. Lack of visibility signals content gaps or insufficient topical depth.

Incorporate AI and LLMs into your international SEO today

Most international sites treat taxonomy as infrastructure: build once, maintain minimally, and refresh every 2-3 years during a website redesign. 

Our SWLegion.com example started with an identical architecture across four markets. Implementing this strategy, we showed how to localize architecture and navigation and optimize for each market.

This strategy builds something fundamentally different — architecture that breathes with market behavior, responding to signals rather than assumptions. You’re cultivating taxonomy rather than just maintaining a website.

Your new taxonomy will reflect current user behavior and also anticipate and adapt to behavioral shifts before competitors notice that the market has changed.

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