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ChatGPT Shopping Is Here- And Why It Matters

Caleb Turner on November 3, 2025

OpenAI’s Agentic Commerce approach lets ChatGPT understand your product catalog, present your items in shopping conversations, and, if you’re an approved merchant, complete purchases with Instant Checkout. You participate by providing a structured Product Feed and, if you opt in to purchases, lightweight checkout endpoints and a payment path. The feed is the assistant’s structured source of truth for your products; the spec accepts CSV, TSV, XML, or JSON and allows updates as often as every fifteen minutes so pricing and inventory stay fresh. For brands working with agencies like SEO Rank Media, this means keeping a clean, regularly refreshed catalog export and wiring up the minimal checkout endpoints when you’re ready.

Where this diverges from the traditional search mindset is intent and format. The goal isn’t just to match a query; it’s to help the assistant reason about fit, trade-offs, and next best products in a conversation. That’s why the feed includes fields for relationships (accessories, bundles), review content, and media like video and 3D models. It’s also why there are per-product control flags, so you can allow discovery without enabling checkout, or enable checkout only where you’re operationally confident.

Momentum matters here, too. OpenAI has publicly described merchant onboarding in the United States, Etsy availability inside ChatGPT, Shopify support coming, and a headline partnership with a major national retailer to enable shopping via ChatGPT. In other words, this is not a lab demo; it’s becoming a mainstream retail surface.

What’s actually different from a Google Shopping feed

Google search results page for the query bullet journal.

Google Merchant Center’s base product feed is mature and widely adopted. It supports identifiers, variant grouping, sale windows, and even 3D model links for certain categories. It also enforces parity between the feed and your landing pages; Google crawls and compares price and availability, and it can disapprove products for mismatches.

OpenAI’s feed is built for a conversational assistant. It encourages you to author narrative descriptions, include related products and compatibility, and attach reviews and Q&A inline so the model can quote real customer language to address objections. Regional price and availability can live right in the same product record. And visibility and checkout are toggled per SKU via control flags, giving you more granular participation.

Comparison chart: what’s in the base feed vs. what requires extras

bar chart comparing the features of the OpenAI Product Feed and the Google Merchant Center

Legend: 1 = in the base product feed; 0.5 = supported via a separate feed or API; 0 = not supported

(The chart shows both feeds include core identifiers, variants, sales windows, and preorders. Google handles product ratings and regional inventory through separate mechanisms, so those are shown as partial support. OpenAI’s base feed includes reviews and Q&A inline, explicit related-product relationships, geo-overrides within the same record, and per-SKU participation flags for search and checkout.)

Capability / AttributeOpenAI Product Feed (base)Google Merchant Center (base)Google Separate Mechanism (if any)Notes
Core identifiers (GTIN, MPN, brand)YesYes—Both ecosystems rely on standard identifiers for matching and deduping.
Variant grouping (parent/child variants)YesYes—Use a stable parent grouping to present color/size options as one product.
Sale price + effective date windowYesYes—Always pair sale price with its active start/end window.
Preorder / backorder + availability dateYes (preorder date required)Yes (preorder/backorder with date)—Both require a future availability_date when you mark preorder; GMC also supports backorder.
Inventory quantityYes (inline)NoYes (Content/Merchant API)GMC uses inventory endpoints (incl. regionalinventory) rather than a quantity in the base feed.
Regional price and availabilityYes (inline geo overrides)No (in base)Yes (regionalinventory via Content/Merchant API)Google manages regional overrides through its API and regions service.
Reviews & ratings in product feedYes (inline review stats / Q&A)No (in base)Yes (separate Product Ratings feed)GMC ingests ratings via a distinct reviews feed; OpenAI accepts review data in the product feed.
Related products & relationships (accessories, substitutes, “often_bought_with”)Yes (inline relationships)No—OpenAI exposes explicit relationship fields to power conversational cross-sell.
3D model linkYes (model_3d_link)Yes (virtual_model_link)—GMC’s 3D attribute is currently scoped; OpenAI lists 3D as a media field in the base feed.
Video link in base feedYes (video_link)Limited (not a standard base attribute)—OpenAI’s base spec includes video URLs; Google handles rich media through other paths, not the simple base feed.
Per-SKU discovery & checkout flagsYes (enable_search,enable_checkout)No—OpenAI provides channel-specific participation controls per product.
Site-parity enforcement (price/availability)Not described as a crawl-enforced requirementYes (site ↔ feed parity required)—GMC explicitly checks for price/availability mismatches and can disapprove items.

Why SEOs and growth teams should treat this as a separate feed

Google Sheets spreadsheet titled chatgpt_product_feed_with_narrative

First, narrative and semantics matter. The assistant isn’t just matching strings—it’s explaining. Well-written product descriptions, explicit benefits, trade-offs, and voice-of-customer snippets give it better material to work with. You can also declare relationships such as “required_part,” “often_bought_with,” or “compatible_with,” which helps the assistant build a smarter basket in one conversation.

Second, unification reduces friction. Instead of juggling separate files for product, reviews, and regional overrides, you can put complete context in one feed. That simplifies your pipeline and ensures the assistant always has the full picture.

Third, freshness is a visibility lever. If you update price or stock, you can push those changes quickly so the assistant never promotes items that are out of stock or mispriced. Frequent updates are supported and expected.

Fourth, there are real channel differences. Google requires strict parity between feed and website. OpenAI’s guidance, by contrast, centers the feed as the structured source of truth for conversational use. Treat them as distinct channels with distinct goals and editorial constraints, not a single file you copy and paste.

What this looks like operationally (a practical loop)

A typical team sets up a fifteen-minute job that builds a single authoritative export. The job joins product data with inventory, pricing, regional overrides, media, relationships, and review snippets or aggregates. The exporter validates key rules: currency codes, sale windows, working image and video links, and preorder dates when applicable. It then serializes to CSV or JSON and posts the file over encrypted HTTPS to the allow-listed endpoint. On success, it archives the file and logs the record count and checksum; on failure, it retries with backoff and raises an alert. Many teams begin with full refreshes and later shift to delta exports once volume grows.

To phase in purchases, keep discovery enabled while checkout is off, then enable checkout per SKU once you’re confident in stock accuracy, shipping options, and tax logic. Payments are delegated to your processor; the assistant renders the UI, but you authorize, capture, settle, and refund on your rails. If this sounds complicated, reach out to us and we’ll help you.

Field-by-field: what each attribute means (plain English)

openai feed product list
  • id: Your stable internal SKU or product identifier. Do not recycle it; this anchors updates and prevents duplicates.
  • title: The product name customers expect to see. Keep it specific and readable.
  • description: A clear, scannable explanation of what the product is, what it does, who it’s for, and why it’s different. Treat this like conversion copy, not a keyword dump.
  • brand: The brand or manufacturer. Consistency here improves matching and trust.
  • gtin: The global trade item number, such as a UPC or EAN. When available, this is the most reliable cross-system identifier for exact product matches and review association.
  • mpn: The manufacturer part number. Use it with brand if you lack a GTIN.
  • image_link and additional_image_link: The primary product image and any extra angles or lifestyle shots. Assets should be accurate for the variant (color, size) and high quality.
  • video_link: A product video for demonstrations, comparisons, or unboxing. Helpful for the assistant to surface richer explanations.
  • model_3d_link or virtual_model_link (terminology varies by platform): A 3D asset the assistant or storefront can reference for interactive views or augmented reality in supported categories.
  • price: The regular price with currency code.
  • sale_price and sale_price_effective_date: A promotional price and the start/end window when it applies. Always pair these so the assistant knows when the discount is valid.
  • availability: Stock status such as in_stock, out_of_stock, preorder, or backorder (terminology varies by platform).
  • availability_date: When a preorder or backorder is expected to ship. Required when you mark something as preorder.
  • inventory_quantity: Your current stock count. In OpenAI’s feed this helps the assistant avoid recommending something you can’t fulfill.
  • condition: Usually “new,” but other values exist for refurbished and used goods where allowed.
  • item_group_id: The parent code that ties variants together. Use the same value across color and size variants so the assistant can show a single product with selectable options.
  • color / size / size_system / gender: Variant attributes, especially important in apparel and footwear. Keep naming consistent across the group.
  • shipping: Your shipping methods, regions, and prices in a structured format. Use it to expose free-shipping thresholds and expedited options where they help conversion.
  • delivery_estimate: When the customer can expect the item to arrive. The assistant can use this to answer timing questions.
  • geo_price and geo_availability: Region-specific overrides for price and stock. Handy for cross-border or multi-state operations with local pricing or inventory pools.
  • seller_name / seller_url / policy fields: Merchandising and trust elements—your store name, privacy policy, terms, and return policy. If you enable checkout, many of these become required.
  • product_review_count / product_review_rating: A quick summary of review volume and score to aid ranking and give the assistant context.
  • raw_review_data or q_and_a: Snippets of customer reviews or curated questions and answers. This is powerful: the assistant can quote or paraphrase real buyer language to resolve objections.
  • related_products and relationship_type: Cross-sell and compatibility. Examples include required accessories, compatible parts, alternatives, or bundles. This is how you encode the buying graph you want the assistant to use.
  • enable_search: Whether a product is eligible to surface in ChatGPT shopping results.
  • enable_checkout: Whether the product is eligible for in-chat purchasing. These two flags let you stage participation without committing everything at once.
  • created_at / updated_at: Timestamps for auditing and freshness logic. Keep them accurate to make incremental updates reliable.

    A short, practical checklist

  • Maintain a separate OpenAI feed so you can write conversational descriptions, include reviews and Q&A inline, define relationships, and set participation flags per SKU.
  • Keep identifiers clean: GTIN where available; otherwise brand plus MPN; never recycle id.
  • Pair sale_price with sale_price_effective_date; include availability_date for preorders; validate currency codes.
  • Update whenever product, price, or availability changes—fifteen-minute exports are supported and recommended.
  • Start with discovery enabled and checkout off; enable checkout per SKU once stock, taxes, and shipping are proven.

Closing thought

Previously, your shopping feed was a supporting document that needed to line up with your PDPs and help an ad platform target the right queries. In the agentic era, your feed is the main character: it’s the content the assistant reads, reasons over, and uses to recommend the right bundle, answer the buyer’s objections, and—in many cases—complete the order. That’s why this isn’t “just SEO.” It’s the connective tissue between product truth, persuasive storytelling, and a new, conversational shopping surface.

The llms.txt debate: what’s real, what’s hype, and how to use it (safely)

Caleb Turner on October 9, 2025

Short version:
llms.txt is a proposal, not a formal web standard. Some sites ship it; some AI tools and SEO plugins promote it. Google says you don’t need it to show up in AI Overviews or any other Google products.

But: …like any public .txt file, an llms.txt URL can be crawled and indexed, so in rare cases a bare, unstyled text page could show up in search instead of your real, designed page.

I know of someone who created a playful file called cats.txt to make a simple point: Google can index plain text files if they’re publicly accessible and discoverable. In other words, the name doesn’t matter, if it’s a .txt and reachable, it can show up in search, just like any other indexable file type.

If a text file is publicly visible on your site, search engines can list it, which you certainly don’t want. To prevent this, send X-Robots-Tag: noindex in the HTTP response (works for non-HTML files), and if you want to point search engines to the right page, add a Link: <https://your-page>; rel=”canonical” header. Here’s what a real plain-text doc looks like in a browser (the kind that could hypothetically rank if you don’t block indexing):
 https://developers.cloudflare.com/llms.txt.

Google’s documentation covers both the noindex response header for non-HTML resources and using an HTTP canonical header.

Below is a clear walk-through, first in plain English (with analogies), then the technical details and safest practices.

What is llms.txt?

llms.txt is a simple text file you put at example.com/llms.txt. The file lists or summarizes your most important pages in Markdown, so AI systems can more easily read and use your content. Think of it like a cheat-sheet menu for bots: “Here are the dishes you should try; here’s how to ingest or interpret them.” It’s inspired by robots.txt and sitemaps, but it isn’t an official protocol like those are, just a community proposal.

Today, there’s no universal adoption. A directory of live implementations shows many developer-tooling/docs sites experimenting, often paired with a bigger llms-full.txt that expands the content.

John Mueller / Google’s stance:  In a conversation with Caleb and a few other colleagues, Google’s John Mueller made it clear what side of the debate he’s on. You don’t need llms.txt; the guidance is to keep following normal SEO practices.

Why is there a debate?

Why is there a debate

The “we don’t need this” camp

  • Google says it won’t use llms.txt for AI search results, so for many sites this is optional at best.
  • llms.txt isn’t a standard; support and behavior differ by bot. In other words, don’t expect consistent results.

The “we’re seeing activity in the wild” camp

  • Practitioners have shared examples and logs showing Google indexing llms.txt pages, alongside surges of bot hits when large platforms roll it out, because it’s just a public text file like any other.
  • That doesn’t prove Google uses llms.txt for AI search results. It only proves that public .txt files can be crawled and indexed, which Google’s docs have said for years.

A layperson’s guide to the technical bits

  • robots.txt vs noindex
    robots.txt     is like a bouncer who tells certain crawlers not to walk certain halls. It doesn’t guarantee your URLs won’t show up in the search phone book (the index). Pages blocked in robots can still be indexed by URL if they’re linked elsewhere. If you truly don’t want a URL in the index, use noindex, that’s a separate rule delivered in HTML or HTTP headers.
  • X-Robots-Tag and canonical
    For non-HTML files like .txt, the right place to control indexing is the HTTP response header.

    • X-Robots-Tag: noindex = “don’t list this address in Google.”
    • Link: <https://example.com/page>; rel=”canonical” = “if you do need to reference this, use our public address here.” (Google supports canonical in the header for non-HTML formats.)

  • User-Agent/UA and why blocking by UA isn’t enough
    “UA” is the name badge a crawler shows at the door (e.g., Googlebot, GPTBot). You can write per-UA rules in robots.txt, and major AI vendors document their UA strings.

But name badges can be forged; to be sure a request is truly a vendor’s bot, verify by reverse-DNS/IP, not just the UA string. Cloudflare has even accused some AI crawlers of stealth crawling (changing badges and IPs).

  • “Cloaking” and dynamic rendering
    Serving different content to bots than to users is a slippery slope. Google considers cloaking a spam tactic when bots and users see materially different things. Google also deprecated dynamic rendering (bot-only HTML) as a long-term approach. If you want a bot-friendly version, keep the substance the same as what people see.

The cannibalization risk (and how to avoid it)

The cannibalization risk (and how to avoid it)

Imagine your llms.txt ranks for a branded query. A searcher clicks and lands on a wall of plain text with no design, navigation or conversion paths. That’s the risk: poor UX and lost revenue. It’s not hypothetical, plain text files do get indexed, and practitioners have shown real examples of llms.txt and llms-full.txt pages in the index. We also showed an example earlier in this article.

Fix: keep llms.txt fetchable (so AI tools can read it) but non-indexable in web search with:

X-Robots-Tag: noindex

Link: <https://www.example.com/your-preferred-page>; rel=”canonical”

These are HTTP headers on the llms.txt response, not tags inside the file. This is the safest, standards-compliant way to prevent cannibalization while still letting crawlers pull your file.

If you still want to experiment: safest practices

If you still want to experiment: safest practices

1) Treat llms.txt as optional, and experimental

Ship it only if it supports real goals. Keep expectations modest; it’s a proposal, not a protocol.

2) Prevent web-search cannibalization

Serve HTTP headers on llms.txt (and llms-full.txt if you publish one):

  • X-Robots-Tag: noindex
  • Optional: Link: <https://www.example.com/the-main-URL>; rel=”canonical”

Think: “Please don’t list this file in the phone book; if you must reference something, here’s the main storefront.”

3) If you want to block certain AI crawlers elsewhere, do it the right way

  • In robots.txt, write rules per UA (e.g., User-agent: GPTBot). Vendors like OpenAI document their bot names.
  • For high-stakes data, verify IPs (reverse-DNS) because UA strings can be faked. Google documents how to verify Googlebot; similar logic applies to others.
  • Be aware: some AI bots have been accused of ignoring robots.txt or crawling stealthily, so consider edge-level blocking if needed (WAF/CDN).

4) What to put in llms.txt (if you use it)

Link to canonical, public pages that you want AI systems to cite: FAQs, policies, product specs, pricing explainer, and key how-tos. Keep it concise; don’t dump the whole site.

5) Instrumentation & monitoring

  • A .txt file is just raw text. Web browsers don’t run code inside it, so you can’t drop a JavaScript analytics snippet (like GA/GTAG) into a .txt and expect it to fire. Browsers only execute scripts when the content is served as a script/HTML type, not text/plain.

If you still want to see who’s fetching that file, look at your server or CDN access logs. Those logs list every request (time, IP, user-agent, URL, etc.), so you can count hits to /llms.txt even without JavaScript. Examples: Apache’s access log and Cloudflare Logs.

  • Watch Search Console: if a text file starts appearing in “Indexed,” revisit your headers. Google’s docs confirm indexing can occur even without crawling the content (e.g., when discovered by links).

6) Don’t block JS/CSS for Googlebot

If your SEO defense plan includes blocking scripts to hide unique content from AI, be careful: blocking JS/CSS broadly can break rendering in Google Search. If you must, target AI bots individually, not Googlebot

The bottom line (for decision-makers)

The bottom line (for decision-makers)
  • Not required: Google’s AI Mode doesn’t depend on llms.txt; normal SEO still wins.
  • Not a standard: It’s a proposal with uneven support. Useful for experimenting, especially for docs-heavy products; not a silver bullet.
  • If you try it, ship it safely:

    • Put it at the root.

      Keep it short and link to your best pages.

      Send X-Robots-Tag: noindex and, if helpful, a header canonical.

      Keep content parity; avoid UA-based “special versions” that diverge.

    • If you must block certain AI bots elsewhere, use per-UA robots rules plus IP verification at the edge; be aware of stealth crawlers.

If you want a place to start, you can base your evaluation on current adoption (developer docs ecosystems, directories of live files) and any internal log evidence you have about bot hits to llms.txt. Then decide whether it’s worth maintaining a curated cheat-sheet for AI, or whether your time’s better spent doubling down on structure, internal links, and copy, the proven levers. SEO Rank Media is among the leaders in the AI search conversation. Reach out to explore how we can set your brand up for the future.

The AI Era: Why Search Engines Aren’t Going Anywhere

Caleb Turner on September 22, 2025

There’s a common misunderstanding that large language models (LLMs) like ChatGPT or Gemini are replacing search engines. They aren’t. LLMs change how results are presented and explained, but the heavy lifting of finding, organizing, and ranking the web still belongs to search engines. In plain English: LLMs are the brainy librarians inside of a giant library; search engines are the library’s cataloging system that keeps track of every book, page, and shelf.

Below is a clear look at what each does, why they’re different, and why search is not only sticking around but also growing.

What search engines actually do (and why that matters)

Search engines run a huge, ongoing pipeline that works like this:

  1. Crawl: Automated bots (“crawlers”) visit web pages and take notes on what they find.
  2. Index: Those notes are stored in a gigantic, constantly updated catalog (the “index”).
  3. Rank & Serve: When you search, the engine looks up the most relevant pages in that index and ranks them using complex algorithms.

Google’s own documentation lays out this crawl → index → rank process in detail. If you’ve never read it, it’s surprisingly readable and shows the scope and complexity behind what looks like a simple search box. directly;

You can’t browse Google’s index directly, it’s proprietary and unimaginably large. You query it. If you own a website, you can see your slice of the index in Google Search Console’s Page indexing report, which shows which of your pages are in or out and why. Microsoft offers similar visibility in Bing Webmaster Tools, including a Sitemap Index Coverage report that flags reasons URLs are excluded.

This is the invisible machinery of the open web. It’s what makes it possible to find new content minutes after it’s published and to keep billions of pages ordered enough to be useful.

What LLMs actually do (and what they don’t)

LLMs are trained to predict and compose text. They’re excellent at summarizing, explaining, reformatting, and reasoning over information they’re given. But there are two common misunderstandings:

  • LLMs do not maintain a live, internet-wide search index. The model itself isn’t crawling the web in real time or keeping a searchable catalog of every page like a search engine does. When LLMs need fresh facts, they typically consult a search engine index. Meaning they call a search engine service (UI or API). The search engine then queries its own index, returns ranked results, and the LLM fetches a few of those pages and combines them into the answer it generates for the user. Google literally calls this “grounding with Google Search.”
  • “Browsing” ≠ “crawling.” What we just described is called retrieval and summarization, not operating a global crawler and index. OpenAI’s newer “deep research” mode, for example, plans multi-step lookups and shows sources. Again: retrieval plus synthesis, not running its own universal web index.

This distinction matters because it explains why LLM answers can be hallucinatory. Without a high-quality retrieval step (i.e., search), an LLM is just “guessing” based on training data that could be outdated or incomplete.

That said, ChatGPT (the product of OpenAI) now runs a real web crawler called OAI-SearchBot and maintains OpenAI’s own web index so it can discover pages and show them as cited sources in ChatGPT Search. Which again proves this article’s point: you still need search infrastructure under the LLM.

The winning combo: grounding LLMs with search

The industry term for blending search with generation is Retrieval-Augmented Generation (RAG). In RAG, the system first retrieves relevant documents from a trusted source (like a search index or an enterprise knowledge base) and then generates an answer that cites those sources. Requiring the AI search engine to cite its sources can also dramatically reduce hallucinations. The original RAG research popularized this approach in 2020, and it’s now widely used.

You’ll see this philosophy in multiple places:

  • Google Gemini / AI Overviews: “Grounding with Google Search” pipes real-time search results into the model and returns answers with citations.
  • Vertex AI: Google Cloud’s guidance explicitly recommends grounding model outputs in verifiable data, via Search, RAG, Maps, and more, to reduce hallucinations.

The big picture: LLMs are the presentation and reasoning layer; search is the fact-finding and verification layer. You need both.

The library and the librarian

Think of the web as a giant library:

  • The search engine builds and maintains the card catalog (the index). It constantly scans new “books” (web pages), decides where they belong, and keeps the catalog current.
  • The LLM is the librarian who reads the relevant pages you point to and then explains them in friendly language, weaving them into a clear, direct answer. If the librarian is allowed to cite the exact books and page numbers, you can check the work.

When the librarian doesn’t check the catalog first and just “remembers” what books might say, mistakes happen. That’s why modern AI features emphasize grounding and citations.

“But aren’t people just using AI instead of Google now?”

Short answer: no. AI usage is up, and Google Search remains massive and growing.

  • Alphabet’s earnings releases and CEO remarks throughout 2025 show double-digit growth in Search revenue and healthy overall query growth, including a 70% year-over-year jump in Google Lens searches, much of which is incremental (i.e., additional to traditional text queries). That’s expansion, not replacement.
  • Independent financial reporting backs this up: multiple quarters in 2025 attribute Alphabet’s outperformance partly to strength in core search, even as AI features roll out alongside it.

It’s also useful to separate revenue from queries. Revenue grows when users stay engaged and ads remain effective; queries grow when people search more, in more ways. Google has repeatedly highlighted growth in newer, multimodal behavior, like searching with your camera (Lens) or combined gestures, showing search is evolving rather than shrinking.

Why LLMs don’t (and shouldn’t try to) be search engines

  1. Freshness at web scale: The public web adds and changes billions of pages. Keeping a comprehensive, deduplicated, spam-resistant, and continuously updated index is a specialized, infrastructure-heavy job. It’s what search engines were built for.
  2. Transparency and provenance: When an LLM is required to cite sources, users can click and verify. This is standard in grounded systems like Gemini’s “Search grounding” and Vertex’s guidance. Purely generative answers can’t offer the same audit trail.
  3. Governance and site control: Website owners monitor their presence in the index through Google Search Console and Bing Webmaster Tools, diagnosing why pages are in or out. That visibility is essential for a healthy open web and isn’t replaced by a model’s internal training data.
  4. Commercial ecosystems: Search drives measurable, intent-rich traffic that businesses can analyze and optimize. That incentive structure sustains publishing and commerce broadly. The earnings results we’ve seen suggest these dynamics are holding, even as AI features appear in the interface.

What this means for everyday users

  • You’ll see more answers. AI summaries sit on top of search results and often include citations so you can dive deeper. Expect more multimodal options (speak, snap a photo, or draw a circle on your screen) that kick off a search behind the scenes.
  • Quality still wins. If you publish online, the fundamentals matter even more: sitemaps, clean site architecture, crawlability, canonical tags, structured data, and helpful content. Search engines need to index and rank your pages before an LLM can confidently cite them.
  • Trust but verify. AI answers can be great for speed and clarity, but when it counts, click through the citations. Even OpenAI’s more advanced research features emphasize sources precisely because models can still overstate or hallucinate details.

What this means for businesses and publishers

  • Search is still the discovery backbone. Alphabet’s 2025 results show search’s resilience and growth as AI features roll out; the pie is getting bigger, not smaller.
  • Optimize for being cited. When LLMs ground answers, they look for trustworthy, well-structured, crawlable sources. Make sure your pages are indexable and well-labeled so they’re retrieved and cited instead of a forum thread summarizing your work.
  • Expect new query types. Visual and voice-led searches are growing fast, often incrementally—meaning they’re additions to classic typed searches, not replacements. Prepare your content and product data (images, alt text, schema) to be useful in those contexts.

Quick FAQ

Do LLMs “crawl the web”?
No. The applications around LLMs may fetch pages when you ask a question, often via a search partner, but the models themselves don’t operate a global crawler and index like a search engine. Google’s own AI stack explicitly “grounds with Google Search.”

Can I see the web index somewhere?
Not directly. You can query it (e.g., with Google or Bing), and if you own a site, you can inspect your pages’ status in Google Search Console or Bing Webmaster Tools.

Isn’t AI going to reduce searches
Evidence to date suggests the opposite: search usage and revenue are growing while AI features roll out, and newer behaviors like Lens are expanding the pie.

So what’s the right mental model?
Search engines find and rank facts at web scale. LLMs present and reason over those facts. Together, they produce faster, clearer answers, with links you can check.

The bottom line

LLMs have not replaced search; they’ve changed its surface. Underneath any polished AI answer, the classic information-retrieval pipeline, crawling, indexing, retrieval, and ranking, is still doing the heavy lifting. Modern systems combine them: search grounds the answer; the LLM explains it. And if you look at 2025’s numbers and usage patterns, search isn’t going anywhere. It’s evolving, growing, and quietly powering the AI experiences we’re all watching unfold before our eyes. Reach out to SEO Rank Media if you want a partner who understands the direction search is headed and how to position your business to be at the forefront of the evolution.

AI API Test: See What AI Agents Read, Cite, and Trust

Caleb Turner on September 12, 2025

An AI API test shows exactly how AI search engines and assistants (like ChatGPT, Google’s AI Mode, and Perplexity) consume and reuse your content by giving them a clean, structured endpoint and logging what they ask for. Brands that run these tests learn how to feed AIs the right facts, earn more citations and brand mentions in answers, and protect revenue as clicks shift from traditional organic search to AI summaries (Pew finds link-clicks drop sharply when AI summaries appear, while Semrush reports AI searchers convert far better than classic search users).

Key takeaways

  • AI summaries reduce clicks to websites; visibility now means “being the answer,” not just ranking.
  • A simple “honeypot” API reveals what each agent asks, which sources it trusts, and how often it re-checks your data.
  • A dual-feed strategy wins: persuasive HTML for humans, structured JSON for AI agents.
  • Cloudflare Radar now tracks crawl-to-refer ratios by AI bot, evidence that many models consume more than they send back.
  • Semrush’s AI Visibility Index shows which sources each model leans on; community sites (e.g., Reddit) often dominate ChatGPT while Google’s AI Mode favors structured authorities. Optimize for both.

Detailed guide

What is an “AI API test” in plain English?

It’s a small experiment where you (1) detect likely AI agents, (2) offer them a machine-friendly API endpoint with verified facts about your products/services, and (3) log exactly which agents called it, what they asked, and how they used it. This gives you measurement, attribution, and a blueprint for fixing gaps in how AIs talk about your brand. In a field test summarized by Agent Berlin, teams used a honeypot endpoint and could see “fan-out” sub-queries from AI agents, proving what details matter most to machines.

Why is this suddenly business-critical?

Two shifts collided in 2025:

  1. Google’s AI summaries change click behavior—Pew shows users are less likely to click links when AI summaries appear, and they almost never click the cited sources (≈1% of visits). That is a direct threat to traffic.
  2. AI searchers who do engage are high-intent. Recent data reveals LLM search as a conversion engine and urges brands to “be the answer,” not just the blue link.

Translation: you can’t afford to guess what AIs read from your site. You need instrumentation.

What do recent large-scale studies say about AI visibility?

  • Pew Research (2025): ~18% of studied Google searches showed an AI summary; when summaries appeared, users clicked less and often ended the session sooner. Wikipedia/Reddit/YouTube dominate as cited sources.
  • Semrush AI Visibility Index (2025): Tracks brand mentions and cited sources across industries for ChatGPT vs. Google AI Mode and shows they lean on different source ecosystems; the microsite also surfaces “Top 10 Sources” per vertical, helping you prioritize where to earn citations.
  • Cloudflare Radar (2025): Adds AI-bot telemetry (crawl vs. refer) and documents industry worries about “stealth crawling” plus tools to control or charge for AI access. This reinforces why you should measure agent behavior directly with an API test.

How does an AI API test actually work?

How does an AI API test actually work?

At a high level: your server inspects requests (the “User-Agent” header, IP ranges, and other signals). If it looks like an AI agent, you give it short instructions pointing to a special endpoint (the honeypot). That endpoint returns clean JSON facts, product specs, pricing, policies, and store hours—tagged with an agent identifier so you can attribute calls. Tests have observed platform differences: Perplexity often follows API instructions directly, while ChatGPT tends to ask the user for permission first before calling outside APIs.

Is this “cloaking”?

No, if the structured JSON reflects the same facts humans see. You’re not hiding or manipulating content; you’re decluttering it for machines. Keep parity with your human-visible page, and document your intent in your privacy/robots notes.

What should I measure?

  • Call volume per agent (e.g., ChatGPT, Perplexity-User): trend lines reveal who’s reading you.
  • Query themes & “fan-out” sub-questions: Shape your FAQs and product pages around what agents actually ask.
  • Field-level usage frequency: Which JSON fields are read most (price, ingredients, warranty)?
  • Citations/mentions in AI answers: Did the model name your brand and/or cite your URL?
  • Crawl-to-refer ratio: Cloudflare’s new Radar views help you compare model consumption to referral behavior.

What does a minimal setup look like?

  1. Detect agents: check User-Agent and allow-listed IPs where possible. Perplexity documents PerplexityBot and Perplexity-User plus IP JSON; use that as a baseline.
  2. Redirect AIs to a clean endpoint: return a one-liner on the HTML page that politely tells agents to fetch /agents-api?src={agent}.
  3. Serve normalized JSON: consistent field names (e.g., price, in_stock, last_updated).
  4. Log & attribute: require an identifier (query param or header) and record IP/ASN where legal.
  5. Validate truth: cross-check JSON vs. the visible page; consider using time-stamped fields so you can prove freshness if an AI answers with stale data.
  6. Compare answers: ask the same questions on ChatGPT, Perplexity, and Google’s AI Mode and see if they reuse your fields or cite you.

Example / Template (copy & adapt)

Minimal agent endpoint JSON

{

  “brand”: “Acme Widgets”,

  “sku”: “W-42”,

  “price”: 19.99,

  “currency”: “USD”,

  “availability”: “in_stock”,

  “warranty_months”: 24,

  “last_updated”: “2025-09-10T12:00:00Z”,

  “source_url”: “https://www.example.com/widgets/w-42”

}

Attribution tip
Require ?agent= on the endpoint and accept X-Agent as a header fallback, which creates reliable logs even if a crawler spoofs a browser string. The aforementioned honeypot proved this strategy works in practice.

How do I turn test insights into more visibility and revenue?

How do I turn test insights into more visibility and revenue?
  • Give AIs exactly what they seek. If the logs show frequent questions about returns or materials, add those as discrete JSON fields and as scannable bullets on your HTML page. Better answers → more brand mentions and citations in AI summaries.
  • Win the sources AI trusts. Research and determine the top cited sources for your vertical; prioritize partnerships, PR, and content for those domains (e.g., Reddit threads, .gov guidance, and industry reviewers) to increase your odds of inclusion.
  • Treat AI visibility as a funnel. Even if clicks fall on AI summary pages, branded mentions and “as cited by” moments can lift assisted conversions, especially since LLM users tend to be higher-intent. Measure brand search and direct traffic after AI mention spikes.
  • Protect your margins. Cloudflare Radar’s crawl/refer telemetry plus access-control features help you decide who to allow, throttle, or charge, which is useful if training demand outpaces referral value.

How long should the test run, and what’s a “good” sample?

Run at least 2–4 weeks to capture weekday/weekend cycles and product updates. For smaller sites, aim for 100+ attributed agent calls; for enterprise, target 1k+ calls across at least two models. Pair logs with a weekly review of brand mentions in ChatGPT and Google AI Mode.

What about policy and ethics?

Honor robots and published bot IP lists; several providers (Perplexity, OpenAI) document user agents and controls. If you must block training, manage robots and WAF rules thoughtfully; Cloudflare offers features to segment/verify bots and even explore “pay per crawl.” 

Example Box: A dual-feed pattern you can pilot this week

  • Human page: persuasive copy, images, reviews, and rich FAQs.
  • Agent endpoint: normalized JSON of the same facts.
  • Post-deploy checks:
    1. Ask each model a buying-intent question (e.g., “Is W-42 waterproof?”).
    2. Observe whether your JSON field appears verbatim in the answer.
    3. Track if your brand/URL is named or cited.
    4. Adjust fields and repeat next week.

FAQs

Will this help or hurt my SEO?

It helps if the JSON matches what humans see. You’re clarifying, not hiding. AI visibility is its own discipline alongside SEO; treat it as a complementary layer.

What if an AI bot ignores robots?

Measure first (your logs), then decide whether to throttle, challenge, or block. Cloudflare Radar documents concerns and provides controls for AI bots. 

How do I know which sources to court for citations?

SEO Rank Media uses AI visibility tracking tools to see which domains each model most often cites in your niche. Cross-check the lists across tools and models, note the sites that appear repeatedly, and rank them by relevance and authority. We then prioritize outreach and content placements on those high-overlap sources, then monitor citations over time to refine the target list.

Does Perplexity publish its bot details?

Yes. Perplexity lists PerplexityBot and Perplexity-User, plus IP ranges you can allowlist. 

Checklist / TL;DR

  • Detect agents; route them to /agents-api.
  • Serve parity JSON with versioned, time-stamped facts.
  • Log agent, IP/ASN, path, and fields read.
  • Compare AI answers for citations/brand mentions weekly.
  • Target the sources each model favors in your vertical.
  • Monitor crawl-to-refer ratios; adjust allow/block rules.
  • Re-run quarterly as models and policies change.

Ready to win AI answers and local visibility?

Ready to win AI answers and local visibility?

Turn your AI API test insights into traffic, leads, and revenue. Contact SEO Rank Media for AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) services. We’ll:

  • Audit your site’s AI readiness and structured data
  • Set up & instrument your honeypot/AI API test
  • Build dual-feed content (human HTML + machine JSON)
  • Craft an AEO/GEO roadmap to earn citations in ChatGPT, Google AI Mode, and Perplexity
  • Align your source acquisition plan to what the models actually trust

Get started today with a quick strategy call; SEO Rank Media is ready to help.

How AI Mention Trackers Work: A Clear Guide to Understanding Visibility in Large Language Models

Caleb Turner on July 14, 2025

Artificial intelligence is becoming more deeply embedded in the way users search for, engage with, and consume information online. Businesses are now facing a new visibility frontier: large language models (LLMs) like ChatGPT, Claude, Google’s Gemini, and Perplexity. These AI tools are rapidly shifting how people discover brands and products, but there has long been a missing piece for marketers: how can you measure your brand’s presence across these tools?

Enter AI mention trackers—tools like Profound, Peec AI, and others that are helping brands figure out how often they appear in AI-generated answers. Think of them as the modern-day equivalent of media monitoring tools, but instead of scanning newspapers or websites, they scan what the AIs are “saying” about you. Let’s walk through exactly how these tools work, step by step, in simple and clear terms.

Step 1: Feeding Questions to AI Models

The first thing an AI mention tracker does is simulate real-world user queries. For example, if you sell coffee, it might generate prompts like:

  • “What are the best coffee brands for home brewing?”
  • “Which companies sell sustainable coffee beans?”

These questions are either preloaded by the tool or customized by the user. Then, the tool asks these questions to various AI platforms—ChatGPT, Claude, Perplexity, and others. These queries are sent using APIs or simulated browser sessions, mimicking the behavior of a real user.

To make the results more robust, the tool may vary how it phrases the questions, capturing a wider net of responses. This ensures the data reflects how real users might engage with AI tools.

Step 2: Collecting the AI’s Answers

Once the questions are submitted, the AI models reply with natural-language answers. The tracker collects all of these answers—a big pool of unstructured text. If the AI provides source citations or links (as Bing or Google often do), the tool grabs those too.

This phase is about capturing everything that the AI outputs, regardless of whether your brand appears yet.

Step 3: Detecting Brand Mentions

Now comes the scanning. The tool searches through each AI-generated answer looking for specific brand names, website URLs, or product terms. It checks to see if, for example, “Acme Coffee” or “acmecoffee.com” shows up in the text.

This is similar to a human pressing “Ctrl+F” and looking for their company’s name. The tool notes:

  • Where the mention appeared
  • How often it appeared
  • In what context (Was it a top recommendation? Just a mention in passing?)

If the brand doesn’t appear, that’s recorded too. These “non-mentions” are equally important because they show where the AI isn’t recognizing your brand.

Step 4: Counting and Aggregating Mentions

Counting and Aggregating Mentions

The tracker now tallies up the results across many queries and platforms. This helps quantify your brand’s visibility. You might learn that your brand appeared in:

  • 8 out of 20 questions on ChatGPT
  • 10 out of 20 on Google Gemini
  • Only 3 out of 20 on Bing Chat

These numbers are typically translated into metrics like “share of voice” (SOV) or mention frequency. Tools like Profound display this in an easy-to-read dashboard, comparing your visibility to your competitors.

Over time, this creates trend lines that show whether your brand’s AI visibility is improving or declining.

Step 5: Attributing Mentions to Sources

A crucial part of these tools is identifying why an AI mentioned your brand. In many cases, it’s because of external sources cited by the AI model. For example:

  • Bing Chat might footnote your brand with a link to a popular review site
  • Google’s AI Overviews might mention your company and cite your blog or Wikipedia

The tracking tool records these citations and links them to your mentions. This is called “citation analysis.” It helps you understand which articles, websites, or publications are fueling your AI visibility.

When an AI doesn’t mention you but mentions a competitor, these tools can also highlight what sources were cited for them. This gives you ideas about where you might need more coverage.

Step 6: Presenting the Results

Presenting the Results

All of this data gets organized into a simple dashboard. It might tell you:

  • Your brand was mentioned in 40% of answers about “best coffee brands” on ChatGPT this month
  • That’s up from 30% the month before
  • The most frequently cited source was HomeBarista.com
  • Competitor JavaWorld appeared more often than you on Google SGE

Some tools also analyze sentiment: whether the AI’s tone was positive, neutral, or negative about your brand. While more advanced, this adds another layer to understanding your visibility.

A Real-Life Example: Acme Coffee

Imagine you run a fictional brand called Acme Coffee. You want to know if AI tools are recommending you when people ask about coffee.

  1. The tracker sends prompts like “What are the best coffee brands?” to ChatGPT, Claude, Google Gemini, and Bing Chat.
  2. ChatGPT responds with: “Some great coffee brands are Acme Coffee, BeanCo, and JavaWorld.” The tool flags that Acme was mentioned.
  3. Google’s AI says: “According to HomeBarista.com, Acme Coffee roasts top-tier beans.” The tool notes the mention and attributes the source.
  4. Bing Chat doesn’t mention Acme at all but includes JavaWorld. That’s also important intel.

After querying multiple questions and platforms, the tracker produces a report:

  • Acme was mentioned in 7 out of 10 queries on ChatGPT
  • 5 out of 10 on Google Gemini
  • 3 out of 10 on Bing Chat
  • Most Acme mentions cited HomeBarista.com
  • JavaWorld beat Acme by 2 mentions across the board

Tools Like Ahrefs Add Another Layer

Tools Like Ahrefs Add Another Layer

Some platforms, like Ahrefs, take a slightly different but powerful approach. Rather than running queries in real time, Ahrefs leverages a vast existing database of AI responses and questions. You can type in a brand name or topic like “sneakers,” and instantly see a list of relevant queries and AI answers that reference the topic.

This lets you:

  • Identify competitor gaps (queries where your competitors show up but you don’t)
  • Discover new topic opportunities (queries you never thought of that relate to your niche)

This retrospective approach complements real-time trackers like Profound or Peec AI by giving you a broader strategic view.

Tracking LLM Traffic in GA4: Why It Matters

AI visibility isn’t just theoretical. Brands are already seeing meaningful traffic driven by AI tools. Tracking this traffic in Google Analytics 4 (GA4) is now essential.

While Google Search Console still blends AI Overview and AI Mode traffic with regular search, GA4 gives you tools to segment this data more precisely.

Two Main Tracking Approaches:

  1. GA4 Explore Reports:
    • Create a session segment using a custom regex filter to capture traffic from AI sources like ChatGPT, OpenAI, Copilot, Gemini, Perplexity, etc.
    • Visualize this data with line graphs, bar charts, or tables.
  2. Looker Studio Reports:
    • For detailed reports: Create a new channel group in GA4 for AI traffic.
    • For quicker views: Use the same regex filter in your Looker Studio tables and charts.

These dashboards let you:

  • Track how much traffic is coming from AI tools
  • See which pages are being visited from AI answers
  • Understand whether your AI visibility is translating into real engagement

Final Thoughts: Why This Matters

The future of search is increasingly conversational and AI-driven. Tools like Profound, Peec AI, and Ahrefs help marketers stay ahead by answering this crucial question:

“Are the AIs talking about me?”

If they are, great—you can double down on what’s working. If not, you can take action to increase visibility by improving the content on sites that AIs pull from.

AI mention trackers give marketers, PR pros, and SEOs a crucial lens into how modern algorithms perceive and recommend their brands. By bridging the gap between traditional SEO metrics and AI-powered search behaviors, these tools ensure your strategy remains both measurable and forward-looking.

Start tracking now, and you’ll not only see how often you appear in the AI conversation, you’ll start shaping it.

How do modern AI search engines and LLMs operate and how do you optimize for them?

Caleb Turner on June 2, 2025

This isn’t 2015 anymore, yet some SEO “experts” are still clinging to tactics like they’re waiting for Windows 7 to make a comeback. Modern AI-powered search engines and large language models (LLMs) leverage Retrieval-Augmented Generation (RAG) to combine external data retrieval with text generation, ensuring answers are both current and contextually accurate. By performing a real-time search of trusted documents before crafting a response, these systems mitigate outdated training data and “hallucinations.” To optimize for them, create clear, structured content with up-to-date citations, conversational Q&A headings, and appropriate schema markup, so AI retrieval steps can easily identify and quote your material.

Key Takeaways

      • RAG enables AI to fetch and ground answers in fresh, external sources.

      • Structured Q&A headings and bullet points improve AI snippet retrieval.

      • Embedding authoritative, date-stamped references boosts trust signals.

      • Conversational phrasing and varied keywords aid vector-based matching.

      • Schema markup (FAQPage, HowTo) helps AI isolate self-contained snippets.

      • Off-page promotion can still surface in AI searches.

      • Optimizing content for RAG-driven AI results increases probability to appear in AI summaries and chatbot responses, giving you traffic that static search rankings might miss.

    Detailed Guide

    What is Retrieval-Augmented Generation (RAG) in simple terms?

    retrieval augmented generation

    Retrieval-Augmented Generation (RAG) is a hybrid AI workflow that enhances language models by letting them “look up” relevant documents at query time, rather than relying solely on what they learned during pretraining. Imagine asking a librarian to fetch the latest journal article before answering your question; RAG works similarly. Except this librarian is more like Alexa or Siri than your stereotypical Miss Finster.

    When you submit a query, the system first searches an external data source, such as a website index, a private knowledge base, or a specialized dataset of academic papers, for pertinent passages. Then, it feeds those retrieved snippets into the LLM as additional context, guiding the generative process so the answer is grounded in factual, up-to-date material. This approach addresses two major limitations of standard LLMs: information cutoff dates and the risk of “hallucinations,” where the model invents plausible-sounding but incorrect details.

    How does the retrieval phase work?

        1. User Query Submission
          You ask a question—e.g., “What are the 2025 tax deadlines for small businesses in Texas?” The RAG-enabled system takes this natural-language query as input.

        1. External Search
          Instead of directly generating an answer from pretraining data, the system performs a search against an external document collection, which could be a public web index, a company’s internal file repository, or a specialized dataset of academic papers (AWS, 2024; WEKA, 2025).

        1. Result Ranking
          Retrieved documents or text snippets are ranked by relevance using vector similarity, which transforms both the query and documents into numerical embeddings, or traditional keyword-based matching. The top N results (often broken into smaller “chunks” of text) are selected based on how closely they align with the user’s question.

        1. Outcome
          At the end of this phase, the system holds a set of highly relevant, often date-stamped passages that directly address the query.

      How does the augmentation and generation phase work?

          1. Context Assembly

        The RAG engine takes the top-ranked snippets—sometimes as short as a few sentences each—and concatenates them with the original user query. This assembled context is fed into the LLM.

            1. Guided Response Generation

          Rather than “freewriting” from its pretraining knowledge, the LLM now “reads” the assembled context and composes an answer that weaves together facts from the retrieved snippets with its own linguistic patterns. It essentially uses the retrieved passages as anchors, ensuring that every factual statement can be traced back to a specific external source.

              1. Optional Citation Insertion

            Some RAG implementations explicitly insert inline citations or footnotes, indicating which document or page each fact originates from. This enhances transparency and credibility, especially in domains like healthcare or legal research.

                1. Outcome

              The final output is a coherent, conversational response that is both fluent and verifiably sourced—reducing the likelihood of “hallucinations”.

              Why does RAG matter?

                  • Accuracy and Currency

                Because RAG fetches fresh data at query time, it can provide up-to-the-minute answers—even if the underlying LLM was last trained months or years ago. For example, a healthcare AI using RAG can retrieve the latest CDC guidelines before generating a recommendation, rather than relying on outdated training data.

                    • Reduced Hallucinations

                  By grounding responses in concrete, external sources, RAG dramatically lowers the risk of fabricated or misleading information. When users see inline citations, trust in AI-generated answers increases.

                      • Domain Specialization

                    Organizations can connect RAG systems to highly specialized knowledge bases—like a law firm’s case archives or a manufacturer’s product specs—without retraining the LLM. The AI becomes an expert in that domain simply by accessing the right repository at query time.

                        • Cost Efficiency

                      Instead of fine-tuning a massive LLM every time new information is added, you update the external datastore. This “decoupling” of model training from content updates is faster, cheaper, and more scalable—especially for companies that produce time-sensitive reports or whitepapers.

                          • Competitive Differentiation

                        As Google’s “AI Mode” is rolled out on a more massive scale, organizations that optimize for RAG-driven visibility gain a strategic edge. Their content is more likely to be surfaced in AI-generated summaries and chatbot answers, capturing traffic that might otherwise bypass static search engine results.

                        How to optimize content for RAG-driven AI search engines?

                        Google EEAT

                        Optimizing for RAG workflows means ensuring your content is structured, authoritative, and easy for retrieval algorithms to pinpoint. Below are actionable tactics:

                        1. Craft Clear, Structured, Answer-Focused Content

                        AI retrieval steps look for self-contained “snippets” that directly match user queries. Use semantic headings for primary sections so AI bots can isolate exact sections to quote. Begin each section with a concise answer.

                        For example:

                        How to File Sales Tax in California (2025 Update)

                        As of June 2025, all California small businesses must file sales tax returns by the 15th of each month. Refer to the California Department of Tax and Fee Administration website for exact forms.

                            • Use bullet lists and numbered steps for procedures to enhance snippet eligibility.

                            • Include a “TL;DR” summary at the top of long articles so RAG systems can grab that concise overview.

                          2. Embed Up-to-Date, Authoritative References

                          RAG systems ground their output in trusted documents. Pages that cite reputable, recent sources—such as government websites, peer-reviewed journals, or industry white papers—signal higher trustworthiness.

                              • Link to the latest guidelines or studies with a clear “Last Updated” date.

                              • Regularly audit and update publication dates to maintain freshness, benefiting both human readers and AI bots.

                            Example:
                             “According to the CDC’s May 2025 update on COVID-19 guidelines, mask mandates for healthcare workers in high-risk settings remain in effect (CDC, May 2025).”

                            3. Use Conversational Phrasing and Natural-Language Keywords

                            RAG retrieval often relies on vector-based similarity, matching semantic meaning rather than exact keywords. Write headings as questions users would ask—e.g., “What Are the 2025 Tax Deadlines for Freelancers in Texas?”—and follow with an immediate, concise answer.

                                • Include synonyms and related terms, such as “self-employed tax due dates” and “independent contractor tax deadlines,” to create multiple semantic entry points.

                                • Adopt a conversational tone so your content aligns with how AI systems interpret queries, boosting retrieval probability.

                              4. Leverage Schema Markup and FAQ/HowTo Blocks

                              Structured data markup—like FAQ Page or How To schema—helps AI crawlers precisely identify Q&A pairs and step-by-step instructions.

                                  • Wrap each Q&A pair in FAQ Page JSON-LD so RAG systems know these are self-contained snippets.

                                  • Use How To schema for multi-step guides, clearly delineating each step.

                                When Google’s AI Mode or other RAG-enabled platforms crawl your page, they can directly parse these structured blocks without scanning raw text.

                                5. Build Topical Authority and Maintain a Clean Technical Foundation

                                RAG systems prefer content from authoritative domains with strong topical clusters.

                                    • Publish comprehensive guides that interlink subtopics, demonstrating subject-matter depth.

                                    • Acquire backlinks from reputable industry publications—these act as trust signals in both traditional SEO and AI retrieval scoring.

                                    • Optimize technical SEO: ensure fast page load times, mobile responsiveness, secure HTTPS hosting, and accurate XML sitemaps so crawlers can index every relevant page.

                                  Tip: Use tools like Google Search Console to verify your sitemap and crawling status. If pages are excluded, AI retrieval systems won’t be able to find your snippets, regardless of content quality.

                                  6. Monitor and Adapt to AI Search Analytics

                                  Once your content is live, track AI-driven search performance via analytics platforms that show which snippets are being cited in chatbot outputs or AI summaries.

                                      • Review query logs to identify gaps and update content accordingly.

                                      • Refresh your knowledge base and schema markup periodically to keep pace with algorithmic changes.

                                    By treating optimization as an ongoing process rather than a one-time project, you ensure continual visibility in evolving RAG-driven ecosystems.

                                    7. Incorporate Off-Page SEO And PR Tactics for AI Visibility

                                    Traditional digital PR often promoted press releases, link-building or aggressive directory submissions. In certain AI search contexts, off-page tactics, like creating press releases or being cited on article directories, can cause RAG systems to index multiple instances of your content, increasing the likelihood of snippet selection.

                                    In my short YouTube video, I demonstrate how these tactics, some of which may be called “spammy”, can boost visibility in AI-based searches by flooding the retrieval index with relevant signals. While this approach carries risks in traditional SERPs, it can yield surprisingly effective results in AI-driven environments—so long as you monitor for negative user feedback or credibility issues.

                                    FAQs

                                    What is the difference between RAG and a standard LLM response?

                                    A standard LLM generates answers based solely on its pretraining data, which may be outdated if trained months ago. RAG, by contrast, performs a real-time search of external documents before generating an answer, ensuring the information is up-to-date and grounded in factual sources.

                                    Can I use RAG to search proprietary company files?

                                    Yes. By connecting a RAG-enabled system to your internal knowledge base—such as a SharePoint repository or a private document store—your organization can get highly specialized answers rooted in proprietary data without retraining the entire model.

                                    How do schema markup and structured data help AI retrieval?

                                    Schema markup like FAQ Page or How To tells AI crawlers exactly where Q&A pairs and step-by-step instructions begin and end, so retrieval engines can extract self-contained snippets without scanning the entire page. This increases the chances of your content being quoted verbatim in AI-generated summaries.

                                    Checklist

                                        • Identify and segment core Q&A snippets with clear semantic headings.

                                        • Embed date-stamped, authoritative citations (e.g., government or peer-reviewed).

                                        • Use conversational, question-style headings and varied synonyms.

                                        • Apply FAQ Page or How To schema markup around structured content.

                                        • Ensure fast load times, mobile optimization, and valid XML sitemaps.

                                        • Monitor AI search analytics to track snippet performance and update.

                                        • Experiment with off-page snippet postings; measure AI retrieval impact.

                                      Brief Summary and Conclusion

                                      Modern AI search engines and LLMs harness RAG workflows to merge external data retrieval with text generation, often producing answers that are highly accurate and current. By structuring content with clear semantic headings, embedding up-to-date citations, using natural-language Q&A phrasing, and applying FAQ Page or How To schema, you make it easier for AI retrieval to spot—and quote—your material without resorting to a virtual game of hide-and-seek. 

                                      Building topical authority, maintaining strong technical SEO, and even testing off-page snippet tactics can further boost your visibility in AI-driven searches. As AI search evolves, continually monitoring and adapting your strategy will be crucial for long-term success in the RAG-powered landscape.

                                      How do you optimize for Google’s new AI‑Mode answer summaries?

                                      Caleb Turner on May 28, 2025

                                      Google now runs two separate generative‑AI surfaces inside Search: AI Overviews (a quick snapshot embedded in the classic results page) and AI‑Mode (a standalone, Gemini‑powered tab that behaves more like a research assistant). To earn citations in either, you still need strong ranking signals, iron‑clad E‑E‑A‑T and snippet‑ready prose, yet the tactics differ enough that you must optimise for both layers.

                                      Key Takeaways

                                      • AI Overviews ≠ AI‑Mode. Overviews are inline snapshots; AI‑Mode is an opt‑in, dedicated search mode with deeper follow‑ups.
                                      • Overviews appear automatically when Google’s systems deem a query complex enough and safe; AI‑Mode is user‑initiated via a new AI tab.
                                      • Ranking top‑10 still matters—Overviews pull from high‑ranking, verified documents first.
                                      • Put a 60‑–80‑word hero answer under every H1 to maximise extractability.
                                      • E‑E‑A‑T + freshness remains the admission ticket for both layers.
                                      • Expect CTR to fall on Overview queries; offset with branding and lead magnets.

                                      Detailed Guide

                                      1. How do AI Overviews and AI‑Mode actually differ in 2025?

                                      Feature AI Overviews (inline) AI‑Mode (standalone)
                                      Launch timeline US rollout May 14 2024 → 100+ countries Oct 2024  US mass rollout May 20 2025 after Labs testing 
                                      Interface Appears above organic links inside standard SERP; collapsible; cites sources as chips Separate AI tab or toggle; full‑screen conversational UI; shows citations plus follow‑up prompts
                                      Use‑case Quick snapshot for moderately complex “how/why” questions Deep research, multi‑step planning, agentic tasks (e.g., buying tickets, data comparisons)
                                      Trigger Automatic—requires query to meet content‑safety + complexity thresholds Manual—user selects AI‑Mode; no popularity threshold
                                      Model Gemini 2.x tuned for latency Custom Gemini 2.5 with query fan‑out + Deep Search

                                      Why it matters: Overviews reward concise clarity; AI‑Mode rewards depth and interactivity.Optimise pages to satisfy both in one pass: lead with a distilled answer, then dive deep.

                                      How do AI Overviews and AI‑Mode actually differ in 2025

                                      2. When does Google show an AI Overview?

                                      Google has never published exact numbers, but data from SE Ranking and Search Engine Land suggest that queries need both sufficient search volume and a level of informational complexity.

                                      Guideline: Pages that already rank for queries with ≥100 monthly US impressions and 8‑plus words are far more likely to trigger an Overview.

                                      What This Means for SEO & Content Strategy

                                      SEO Moves

                                      1. Track impression‑heavy question keywords in Search Console.
                                      2. Consolidate overlapping articles—one URL per FAQ.
                                      3. Refresh answers quarterly to keep Overview eligibility.

                                      3. Crafting the hero answer—your 80‑word golden ticket

                                      A well‑formed hero paragraph can surface in both Overviews and the first AI‑Mode answer.

                                      • Length: 60–80 words, two sentences max.
                                      • Structure: statement → key fact → source cue (stat/name).
                                      • Branding: mention brand once in first clause.
                                      • Location: immediately after H1, above any images or ads.

                                      Copy hack: Draft two variants (60 w & 80 w) and alternate every 14 days to compare CTR. 

                                      4. Two‑phase summarisation still underpins both layers

                                      Google’s 2024 patent describes an espresso (fast) and slow‑brew (deep) retrieval loop Overviews rely mostly on espresso; AI‑Mode can wait for slow‑brew and even expand with Deep Search, issuing hundreds of sub‑queries.

                                      5. Verification signals—earning the invite

                                      Both systems filter the candidate set to verified documents before prompting the model. Signals include:

                                      1. Authorship credentials with professional links.
                                      2. Citations to primary research (government, peer‑reviewed, corporate filings).
                                      3. Structured data—Article, FAQPage, HowTo, FactCheck.
                                      4. Fresh timestamps and frequent updates for YMYL topics.
                                      5. Fast Core Web Vitals (LCP < 2.5 s; INP < 200 ms).

                                      6. Snippet engineering—teaching robots to skim

                                      Robots skim like distracted humans. Help them:

                                      • ≤ 3‑sentence paragraphs; no walls of text.
                                      • Bullet or numbered lists for steps.
                                      • Definition call‑outs (> blockquote or styled div).
                                      • Question‑form headings to mirror Google’s reformulation: “How does…”

                                      7. Technical hygiene—speed still kills eligibility

                                      Even the smartest model aborts slow pages:

                                      • LCP < 2.5 s (espresso cut‑off).
                                      • INP < 200 ms.
                                      • Serve images in AVIF/WebP and lazy‑load below the fold.

                                      8. Branding inside the snippet—CTR insurance

                                      Because Overviews often satisfy intent without a click, brand recall is your safety net:

                                      1. Put brand in the first 50 characters of <title>.
                                      2. Use a distinctive favicon.
                                      3. Embed a next‑step teaser (“Download the checklist”) below the hero paragraph.

                                      9. Measuring success across both layers

                                      Metric Overviews AI‑Mode Target
                                      Impressions vs. Clicks ▼ CTR N/A* Monitor 30‑day delta
                                      Branded search volume ↑ if citations recall brand ↑ via deeper engagement +5 % YoY
                                      Scroll depth & dwell time Standard Longer sessions ≥ 90 s
                                      Assisted conversions Post‑click purchases Research assist → return Attribute multi‑touch

                                      *AI‑Mode traffic logs separately in Search Console’s AI tab (beta).

                                      10. Pitfalls to avoid

                                      • Burying answers under anecdotes
                                      • Splitting one FAQ across multiple URLs
                                      • Out‑of‑date stats (Overviews drop stale pages fast)
                                      • Ignoring long‑tail queries that still deliver clean clicks

                                      Example / Template

                                      <!– 74‑word hero snippet under H1 –>

                                      <p>Google’s AI‑Mode shows a fully cited answer in its dedicated tab, while AI Overviews

                                      surfaces a concise snapshot above organic links. Rank in the top‑10, write a

                                      60–80‑word solution here, and back it with expert citations to earn both source

                                      chips.</p>

                                      FAQs

                                      Will AI‑Mode kill my CTR?

                                      AI‑Mode sits behind a tab, so only sessions where users opt in bypass organic links entirely. AI Overviews is the bigger CTR threat, trimming clicks by 10‑25 % on affected queries. Mitigate via branded teasers and interactive assets.

                                      Is schema markup still worth the effort?

                                      Yes—FAQPage, HowTo, and FactCheck schema mirror the AI layers’ Q&A structure, accelerate verification, and can trigger rich snippets when no AI answer shows.

                                      Does AI‑Mode penalise affiliate sites?

                                      No direct penalty, but thin, boiler‑plate reviews rarely count as verified. Add first‑hand photos, test data and disclosure labels.

                                      Can I opt out of AI answers?

                                      No. Blocking Googlebot removes you from Search entirely. Instead, lean in—optimise hero snippets, strengthen branding, and turn AI citations into authority signals.

                                      Ten‑Point Action Checklist

                                      • Audit recurring FAQs and rankings.
                                      • Write 60‑80‑word hero paragraphs.
                                      • Add author credentials, citations, fact‑check schema.
                                      • Use question‑form H2/H3s.
                                      • Break answers into ≤ 3 sentences & lists.
                                      • Hit LCP < 2.5 s, INP < 200 ms.
                                      • Build expert backlinks.
                                      • Monitor AI citations and AI‑Mode sessions.
                                      • Refresh content quarterly (monthly for YMYL).
                                      • Track impressions, brand queries, conversions.

                                      AI Mode Is Here: What Google’s New Tab Means for Your Business & Your Next Search

                                      Caleb Turner on May 22, 2025

                                      Why This Announcement Deserves a Coffee Break

                                      Google doesn’t trot out a brand-new search interface every Tuesday. This week Google flipped the switch on AI Mode, a dedicated tab now rolling out to every U.S. Google user. It had been rolled out on a small scale for some time, but only for certain users. LLMs like ChatGPT and Perplexity have been stealing some of Google’s spotlight in recent months, and now the king of search is back with a vengeance.

                                      For marketers, this isn’t mere gadget chatter. When the world’s largest traffic source decides to converse with your customers before it even shows them your blue link, pipeline math changes overnight. Today we’ll translate the launch into plain English, and lay out the moves your brand should make before AI Mode becomes the default mode.

                                      Why This Announcement Deserves a Coffee Break

                                      So… What Exactly Is AI Mode?

                                      Picture the ChatGPT box fused directly into Google’s results. When you tap the new tab, you can still ask for links, but the interface leads with a Gemini-generated answer—complete sentences, citations, images, sometimes even step-by-step “how-tos.” Under the hood lives Gemini 2.5, Google’s latest large-language model tuned specifically for search reasoning.

                                      Three showcase capabilities shipped on day one:

                                      1. Deep Search – Google explodes your prompt into dozens of micro-queries, crawls for a minute or two, then returns a synthesized briefing. Imagine an intern who reads 40 sources while you sip espresso.
                                      2. Project Mariner – An “agentic” tool that can click around sites, compare prices, even fill in checkout forms once you approve. (Cue every affiliate marketer’s raised eyebrow.)

                                      Search Live – Point your phone camera at a leaky faucet or an abstract painting and have a real-time chat about it—like Lens on steroids.

                                      AI mode gemini

                                      Personalization vs. Privacy: The Summer Opt-In Tango

                                      Google also confirmed AI Mode will soon use your past searches and, if you allow it, Gmail content to shape replies—“Hey, you’re vegetarian, here are plant-based options near your hotel.” Privacy hawks get an off switch arriving later this summer.

                                      From a brand perspective, this means the AI’s first impression of you depends not only on on-page SEO but on a customer’s entire Google history. If your nurture emails land in Promotions and your loyalty offers live in a PDF nobody opens, the new assistant may simply… overlook you.

                                      From Search Query to Digital Concierge: Why Businesses Should Care

                                      AI Mode is more than a UI facelift; it accelerates a behavioral shift already visible in our analytics:

                                      Old Journey

                                      Emerging Journey

                                      User Googles “best CRM for dentists,” scans 10 links, fills demo form on your landing page.

                                      User asks AI Mode “Which CRM fits a five-chair dental office? My staff hates spreadsheets.” Assistant summarizes options, cites you, and—if Mariner is enabled—books your calendar link in one click.

                                      Three implications:

                                      • Zero-Click Conversions – Inquiry, comparison, and commitment can occur inside Google’s walls. Your site may never load, but revenue can still flow. Cue updated attribution models.
                                      • Brand Mentions Trump Rankings – The AI quotes sources. If you’re the authoritative voice the model trusts, you’ll surface even when you’re not ranking first.
                                      • Content Utility Beats Content Volume – AI Mode compresses multiple pages into one answer. Ten fluffy blog posts may collapse into a single unsupported sentence, whereas one definitive guide earns repeated citation.

                                      SEO When Ten Blue Links Shrink to Five (or None)

                                      Our team monitors 40+ client properties. Early tests show AI Overviews display roughly five organic URLs on average, not ten. AI Mode pushes those links even further down—sometimes below the first screen-height.

                                      Action items for the rank-obsessed:

                                      1. Schema or Bust – If your site still thinks schema is a Greek island, it’s time for a wake-up call. Structured data gives Gemini the context it needs to name-drop you.
                                      2. Entity Clarity – Write for the knowledge graph, not just the keyword list. Distinct product specs, founder bios, location markers—feed them plainly to Google so it can “remember” you in follow-up chats.
                                      3. Source-Level E-E-A-T – Build expertise pages, author profiles, and citations. The model chooses voices it trusts; credentials now sit closer to the conversion funnel than meta descriptions ever did.

                                      Paid Media: Welcome to AI Max Economics

                                      Google didn’t bury the lede: if AI answers searches  organic real estate, you want to be found. There is also AI Max for Search Campaigns, a one-click bundle layering Performance Max with smarter bidding signals straight from AI Mode interactions.

                                      Expect:

                                      • Conversation-Native Ads – Sponsored snippets inside the AI response itself (“According to Acme Insurance—Ad”). Label stays, placement changes.
                                      • Deeper Audience Insight – If a prospect lets Gemini peek at Gmail receipts, your look-alike modeling just leveled up (ethics debates pending).
                                      • Site-Less Conversions – Local actions (calls, bookings, purchases) fire within Google’s UI. Budget for the value, not just the click.

                                      Tip: synchronize SEO and PPC teams. In an AI interface, the line between organic citation and paid insertion blurs; consistent messaging cushions the handover.

                                      Practical Checklist: How to Prepare in 30 Days

                                      Week 1 – Technical Hygiene

                                      • Audit schema (FAQ, How-To, Product).
                                      • Ensure JavaScript content is server-side rendered—bots won’t run your carousel code.

                                      Week 2 – Content Refactor

                                      • Merge overlapping posts into pillar pages; brevity plus depth beats serial redundancy.
                                      • Add “expert takeaway” callouts—Gemini loves bulleted insight quotes.

                                      Week 3 – Data & Ads Alignment

                                      • Import offline conversions into Google Ads; AI Max optimizes toward revenue, not leads.
                                      • Update first-party audiences; customer lists sharpen AI targeting.

                                      Week 4 – Experience Testing

                                      • Query AI Mode for your primary topics. Screenshot results weekly to spot citation gaps.
                                      • Pilot Mariner-friendly flows—structured product feeds, calendar APIs, schema-enhanced checkout.

                                      Bonus: draft an opt-in pitch for customers explaining how sharing Gmail data with Google can personalize their future deals with you. Transparency beats creepy any day.

                                      Environmental Footnote (Because Someone Has to Ask)

                                      Training Gemini 2.5 isn’t carbon-free. Google remained mostly mum on energy impact yesterday, and we’d be remiss not to flag the elephant in the server farm. Sustainable hosting, efficient code delivery, and carbon-offset campaigns still matter—perhaps more when your site loads fewer times but your brand equity depends on saying the right thing.

                                      Final Takeaways for Brands and Curious Humans

                                      1. AI Mode is optional today but foundational tomorrow. Treat it as a lab only if you’re comfortable ceding mindshare to faster competitors.
                                      2. Authority travels farther than position. Become the quoted expert, not the forgotten hyperlink.
                                      3. User trust is your moat. Deliver real value, disclose data use, and your brand can ride this wave instead of wiping out.

                                      Change in search is rarely polite. Yet with a strategic blend of technical polish, authoritative content, and ethical customer communication, your business can meet Google’s new concierge at the door—then politely hand it a branded welcome packet.

                                      (Now, back to that coffee. Your intern—er, Gemini—has already summarized three competitor white papers while you read this.)

                                      GEO (Generative Engine Optimization): Mastering AI Search with the G.E.O.D.A.T.A. Framework

                                      Caleb Turner on March 29, 2025

                                      Generative Engine Optimization

                                      Remember the good days of SEO? Where you could cram in a few related keywords into your website content and Google would (maybe) reward you with top-ranking glory? 

                                      These were simpler times, and now we unfortunately find ourselves waving goodbye to the simplicity of it all. These days, AI-driven search tools like those found in ChatGPT, Claude, and Perplexity are re-writing the playbook (or just setting it on fire). 

                                      For businesses, the SEO game has changed.

                                      It’s not just businesses pulling their collective hair out over this. Searching online as a regular human being has turned into an Olympic-level patience test. You type a question into Google and rather than getting a helpful answer, you’re bombarded with ads masquerading as advice. Those of us who have recently made use of AI-driven search have discovered a little secret: AI can sometimes answer our questions better than Google ever could. 

                                      Welcome to the Future of Search (or How We All Lost Our Minds)

                                      So, how do we fix this? Well, we don’t. Instead, we adapt to this new wave of search technology that’s fast becoming a survival strategy for brands that need to stay relevant. 

                                      Say hello to Generative Engine Optimization (GEO) — a new lifeline for traditional SEO experts feeling the sting of AI-driven search. GEO offers more than merely surviving the noise but allows your brand to stand out where it matters the most, with visibility that actually counts. 

                                      The G.E.O.D.A.T.A Framework from SEO Rank Media is a seven-step strategy that covers everything from ensuring bots can crawl your content to dealing with those AI “hallucinations” where facts go to die. 

                                      Instead of fighting the system, make it work for you. If you’re ready to drop the SEO tricks of yesterday and learn more about GEO, let’s get started.

                                      The G.E.O.D.A.T.A. Framework

                                      AI search platforms like ChatGPT, Claude, and Perplexity have opened up a whole new world for businesses to connect with audiences. Sounds great, right? But here’s the twist—this isn’t “business as usual” SEO anymore. 

                                      If your strategy is still clinging to Google SERPs like a security blanket, you’re already behind the curve.

                                      That’s where the G.E.O.D.A.T.A. Framework comes in. Developed by SEO Rank Media, the framework gives your business a head start in the AI-driven search arena.

                                      What makes the G.E.O.D.A.T.A. Framework different?

                                      1. Practical from Day One: Each step is clear and actionable—you can actually do something with it.
                                      2. Bigger Than AI Rankings: Sharpen your overall marketing game.
                                      3. Team-Friendly: Easy enough to explain to your boss, clients, or that one coworker who still doesn’t “get” AI.

                                      Why Bother with a Framework?

                                      The field is no longer about simply “ranking in Google.” Today’s search environment demands leadership and strategy. Brands need guidance to navigate:

                                      • How to perform across multiple AI search platforms.
                                      • What kind of content to produce to engage these platforms.
                                      • Where and how to distribute content to maximize visibility.

                                      The Steps of G.E.O.D.A.T.A.

                                      The framework outlines a step-by-step process to align your content and search strategies with the AI-dominated world. Each step builds on the last to ensure your brand is positioned for success:

                                      1. Gather Intelligence – Know what’s happening in the AI search world.
                                      2. Evaluate Accessibility – Make sure bots can actually find your stuff (duh).
                                      3. Optimize Brand Presence – Be unforgettable, or at least noticeable.
                                      4. Develop Sentiment – Build a brand people (and AI) actually like.
                                      5. Analyze Competitors – See what’s working for them and learn.
                                      6. Target Data Sources – Be where the algorithms are pulling from.
                                      7. Answer Accurately – Deliver real answers, not fluff.

                                      1. Gather Intelligence

                                      Tools like ChatGPT and Claude are shaping the way people perceive your business, whether you’re aware of it or not. So, understanding how these AI platforms view your brand is a big deal. If AI gets it wrong, like misrepresenting your brand or offering answers that aren’t very accurate, you’re left with customers who are judging your offerings based on bad info. 

                                      So, how do these AI platforms know what to say about you? It all comes down to the data they have been trained on. AI pulls from all sorts of sources, including:

                                      • Websites, blogs, and forums (including user-generated forums).
                                      • Search Engine Results Pages(like Google.com)
                                      • Social media chatter
                                      • Structured datasets like Wikidata
                                      • Specialized integrations like OpenAI’s via links like Microsoft. 

                                      Ai synthesizes all this information and uses it to generate answers. The quality of those answers depends heavily on the data available. If your brand isn’t well-represented, or worse, represented inaccurately, the AI delivers those misleading results—with confidence.

                                      So the first step is simple: start asking questions. Fire up an AI tool like ChatGPT and test the waters with queries like:

                                      • “What is [Your Brand]?
                                      • “What does [Your Brand] offer?
                                      • Is [Your Brand] trustworthy?”

                                      Pay close attention. Does the AI accurately summarize your business? Are there outright inaccuracies? 

                                      Armed with these insights, you can identify where your messaging needs to improve and take steps to fix it. This isn’t guesswork, it’s actionable intelligence, and the very foundation of effective GEO.

                                      2. Evaluate Accessibility

                                      There’s been a lot of chatter lately about blocking AI from crawling websites—like letting bots read public information somehow equals grand theft data. Unless you’re sitting on government secrets (which shouldn’t really be public in the first place), blocking AI does more harm than good.

                                      AI platforms use bots to crawl sites to get data for their models, the same way Google does. The difference is Google relies on structured indexing, and AI pulls data from a wider range of sources. 

                                      If you want to show up in AI search results, then you need to give these bots access to your page. It’s as simple as that. 

                                      Start by checking your robots.txt, the gatekeeper for bots. This file tells crawlers what they can and can’t access. Yes, it is smart to block some bots to save resources or secure sensitive areas, just make sure you’re not accidentally excluding AI too.

                                      Tools to Test Bot Accessibility

                                      1. User Agent Switcher: This Google Chrome extension mimics different bot user agents and tests how your site responds. 
                                      2. Manually Check robots.txt: Append /robots.txt to your domain (e.g., yourdomain.com/robots.txt) to see what’s blocked and allowed.
                                      3. Known User Agents: Look for these examples to make sure your website is letting in the right bots:
                                      • GPTBot: Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko); compatible; GPTBot/1.1
                                      • ClaudeBot: Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko); compatible; ClaudeBot/1.0
                                      • Anthropic AI Bot: Mozilla/5.0 (compatible; anthropic-ai/1.0)

                                      A full and updated list of these user agent strings can be found on DataDome.

                                      3. Optimize Brand Presence

                                      It’s likely you’re no stranger to how important brand presence is when it comes to SEO. AI platforms pull all of the information they find online and use it to understand and then represent your business when a user searches for it. 

                                      If your messaging is long-winded, vague, inconsistent, or missing, you’re risking misrepresentation, or worse, being completely ignored.

                                      Your landing pages need a very frank and straightforward brand statement that answers the basics:

                                      • Who you are: “[Your brand] leads the way in sustainable home goods.”
                                      • What you do: “We create eco-friendly furniture for modern living.”
                                      • Why you’re different: “Our designs combine style, sustainability, and affordability.”

                                      Make sure this messaging is everywhere AI platforms might be looking. Put it on your website, LinkedIn, and social media, and review responses as AI will draw answers from a multitude of sources. 

                                      Consistency is what gets your brand represented the way you want, and not as some random mashup of outdated info. Set the record straight before anyone can even get the wrong idea. 

                                      4. Develop Sentiment

                                      AI platforms don’t just pull out the facts, they piece together a brand’s overall vibe from an array of sources: forums, reviews, and social media. The catch is that bad press tends to stick around like gum on a shoe. 

                                      Take AT&T, for example: ask ChatGPT about their reliability as a service provider and you’ll likely hear all about their 2024 outage alongside mentions of their reliability. Ouch.

                                      Now, compare that to CrowdStrike. Despite their infamous broken Windows update causing probably the biggest global IT outage in history, you won’t see AI harping on it.

                                      Why? They have absolutely mastered sentiment management, strategically flooding the digital space with positive content and well-managed review responses that overshadow their epic blunder.

                                      If you want AI to focus on your wins, start by testing how platforms portray your brand. Ask questions like “Is [Your Brand] reliable?” Spot the negatives and tackle them head-on with corrective content. 

                                      Strong sentiment GEO means when people search for your brand, they see your strengths and not your stumbles. 

                                      5. Analyze Competitors

                                      Keeping tabs on your competitors in the SEO world is a necessary evil, but with AI, it becomes a whole lot easier to see just where your business could sit in rankings.

                                      AI rankings heavily influence user decisions, especially for the juicy middle-of-funnel searches like “Best

                                      in [location]” or “Top providers for [service].” Having an understanding of how your business stacks up against the competition reveals where you can step up your game, be more visible, and take your place in the share of the market.

                                      Start by identifying the key competitive queries that are relevant to your industry. AI tools like ChatGPT make this quite easy, but for the best results, use a GEO service like SEO Rank Media to map out how competitors are ranking. 

                                      With this intel, it’s time to take action. Create content that answers these questions better than anyone else. Use clear, direct language, highlight your benefits, and make sure your expertise comes through in a specific way AI platforms recognize. 

                                      The goal here is to make sure your brand is the obvious choice for these searches.

                                      6. Target Data Sources

                                      Free Close-up image of the LinkedIn app update screen on a smartphone display. Stock Photo

                                      Image: Pexels

                                      AI platforms don’t just make things up (well, most of the time), they draw from trusted data sources like LinkedIn, GitHub, and even Reddit to create their responses. If you want your brand to show up in those results, you need to meet AI where it’s looking.

                                      Here are a few ways you can improve your visibility:

                                      • Publish technical content on GitHub: This platform is a favorite for technical queries, so it’s perfect for showcasing your expertise in a concrete, credible way.
                                      • Share insights on LinkedIn: As a part of Microsoft’s ecosystem, LinkedIn is practically a VIP source for professional and industry-specific content.
                                      • Have some fun on Reddit: Claude and ChatGPT crawl Subreddits to gain community-driven perspectives. Join in on relevant discussions in an informational (not sales) way to boost your authenticity. 

                                      Get strategic in the way you place content and you’ll ensure your brand’s voice is part of the AI conversation.

                                      7. Answer Accurately

                                      AI “hallucinations” aren’t as fun as they sound. These occur when AI platforms respond with incorrect or misleading information that is so confident it would give ToastMasters a run for their money. Basically, they’re not something you want to happen when someone uses AI to look up your offerings.

                                      The GEO fix for this issue is to create well-structured and relevant FAQ pages that answer critical questions like:

                                      • “Does [Brand] ship internationally?”
                                      • “How does [Brand] handle refunds?”
                                      • “What services does [Brand] Provide?”

                                      Here’s some proof in the pudding. Taking a look at Ancestry.com’s FAQ page, you can see they have answered commonly asked questions about their service, with one being what do the results tell me?

                                      Jumping onto ChatGPT and asking the question “What do my ancestry.com results tell me?” yields a result that was quite clearly taken from this FAQ page. 

                                      Understanding your audience helps here. You need to know what kind of questions they’re likely going to be typing into an AI search engine and give straightforward and simple answers to them on your website’s FAQ page. 

                                      The payoff will be fewer opportunities for hallucinations and a more accurate representation of your business in AI-generated results. 

                                      Why GEO is the Way Forward

                                      Let’s be honest: AI search has turned SEO into a wild roller coaster. One minute, you’re impressed by ChatGPT’s ability to summarize complex topics; the next, it’s confidently claiming your brand sells banana-flavored widgets (which, of course, you don’t). 

                                      Staying ahead feels like having to learn SEO all over again, but it doesn’t have to.

                                      With SEO Rank Media and the G.E.O.D.A.T.A. Framework, you’ve got a reliable roadmap to tame the chaos and put your brand back in the spotlight. It’s your chance to future-proof your digital strategy, outsmart AI’s quirks, and thrive in this unpredictable search landscape.

                                      Ready to take charge? Let SEO Rank Media help you GEO your way to success.

                                      Gather Intelligence: The First Step in G.E.O.D.A.T.A.

                                      Caleb Turner on March 26, 2025

                                      If you want to take G.E.O.D.A.T.A. by the horns and really use it to your advantage, then you need to truly understand each step. And like with everything, the most foundational step is the first one.

                                      AI-driven search is entering the search engine world like a bulldozer, which means you’ll want to jump on the Generative Engine Optimization (GEO) train as quickly as possible.

                                      To do that, you’ll be using the G.E.O.D.A.T.A. framework, with the first step being “Gather Intelligence.”

                                      A Match Made in Heaven: AI & Generative Engine Optimization (GEO)

                                      There’s been a big shift in how search engines function, and you’re probably noticing it without really understanding what’s happening behind the scenes.

                                      Google still dominates traditional search, but AI search is excelling at providing direct, zero-click answers. To adjust to these changes, search engines are no longer just indexing keywords. They’re also looking at context, interpreting intent, and considering opinions. 

                                      This is all thanks to platforms like ChatGPT and Gemini that are reshaping how users engage with content. These AI models are trained on large datasets, including Common Crawl. These datasets are a huge collection of web pages that help AI understand language, trends, and user behavior.

                                      In other words, old-school SEO isn’t enough anymore. 

                                      Generative Engine Optimization (GEO) is the next evolution of SEO. That means you need to understand how AI interprets and ranks content. 

                                      By gathering intelligence on how AI search engines operate, brands can:

                                      • Create AI-friendly content: Sure, content’s made for users, but we can’t forget AI. Creating AI-friendly content that ticks all the boxes means you’re more likely to be seen.
                                      • Leverage semantic search: AI prioritizes contextually relevant and comprehensive answers. Intent-based and topic clustering is essential.
                                      • Optimize for multimodal search: Text isn’t the only thing AI cares about. AI-driven search also looks at images, video, and voice search. Knowing these different formats can help improve your visibility.

                                      Why Gathering Intelligence Matters

                                      When we say the first step of the G.E.O.D.A.T.A. framework is gathering intelligence, there’s a good reason behind it. Gathering intelligence on AI search will help you:

                                      • Identify what AI-search deems as a priority: Learning how AI ranks content will help you develop an AI-aligned content strategy.
                                      • Understand algorithm updates: AI is changing quickly. Keeping pace with all the updates can help keep your content relevant.
                                      • Optimize your content: Understand how to tailor content to appease both AI and users.

                                      So, in other words, don’t skip this step. No one likes spending hours researching, but gathering intelligence is a foundational step in this framework. If you overlook this step, it’ll catch up with you later down the road.

                                      How to Start Gathering Intelligence

                                      G.E.O.D.A.T.A. how to start gathering intelligence

                                      Think of yourself as a spy…like James Bond. Okay, you don’t have to be that intense (only if you want to), but you certainly want your marketing efforts to actually pay off.

                                      If you’re feeling lost, it’s time to rethink your content strategy and cozy up to AI-powered search engines. Here’s how to start gathering intelligence.

                                      1. Keep an eye on AI search updates

                                      You may have heard people talking about ChatGPT and Gemini, but have you tested them out? Do you understand how these platforms work? Or know how they’re evolving? 

                                      It’s not about becoming a guru in AI; no one’s asking that of you. But you should refresh your knowledge on the basics and stay informed of upcoming changes. There’s tons of content available on YouTube, and you can also follow blogs and thought leaders.

                                      2.Consider how AI sees your brand

                                      Do you know what AI is saying about you? It may sound like a strange question, but in 2023 alone, 13 million adults in the United States used platforms like ChatGPT and Gemini for search queries. It’s predicted that by 2027, this number will reach 90 million online users.

                                      AI isn’t magic. It gets its information from somewhere. These AI platforms take information from your website, forums, blogs, social media, and search engine results to generate answers. So, it’s extremely important you understand how AI sees your brand and the areas you need to improve on. 

                                      But how do you do this? Simple. Ask AI questions about your brand. Go onto ChatGPT or Gemini and ask questions like:

                                      • What is [Your Brand]?
                                      • What does [Your Brand] offer?
                                      • Is [Your Brand] trustworthy?
                                      • What do people have to say about [Your Brand]?
                                      what do people have to say about Rank Media

                                      Read the answers and take notes. Look at how AI summarizes your business. Is what AI says about your brand true? What information is inaccurate? Where does this information come from? This will help you identify areas of improvement in your messaging and content. Because once AI starts perpetuating incorrect information, it’s hard to stop the cycle.

                                      This is why gathering intelligence is so important. You need to know what you’re working with. Once you have the foundation, you’ll be able to move on to the next steps and make data-driven decisions that improve your business.

                                      AI Platforms Are Saying Inaccurate Things About My Brand - Where’s It Coming From?

                                      AI Platforms Are Saying Inaccurate Things About My Brand

                                      If you notice that ChatGPT or Gemini are saying inaccurate things about your brand, first things first, look at your brand’s content.

                                      This means going through your website and reading through your content with a fine-tooth comb. Look at your ‘About Us’ page or FAQs, go through your blog and social media profiles, and see if the information is up-to-date. 

                                      Most people also forget to look at forums like Reddit, LinkedIn, Medium, Quora, and Google Answers, even though AI also relies on user-generated information as well. It could be that there is some unfavorable content on Reddit about your brand that’s causing the issue. 

                                      Why the G.E.O.D.A.T.A Framework Is the Way to Go

                                      The G.E.O.D.A.T.A. framework isn’t full of cheap tricks. This framework is based on a process to help you align your content and search strategies with the AI-driven online world. Each step builds on the last to ensure your brand is positioned for success. If you’re ready for the next step, make sure to read our guide on Evaluate Accessibility.

                                      With SEO Rank Media and the G.E.O.D.A.T.A. framework, you won’t need to relearn SEO all over again. Rather, you’ll have a reliable roadmap and the support you need to get your brand in the spotlight. This is your chance to jump ahead of your competitors and prepare your business for the next shift in content.

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