current, factual info is needed, meaning SEO is vital for it LLMs Answers Retrieval / Grounding Data For dynamic, factual queries In “search mode” (like ChatGPT’s browsing) the model goes beyond static memory, retrieving real-time external information using it as grounding to produce more accurate, up-to-date answers. Pre-training Data For General knowledge Publicly available text (websites, books, papers, code, forums, etc.) and licensed/curated data, until the model’s knowledge cutoff date. By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
an AI Search era By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO 1. Stop tracking rankings, traffic & conversions and start measuring AI presence, readiness and business Impact 2. Create content for topical completeness, rather than product keyword targeting 3. Optimize for brand authority and trust as a ranking gate
& SEOFOMO BRAND RECALL AI SEARCH REVENUE AI SEARCH TRAFFIC OFF PLATFORM INFLUENCE DECISION IN AI PLATFORM WEBSITE VISIT INDIRECT & MULTI-TOUCH AI SEARCH VISIBILITY PURCHASE IN AI PLATFORM DIRECT CLICK 1. Stop tracking rankings, direct AI traffic & conversions and start measuring AI presence, readiness and business Impact CHECKOUT & AGENTIC COMMERCE
& SEOFOMO Metric Layer Why it matters KPI Roles 1. Presence Are you actually appearing, and how? Replaces traffic-only thinking with visibility and representation measurement Visibility KPIs Optimization and monitoring 2. Readiness Are you structurally prepared to be surfaced? Explains why visibility is weak, strong, or unstable; the diagnostic layer Diagnostic KPIs Diagnosis and prioritization 3. Business Impact Are visibility and readiness translating into value? Connects AI search activity to commercial outcomes without overclaiming attribution Outcome KPIs Executive reporting and decision-making Measure AI search across 3 layers: Presence, Readiness and Business Impact
& SEOFOMO Start by monitoring AI presence: Is the brand shown in the answers that matter, and how is it being represented when it does? Presence KPIs Question it answers How to calculate 1. Prompt coverage Are we showing up where we need to? Prompts where brand appears ÷ total tracked prompts 2. Recommendation rate Are we being endorsed, or just included? Appearances where AI explicitly recommends ÷ appearances 3. Linked citation rate Is this visibility capable of driving visits or purchases? Appearances with a clickable link ÷ appearances 4. Comparative win rate Are we winning the shortlist when users compare options? Comparison prompts won ÷ comparison prompts where brand appears 5. Representation accuracy Are we being understood properly, or misrepresented? Factually correct appearances ÷ appearances
& SEOFOMO For ecommerce: lead with linked citation rate + comparative win rate since revenue depends on click capable mentions & winning selection stage prompts Presence KPIs Question it answers How to calculate 1. Prompt coverage Are we showing up where we need to? Prompts where brand appears ÷ total tracked prompts 2. Recommendation rate Are we being endorsed, or just included? Appearances where AI explicitly recommends ÷ appearances 3. Linked citation rate Is this visibility capable of driving visits or purchases? Appearances with a clickable link ÷ appearances 4. Comparative win rate Are we winning the shortlist when users compare options? Comparison prompts won ÷ comparison prompts where brand appears 5. Representation accuracy Are we being understood properly, or misrepresented? Factually correct appearances ÷ appearances
& SEOFOMO Track presence creating prompt libraries that reflect purchase decisions across the customer journey for product lines & personas Constraint / dimension Pattern Example prompt Category Specific product/subvertical “Best sites to buy running shoes online in the US?” Use case Purchase situation “Best running shoes for beginners training for a 10K?” Attributes Fit, specs, material, ingredients “Best non-comedogenic moisturizers for oily skin?” Price Budget / value / premium “Best wireless headphones under $150?” Trust Safety, authenticity, warranty, returns “Is Back Market reliable for refurbished iPhones?” Logistics Shipping, stock, delivery, returns “Where can I buy a dishwasher with delivery this week?” Comparison Retailer / product / brand shortlist “Amazon vs Walmart for buying baby products online?” Market Country / language / local availability “Best online fashion stores in Spain with easy returns?”
for key questions AI Presence KPIs What it measures Prompt coverage Are we showing up where we need to? Measures whether the brand appears at all across the prompts that matter. Recommendation rate Are we being endorsed, or just included? Measures whether the brand is actively recommended when it appears, rather than simply mentioned in a list or cited in passing. Linked citation rate Is this visibility capable of driving visits or purchases? Measures how often the brand is not only mentioned but also cited with a clickable link or linked source. Comparative win rate Are we winning the shortlist when users compare options? Measures how often the brand is framed as the stronger or preferred option in prompts where multiple brands are evaluated against each other. Representation accuracy Are we being understood properly, or misrepresented? Measures whether the brand is described correctly when it appears: what it does, who it is for, and why it is relevant. https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-era/
Shows whether the brand appears at all where it matters; low values point to visibility or distribution gaps Recommendation rate Shows whether the brand is actively endorsed; low values often point to trust, corroboration, or differentiation gaps Linked citation rate Shows traffic opportunity, not just awareness; low values often point to extractability or page-structure gaps Comparative win rate Shows whether the brand is framed as the stronger option; low values often point to positioning or proof gaps Representation accuracy Shows whether the brand is being described correctly; low values point to entity clarity or consistency issues Each AI search presence KPI should help to focus your AI readiness / optimization efforts https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-era/
Weak category visibility often points to Corroborated, Differentiated, or Useful gaps Weak recommendation rate often points to Credible, Corroborated, or Differentiated gaps Poor representation accuracy often points to Recognizable or Consistent gaps Weak commercial visibility often points to Transactable, Extractable, or Useful gaps https://www.aleydasolis.com/en/ai-search/ai-search-winning-brands-characteristics/
AI crawlers? Accessible Does the content solve the user need competitively — better than what else is on the first page of AI answers? Useful Are brand and entity signals explicit (name, category, founder, HQ, funding, product lines) and machine-readable? Recognizable Are key answers, positioning, and differentiators easy to parse and summarize from the page? Extractable Do those entity signals match across site, Wikipedia/Wikidat a, LinkedIn, review sites, and press? Consistent Do multiple independent third-party sources reinforce the same positioning and claims? Corroborated Do the sources that reinforce the brand carry weight (recognized publications, analyst coverage, peer-reviewed or primary data)? Credible Is the positioning clear, specific, and ownable — or is the language interchangeable with competitors? Differentiated Is the content recent enough (publish/update dates, current facts, live pricing) to remain credible and citable? Fresh Are pricing, plan logic, feature comparisons, and evaluation surfaces clear enough that AI systems can answer “which plan fits my case” questions? Transactable Assess & measure 10 key AI readiness characteristics by asking these questions https://www.aleydasolis.com/en/ai-search/ai-search-winning-brands-characteristics/
impact via 3 metrics layers Metrics from platforms passing a referrer or a UTM. Highest confidence, lowest coverage. Eg. AI referred sessions, AI conversion rate, revenue per AI visit, AI-assisted conversions. Directional signals from your own analytics or tools that sample AI traffic across the web. Medium to medium-low confidence, broader coverage. Eg. 1. Own: branded search lift, direct/unattributed lift, demand for pages known to be surfaced in AI answers, survey-based discovery. 2. External: Similarweb AI traffic behavior vs. competitors, prompt samples per page, etc. Estimates produced by applying assumptions to observed and proxy data. Lowest confidence, used for planning, never for proof. Eg. influenced pipeline, influenced revenue, incrementality estimates. Observed Proxy (own & third party) Modeled https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-era/
confidence, lowest coverage. Directional signals that correlate with AI influence but do not prove it. Medium to medium-low confidence, broader coverage. Estimates produced by applying assumptions to observed and proxy data. Lowest confidence, used for planning, never for proof. Observed Modeled Proxy (own & third party) Question: How many users clicked and converted from an AI answer? Question: 1. Own: Is there evidence users are seeing us in AI answers even when they do not click? 2. Third Party: How does our AI presence compare to competitors and which prompts are driving AI traffic? Question: If we assume X% of branded search lift is AI-attributable, what is the implied pipeline? Each metric layer answers a question to help assess your AI search business impact https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-era/
& readiness impact Shows whether known AI traffic is growing or shrinking. Remember this is the floor, not the ceiling. AI-referred sessions Shows whether known AI traffic is growing or shrinking. Remember this is the floor, not the ceiling. AI conversion rate / revenue per visit Shows whether AI contributes to conversion paths even when it is not the final click. AI-assisted conversions Detects recall and downstream demand effects beyond measurable referrals. Branded search / direct / surfaced page demand Surfaces AI influence on users who arrive via branded or direct, otherwise invisible. Survey AI discovery rate Shows whether observed growth is share-taking or category-riding. Flat share during category growth = loss. Third-party AI traffic share vs. peers Fills the gap analytics cannot: what question triggered the traffic. Drives prompt set updates and page fixes. Third-party prompt samples per top landing page Surfaces platform specific risks and opportunities early. Over indexing on one platform is a fragility signal. Third-party AI platform mix vs. peer average Useful for planning, never for proof. Overclaiming here erodes trust in the whole dashboard. Modeled influenced pipeline / revenue https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-era/
AI understanding & action Metric How to select it What to learn / action Low readiness + low visibility Structural conditions are holding the brand back. Prioritize access, extractability, entity clarity, corroboration. High readiness + low visibility Brand is underdistributed or underrepresented in the source ecosystem. Focus on source presence, distribution, trust ecosystem, competitive disadvantage. Visibility improving + impact flat Brand is appearing but not memorably, persuasively, or on the right pages. Improve recommendation quality, linked citations, memorability, landing-page fit. Strong informational + weak commercial visibility Visible early in the journey but not winning shortlist or selection moments. Improve commercial prompt coverage and transaction-ready surfaces. High visibility + strong recommendation + weak representation accuracy Being talked about but described wrong. Entity and source correction: Wikipedia/Wikidata, schema consistency, review sites, supplier pages, analyst briefings. One segment strong, another weak Issue is segment-specific, not brand-wide. Run a segment-specific readiness and source-ecosystem review. https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-era/
Metrics for the AI Search Era” guide going through it with examples https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-era/
for products AI platforms need full topic graphs to safely recommend products under diverse constraints. 1. For each product line, define a topical map: Core entity (e.g. “women’s road running shoes”), subtypes (daily trainer, long-run, speed, recovery), alternatives, etc. 2. Cover all decision dimensions , not just popular queries (Use cases, constraints, trade offs, risks, edge cases, etc.) 3. Create topic-level summaries above product-level content. 4. Assess & monitor topic completeness gaps: % of subtypes with content, % of products with full attribute sets, % of decision scenarios covered, etc. By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
the decision reasoning and constraint matching performed by AI platforms 1. Re-model demand as jobs × constraints, not queries: • Rows: use cases / jobs-to-be-done • Columns: constraints (price, size, compatibility, delivery, risk, performance, context) 2. Ensure every meaningful intersection is supported by: Product attributes, content modules, filters, internal links 3. Align filters to natural language constraints : “Good for long runs”, “Easy to travel with”, “Soft cushioning” By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
against constraints: Take "Marathon training × Cushioning (Max)": You'd ensure you have the product attributes tagged (cushion level: maximum), a content module explaining why max cushioning matters for long distances, a filter like "Soft cushioning for long runs," and internal links connecting the marathon training guide to the product listing. By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
now directly influence which product gets selected: AI systems pull reasoning from this content when recommending products 1. Create expert-authored Buying guides , “Which should I choose” comparisons, Use-case explainers, Tradeoff content 2. Link products explicitly to scenarios and constraints 3. Maintain consistency between guides and PDP attributes By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
journey for different personas Cover multiple intents for the same topic, so your content aligns with many personalized subqueries, increasing surface area. Add contextual signals that aligns content with profile-based personalization, segmenting content for specific personas or use cases. Retain attention and engagement with fast, useful content that gives a satisfying user experience since AI search systems refine results based on user behavior, thumbs up/down, etc. This feedback loops into ranking and synthesis decisions for future answers. By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
no longer the unit of value but extractable modules are. 1. Structure content with: Lists, Tables, Clear headings, Short factual paragraphs 2. Separate: Facts, Explanations, Comparisons, Policies 3. Make tradeoffs explicit (“best for / not ideal for”) By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
JS to ensure real time consistency with product feeds and content https:/ /www.aleydasolis.com/en/search-engine-optimization/what-we-learned-google-search-central-zurich-2025/
your canonical product truth, rather than secondary data source Make feeds near real-time for price and stock. AI systems deprioritize merchants with stale or inconsistent data to avoid failed transactions and maintain user trust. Ensure three-way consistency: Feeds, Product Pages, and Structured Data. Expand feeds beyond minimum required attributes. Use consistent units and normalized values to enable accurate cross-product comparison. By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
the time to start making your ecommerce journeys agent-ready Agent-ready guideline Ecommerce implication Make actions explicit Add to cart, select size, check stock, compare, return, and track order actions should be clearly visible. Keep layouts stable Product info, variants, price, delivery, returns, and CTAs should appear in predictable places across templates. Avoid ghost elements / overlays Pop-ups, transparent layers, or hidden elements shouldn’t cover selectors, filters, cart, or checkout steps. Use semantic HTML Use real buttons, links, labels, selects, and forms so agents can identify and interact with commerce flows. Label forms clearly Checkout, delivery, payment, account, return, and support fields should have connected, descriptive labels. Signal actionability Interactive elements should be visible, large enough, and use clear affordances such as cursor: pointer. Audit the accessibility tree Ensure PDPs, PLPs, guides, reviews, FAQs, and policy pages are machine-readable and structurally stable. https:/ /web.dev/articles/ai-agent-site-ux
gate AI systems use brand consistency and external validation to reduce recommendation risk 1. Secure third-party mentions and reviews on authoritative sites 2. Ensure consistent brand information across the web 3. Maintain visible customer support, returns, and company info 4. Avoid contradictory claims across channels By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
link building, community & digital PR Digital PR Positive coverage, citations, backlinks from Media Sites Community Management Positive mentions from relevant social platforms and communities Link Building Backlinks from related, authoritative sites reviewing relevant businesses Branding Mentions, promotion, visibility alignment with brand positioning and voice By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
shifts & your own optimization context Differences in User Search Behavior / Intents to further expand your site topical authority targeting the full journey Bigger Role of third-party Citations / Mentions Different bots/user agents for which you might have specific crawlability rules Lack of CSR JS support by AI bots From Performance (traffic, conversions) only to Brand (visibility, sentiment, share of voice) and Performance metrics and goals Crawlability Indexability Relevance Popularity Metrics/Goals By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
revenue and impacting marketing goals? 2. How do your AI search behavior differ from traditional search? 3. What’s your traffic, visibility, sentiment and traffic from relevant topics vs competitors in AI platforms? What’s the gap and growth opportunity? 4. How’s your content already optimized for relevant AI search topics? What’s the gap vs competitors? 5. How do the needed optimization actions overlap with existing or planned SEO, Digital PR & Community management efforts? What additional actions / investments are needed? 6. What’s the ROI from the additional efforts? Are they worthy? Prioritize accordingly! Ask these questions to prioritize your AI search optimization actions based on impact & effort By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
& Founder at Orainti ❏ Co-founder at Finchling ❏ Creator of the SEOFOMO & AI Marketers Newsletters ❏ Maker of LearningAIsearch.com & LearningSEO.io Speaker & Author ❏ Author of SEO, Las Claves Esenciales ❏ Spoke at +200 events in +30 countries By Aleyda Solis - SEO Consultant & Founder at Orainti & SEOFOMO
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