May 13 2026
Data Shows AI Overviews Exposing Negative Reviews Without User Intent. What To Do Next via @sejournal, @EraseDotCom

This post was sponsored by Erase.com. The opinions expressed in this article are the sponsor’s own.

Why does AI pull a 2023 Reddit thread into a 2026 comparison query?
What makes AI cite some complaints about my brand and skip others?
How do I get AI to stop citing old complaints in unrelated queries?

Four signals decide what AI exposes, and once you know them, you can work them.

Q1 2026 analysis surfaces four consistent patterns in what AI engines cite: recency plus volume, specificity that names features, platform authority (Reddit, major review sites), and recurrence across sources. The complaints that hit all four are the ones that show up unprompted in queries where users were looking for solutions, not problems. The fix isn’t a single takedown request; it’s a four-step audit-and-rebuild framework mapped to those same four signals.

When someone asks ChatGPT “which CRM should I choose,” these AI engines don’t just list features. They pull in user complaints, Reddit gripes, and years-old forum threads as part of their comparison. Your brand’s negative signal can appear in an answer about your competitor. Even more concerning, as Fast Company recently reported, there’s growing evidence of AI engines misquoting or misrepresenting brand statements, compounding the challenge of maintaining an accurate reputation in AI-generated summaries.

AI Comparison Queries Are Now Reputation Audits. Here’s What That Means.

Traditional reputation management focused on suppressing results when someone searched “[your brand] + reviews.” That’s still important, but it’s no longer sufficient.

It’s time for a reputation audit.

AI Overviews and LLM-powered search engines treat every product comparison as an opportunity to synthesize user sentiment. When evaluating options, these tools actively scan for negative reviews on complaint sites, Reddit discussions, forum threads, gripe site entries, and customer support complaints that made it into public view.

The critical difference: users aren’t asking about problems. They’re asking about solutions. But AI engines interpret “helping” as including negative signals from your brand footprint.

Why Some Complaints Show Up in AI Answers & Others Don’t

Not every negative mention gets pulled into AI-generated answers, but certain patterns increase surfacing likelihood:

  • Recency + volume: Fresh complaints with multiple corroborating sources rank high.
  • Specificity: Vague posts get filtered out. Detailed complaints that include product names and outcomes are weighted as valuable context.
  • Platform authority: Reddit, Trustpilot, G2, and industry forums get treated as trusted sources.
  • Recurrence across sources: If the same issue appears in multiple places, AI engines treat it as a verified pattern.

The 4-Step Framework: How to Audit, Remove, Rebuild, and Suppress Your Brand’s AI Reputation Signals

Understanding what’s in your negative signal footprint, prioritizing what can and should be addressed, and building a positive content layer that represents your brand accurately when AI tools pull information is the key to success.

Map what AI engines can access about your brand across platforms where complaints surface.

  1. Open ChatGPT or Perplexity and type: “What are the pros and cons of [your brand] vs [top competitor]?” Take a screenshot of the response and note any negative claims.
  2. On Google, search site:[key platform].com “[your brand name]” + “scam” OR “complaint”. This forces the search engine to show you only the filtered conversations AI models are currently scraping.
  3. Search for your brand on Google and check the featured snippets for anything negative, other SERP features like People also ask for negative or adversarial searches.

Key platforms to check:

  • Review platforms (Trustpilot, G2, Capterra, Yelp, Google Business Profile).
  • Reddit (search your brand name + product category + complaint terms).
  • Industry forums (Stack Overflow for tech, niche communities for specialized services).
  • Facebook groups and community pages (particularly industry-specific or local groups where your customers congregate).
  • Social media (Twitter/X, LinkedIn discussions, TikTok comments).
  • Legacy gripe sites (RipoffReport, Complaintsboard); while largely deindexed, content may still be cited by AI engines.

Document these details:

  • Content type and platform.
  • Date posted.
  • Specific claims made.
  • Factual accuracy.
  • Current visibility in Google and AI summaries.

Focus on detailed complaints with enough context that AI engines might treat them as credible sources.

Step 2: Prioritize Based on Surfacing Likelihood

Focus on:

  • High priority: Recent complaints with specific details, issues mentioned across multiple platforms, content on high-authority platforms (Reddit, major review sites), complaints naming features or pricing specifically.
  • Medium priority: Older complaints (1-2 years) still in search results, isolated reviews without corroboration.
  • Low priority: Very old content (3+ years) with low engagement, complaints about discontinued products.

How To Create A Priority Matrix

Create a simple scoring matrix to decide what to tackle first:

  • High Priority: Content that appears in AI summaries AND has high organic visibility (check Semrush or Ahrefs for estimated monthly visits to that specific URL) or compare them against queries for those keywords that you have available in search console – if it’s a branded search, you should have full visibility on this from search console.
  • Verified Impact: For platform-specific reviews (G2, Trustpilot, Google Business), use your internal analytics to track how many users are clicking “Helpful” on negative reviews. A review with 50+ “Helpful” votes is a massive signal that AI engines will not ignore.

Step 3: Remove or Respond Where Possible

Some negative content can be removed outright. Some deserve a response, and some require both.

How to Get Negative Content Taken Down

If the content violates platform policies (false information, impersonation, harassment), request removal through the platform’s reporting process.

For legacy complaint sites and gripe sites, professional content removal services can often negotiate takedowns based on inaccuracies or policy violations, though as reputation defense strategies evolve for AI, the focus has shifted from simply removing content to building stronger positive signals.

For content that mentions you but doesn’t necessarily focus on your brand (like a Reddit thread comparing five tools where yours gets one negative mention), removal usually isn’t an option, but you can dilute its impact by ensuring positive mentions appear more frequently in similar discussions.

When Responding Publicly Actually Helps You

Legitimate complaints about real issues, misunderstandings you can clarify with facts, or service failures where an explanation adds credibility. Keep responses factual, non-defensive, and focused on resolution. AI engines can pull your response into summaries, giving you a chance to reframe the narrative.

When Engaging Makes Things Worse — Skip It

Fake reviews, emotional rants without substance, old complaints about discontinued products, or situations where engagement will amplify visibility.

Step 4: Build a Positive Content Layer That AI Engines Prefer

This is where ongoing reputation management becomes critical. You need owned and earned content that AI engines will preferentially cite when answering comparison queries.

What Goes Into A Positive Content Layer

  • Structured FAQ content: Create pages answering common objections and questions with clear headers and schema markup.
  • Case studies: Detailed examples with metrics, timelines, and direct customer quotes give AI engines concrete data to cite.
  • Community presence: Contribute to Reddit and forums where your audience asks questions. Build credibility through value, not promotion.
  • Third-party validation: Get featured in roundups and comparison articles on authoritative sites.
  • Regular content updates: AI models prioritize recent content. Keep your owned content fresh.
  • How this plays into broader online reputation management: What you’re building isn’t just an AI strategy—it’s a defensible reputation infrastructure. Comprehensive, recent, authoritative content across multiple touchpoints creates a buffer that makes it harder for isolated negative signals to dominate.

How To Build A Positive Content Layer 

  1. Turn your FAQ into a knowledge base that addresses common objections (e.g., “Is [your brand] worth the price?”). Depending on how much reach and authority your brand has, it can be worthwhile to publish these as their own pages with a clear H1 question as the headline and breadcrumb the Q and As in a format like /faq/[service area]/[objection] to create more internal linking opportunities and depth rather than just having everything on a massive FAQ page.
  2. Reach out to some of your satisfied customers and ask for a 2–3 sentence quote about a specific outcome they achieved. Publish these as a case study snippet on your site. Specificity (metrics, timeframes) helps to ensure LLMs treat content as credible evidence rather than marketing copy. Link to their LinkedIn or business website, if possible, to help reinforce that it is a real review for a real customer.
  3. Identify high-authority “Best of” lists or industry roundups where your brand is missing and email the editors to provide a unique expert insight or updated product data for inclusion. These seed high-trust citations that AI engines prioritize when synthesizing brand comparisons and reputation summaries. The higher they rank on Google, the better.

Monitoring becomes essential at this stage. Track which keywords trigger AI Overviews that mention your brand, watch for new complaints surfacing in high-authority platforms, and measure whether your positive content is getting cited in AI-generated comparisons. This isn’t a one-time project; it’s an ongoing program.

Start Here: Your Easy Steps to Managing Your AI Reputation

If you’re dealing with high-stakes reputation issues where missteps could amplify problems, specialized online reputation management services and experts like our team at erase.com can help you move faster and avoid pitfalls. The goal isn’t just reacting to what’s already out there; it’s building a system where positive signals consistently outweigh isolated negatives when AI engines scan for information.

The shift is already here. The question is whether you’re managing it proactively or discovering it reactively when a prospect mentions “something they saw in ChatGPT.”

LEARN MORE

Image Credits

Featured Image: Image by Erase.com. Used with permission.

Ecommerce MGMT 0 Comments
May 12 2026
The Tech SEO Audit for the AI Search Era: How to Maximize Your AI Visibility via @sejournal, @JetOctopus

This post was sponsored by JetOctopus. The opinions expressed in this article are the sponsor’s own.

How do I optimize my site for ChatGPT and Perplexity, not just Google?

How do I know if AI bots are actually crawling my site?

How should my technical SEO strategy change for AI Search?

A significant portion of your site’s search impressions in 2026 are generated by machines researching on behalf of humans.

Those machines don’t care about your keyword rankings. They care whether your:

  • HTML loads cleanly in under 200 milliseconds
  • Product detail page is reachable in fewer than four clicks
  • Content answers a specific, nine-word question that has never appeared in any keyword research tool in your career.

This isn’t speculation. It’s what our server log data across hundreds of enterprise websites is showing us, consistently, since mid-2025.

What’s Actually Happening On Your Site

My colleague, Stan, flagged a pattern in a Slack message: query lengths were growing at rates that didn’t correlate with human behavior.

A 161% growth rate in 10-word queries year-over-year is not driven by users who suddenly got more verbose. It’s driven by AI agents decomposing a single user prompt into dozens of parallel sub-queries, a process researchers now call “fan-out.”

Query Length Growth in 2025

Image created by JetOctopus, Aggregated GSC data across hundreds of enterprise properties, 2025

The gradient is the tell. Human search behavior doesn’t scale this cleanly by word count. Machines do. By October 2025, 7-plus-word queries reached nearly 1% of total query volume, roughly triple their historical share.

More revealing than the volume is the CTR. While impression counts for 10-word queries spiked 161%, click-through rate collapsed to 2.26%, down from 8–11% in 2023.

The AI reads your page, extracts the answer, synthesizes it for the user. Your site never gets the visit.

We call these “phantom impressions.” They’re real signals that your content is being evaluated inside AI reasoning chains. If you’re filtering them out of your reporting because they don’t drive traffic, you are flying blind.

The Three Bots Visiting Your Site & Their Impact On SERP Visibility

Not all AI crawlers are equal, and treating them as a single category is the first mistake most technical SEOs make.

Training bots crawl broadly and ignore click depth. A training visit means the AI knows your content exists, not that users will ever see it.

AI search bots drop off quickly beyond two or three clicks from the homepage and typically visit each page only once a month.

AI user bots are initiated when a real person asks a question in ChatGPT, Perplexity, or Claude, and the AI researches the answer on their behalf. These are the only visits that translate to actual AI visibility.

Bot Type What Triggers It Crawl Depth Impact on AI Visibility
Training bots Model education cycles Deep — ignores click distance None directly. Awareness only.
AI search bots New URL discovery & fresh content Shallow — ~1 visit/month beyond 2–3 clicks Critical gatekeeper. If it misses a page, user bots won’t find it either.
AI user bots Real user query in ChatGPT / Claude / Perplexity Selective — driven by speed and structure High. Closest proxy to an AI impression.

Your site can receive heavy crawling from training and search bots and still be completely absent from AI-generated answers. If you’re not segmenting AI bot traffic by type in your log analysis, you have no idea which third of the iceberg you’re measuring.

Which SEO Signals Do LLMs Respect?

Robots.txt is your primary lever.

Most major AI platforms (ChatGPT, Claude, Gemini) follow robots.txt directives. Perplexity is a partial exception: PerplexityBot respects robots.txt, but Perplexity-User, the user-triggered bot, does not. Cloudflare confirmed this in an investigation. Most sites haven’t audited their robots.txt with AI access in mind. Do it.

Sitemaps are broadly supported.

ChatGPT, Claude, and PerplexityBot all use XML sitemaps for URL discovery. Keep them accurate.

Signals Best Saved For SEO & Ranking Efforts

These signals below don’t appear to impact AI visibility, but are still key for ranking for queries that still trigger traditional SERPs.

Canonical tags and noindex directives do nothing for AI bots.

AI crawlers don’t build a search index, so they have no use for these meta-signals. Content hidden from Google using noindex is fully visible to ChatGPT’s crawler.

LLM.txt does nothing.

Our log data shows major AI bots don’t read this file. Don’t invest time here.

JavaScript rendering is a critical blind spot.

Most AI crawlers (ChatGPT, Claude, Perplexity) don’t render JavaScript. If your product pages load key content client-side, those agents read an empty shell. Server-side rendering is the only architecture that works universally. The exception is Google Gemini, which uses the same Web Rendering Service as Googlebot.

How To Make Sure ChatGPT, Perplexity & LLMs Can Reach Your Content

AI search bots visit deep pages roughly once a month and drop off sharply beyond three clicks from the homepage. The pages with the most specific, answerable information are often the hardest for agents to reach.

The fix: Elevate your most valuable deep pages through internal linking, ensuring they’re reachable within four clicks.

Pages crawled by training bots but never reached by user bots are your highest-priority targets. Pages AI user bots visit frequently are telling you what to scale: more content covering the same topic cluster and depth.

Optimize Content For Longer, Fan-Out Queries

95% of the queries driving AI citations have zero monthly search volume. They’re synthetic sub-queries generated by AI models. But they show up in GSC: impressions, no clicks, query lengths you’d never target voluntarily.

How To Find Fan Out Query Opportunities

To surface fan out queries that are worth chasing, connect your GSC API to JetOctopus (to bypass the 1,000-row UI limit) and filter for: query length greater than 7 words, impressions under 50, clicks at 0, over the last 3 months. That’s your Fan-Out Opportunity Matrix, the exact questions AI agents are asking about your content.

Prompt Types That Fan Out Most

Image created by JetOctopus, 2025

If your content isn’t structured to answer list and comparison queries, with explicit rankings, pros/cons, and side-by-side specs, you’re leaving the highest fan-out surface area unoptimized.

“Product review” intent queries surged from 239 in June 2025 to over 40,000 by September 2025. That 16,000% increase was AI agents systematically harvesting structured opinion data. If your product pages lack this depth, you’re invisible to that harvest.

The Technical Audit: Where to Start

Step 1: Identify AI User Bot Traffic In Logs

Pull raw server logs (Apache/Nginx) and export all lines containing these user agents: OAI-SearchBot and ChatGPT-User, PerplexityBot and Perplexity-User, Claude-SearchBot and Claude-User. Then manually group hits by user-agent patterns and endpoints in a spreadsheet. To distinguish training bots from user bots, you’ll need to maintain your own classification list — one that changes often and isn’t standardized.

In JetOctopus Log Analyzer, this segmentation is built in: filter by bot type (training, search, and user) in a few clicks and immediately see which pages AI user bots visit (your AI-visible content, ready to scale) versus pages training bots hit but user bots never reach (your highest-priority fix targets).

Step 2: Audit Technical Accessibility Of Deep Pages

Pick a sample of deep URLs and check HTML payload size, confirm key content isn’t injected via JavaScript by viewing raw HTML, simulate crawl depth by counting clicks from the homepage, and test load time in Chrome DevTools or Lighthouse. Also check whether important content sits behind accordions or “View More” elements — these require JavaScript execution that AI bots skip entirely. For large sites with thousands of deep pages, this sampling approach misses a lot. AI agents don’t click. If information only appears after user interaction, it doesn’t exist for these crawlers.

Step 3: Clean Up Your Robots.txt

Open your robots.txt and review all Disallow and Allow directives for every user-agent line by line. AI bots follow Disallow rules, so make sure you’re not accidentally blocking important URLs. Manually test key URLs to confirm they aren’t blocked. A 30-minute audit here can prevent you from blocking crawlers you want in, or exposing content you’d rather keep out.

Step 4: Map Your Phantom Impressions

Export data from GSC Performance reports filtered by impressions with zero clicks. Because of the 1,000-row UI limit, you’ll need to use the GSC API or export in chunks by date and query, then merge datasets in spreadsheets or BigQuery. Also factor in query frequency: long queries appearing daily are likely not fan-outs.

Connect your GSC API to JetOctopus to bypass the row limit and build your Fan-Out Opportunity Matrix automatically — the exact questions AI agents are asking about your content, ready to act on.

Step 5: Monitor The Changes

Set up a recurring export process — pull GSC data monthly and compare impressions over time, re-run log analysis scripts and diff bot activity, track Core Web Vitals separately in PageSpeed Insights or CrUX. You’ll end up stitching together multiple data sources with no unified alerting, making it hard to catch regressions early.

JetOctopus Alerts covers exactly this: unified notifications for changes in AI bot activity alongside Googlebot behavior, Core Web Vitals, on-page SEO issues, and SERP efficiency drops, so you catch regressions before they compound.

The New KPI: Technical Accessibility

SEO in 2026 is restructuring around one constraint: can an AI agent crawl, reach, and extract a fact from your 50,000th product page in under 200 milliseconds?

If the answer is no, your rankings, backlinks, and content quality become irrelevant for a growing share of search interactions. The machines are searching. The question is how quickly you can see what’s actually happening.

Start with your logs. Everything else follows from there.

Want to see exactly how AI bots are interacting with your site: which pages they reach, which they skip, and where your fan-out opportunities are hiding? Book a live walkthrough of the JetOctopus platform. We’ll pull your actual log data and show you what your GSC reports aren’t telling you.
CHECK MY AI VISIBILITY

Image Credits

Featured Image: Image by JetOctopus. Used with permission.

Ecommerce MGMT 0 Comments
May 5 2026
Why AI Search Skips Your Content (And How to Diagnose Where It’s Failing) via @sejournal, @jeffrey_coyle

This post was sponsored by Siteimprove. The opinions expressed in this article are the sponsor’s own. 

Why does my content get crawled but never cited in ChatGPT or Perplexity?

How do I tell if my AI visibility problem is technical or content-quality related?

What actually decides whether AI picks my page over a competitor’s?

The gap between appearing in an AI answer and being retrieved by an AI system is where the actual AI search strategy lives.

This article breaks down that AI search strategy process:

  1. How AI search systems retrieve and select content.
  2. Why eligibility alone doesn’t win.
  3. How to diagnose whether your content is failing at the retrieval layer or the quality layer.

The fix is different for each, and most teams are solving the wrong problem.

How AI Search Crawls Your Site & What Just Changed

AI search systems still rely on crawlers. If your pages block crawl access, depend on unexecuted JavaScript rendering, or bury content behind authentication walls, nothing downstream matters.

Semantic HTML, proper heading hierarchy, and descriptive markup remain the cost of entry. But the stakes are higher now: these aren’t just accessibility compliance items anymore. They’re the structural signals AI systems use to parse and chunk your content for retrieval.

Platforms like Siteimprove.ai that audit accessibility and content quality natively can surface these issues before they become retrieval problems. If you’re already running accessibility audits, you’re closer to AI search readiness than you might think.

What has changed is what happens after the system accesses your content.

Why You’re Now Competing Paragraph-by-Paragraph, Not Page-by-Page

AI systems don’t ingest a page as a single unit. They break it into passages: discrete chunks of text that get indexed independently.

This is where most traditional SEO thinking falls short. You’re no longer competing at the page level. You’re competing at the passage level.

A 3,000-word guide might contain 15 to 20 individually indexed passages. Some of those will be clear, self-contained, and directly responsive to a query. Others will be vague transitions or filler paragraphs that contribute nothing to retrieval.

Every passage is either a retrieval candidate or a wasted one. A page can rank well in traditional search while performing poorly in AI search, because its best passages are buried inside paragraphs the system can’t cleanly extract.

How to audit passages manually:

  1. Copy one important page into a plain document. Break it into individual paragraphs or short sections, then read each passage on its own without the surrounding page context.  
  2. Ask one question per passage. For each paragraph, write the query it actually answers. If you cannot name a clear query, that passage probably is not strong retrieval material.  
  3. Rewrite weak passages to stand alone. Lead with the answer, add specific context, and remove vague transitions that only make sense when someone reads the full page from top to bottom. 

      How AI Picks Which Passages Make It Into an Answer

      When a user asks an AI system a question, the system doesn’t read the web in real time. It queries a pre-built index, retrieves the most relevant passages from potentially millions of candidates, and scores them for relevance and quality.

      But the system rarely stops at the literal query. It expands the question into a network of related sub-questions (follow-ups, edge cases, adjacent concerns) and retrieves passages for each. This is query fan-out, and it fundamentally changes what “ranking” means.

      Your content isn’t just competing against pages that target your exact keyword. It’s competing against everything the system retrieves across that entire network of related queries.

      A page that answers one narrow question well might get retrieved for that specific sub-query. But a page that anticipates the follow-ups, the “what about” variations, and the context a user would need next gets retrieved across multiple nodes in the fan-out. That’s a fundamentally different kind of competitive advantage.

      Citation happens after all of this. The system attributes its synthesized answer to the sources that contributed the most useful material. Chasing citations without understanding retrieval is working backwards.

      How to map a simulated query fan-out manually:

      1. Start with one target question. Write down the main query your audience would ask, then list the follow-up questions they would naturally ask next.  
      2. Group those questions by intent. Separate beginner questions, implementation questions, comparison questions, edge cases, and decision-making questions.  
      3. Match each question to existing content. If a question does not map to a clear passage on your site, that is a retrieval gap. If it maps to a vague or buried passage, that is a passage-quality gap. 

      Why Being Indexed Doesn’t Mean You’ll Get Cited

      Here’s where most AI visibility strategies stall.

      Teams invest heavily in technical optimization (fixing crawl issues, improving page speed, adding structured data) and assume the rest will follow. They treat retrieval readiness as the destination instead of the starting line.

      Being indexed by an AI system means your content can be retrieved. It doesn’t mean it will be.

      Consider a practical example. Two sites publish guides on international SEO for e-commerce. Site A has strong domain authority, clean technical SEO, and a 4,000-word guide that covers the topic broadly but generically. Site B is a smaller consultancy with a 1,500-word page focused specifically on hreflang implementation for Shopify stores with three or more language variants.

      When an AI system receives a query about multilingual e-commerce SEO, it fans out into sub-questions. For the specific sub-query about hreflang configuration on Shopify, Site B’s focused passage gets retrieved and cited. Site A’s guide technically covers hreflang, but its relevant passage is buried in paragraph 37 of a general overview, sandwiched between topics that dilute its signal.

      Site A is retrieval-ready. Site B is answer-worthy. That distinction is the core tension of AI search optimization, and it requires a completely different audit than most teams are running.

      How to test this manually:

      1. Run the same query across multiple AI search experiences. Use a small set of high-value questions and record which sources are cited or referenced.  
      2. Compare the cited source to your page. Do not compare the full articles. Compare the exact section or passage that appears to answer the query.  
      3. Look for the selection difference. Ask whether the cited passage is more specific, more direct, more current, or more practical than yours. That usually reveals why it won. 

      The Two Signals That Decide AI Search Passage Selection

      The hreflang example illustrates a broader pattern. Once your content clears the technical gates, competition shifts entirely to quality. And “quality” in AI retrieval means something more specific than most content strategies account for.

      Information Gain Is A Very Important Signal

      An important factor in passage selection is whether your content contributes something the system can’t assemble from other sources.

      This is information gain: original data, proprietary research, first-person case studies, or novel frameworks that don’t exist elsewhere in the index. When every other passage in the candidate pool says roughly the same thing, the passage that introduces a new data point or a genuinely different perspective has a structural advantage.

      Generic coverage that restates widely available information is the easiest content for an AI system to replace with any other source. Original expertise is the hardest. If your content strategy doesn’t have a plan for producing material that is uniquely yours, you’re filling the index with passages any competitor could displace.

      How to identify information gain manually: 

      1. Review the top competing pages for the same topic. Look for repeated claims, definitions, examples, and recommendations that appear across nearly every source.  
      2. Mark anything your page says that competitors do not. This could include proprietary data, internal benchmarks, customer examples, expert commentary, original frameworks, or lessons from implementation.  
      3. Strengthen the unique material. Move original insights higher on the page, give them clearer headings, and support them with concrete examples instead of burying them in generic explanation. 

      How Topic Depth Gets More of Your Pages Into the Candidate Pool

      Information increases the likelihood that gain gets your best passages selected. Depth and coverage determine how many passages you have in the candidate pool to begin with.

      AI systems exploring a subject pull from multiple passages across multiple pages. If your site covers a topic comprehensively, with dedicated pages for subtopics, related concepts, and adjacent questions, you create more opportunities to be retrieved across the full query fan-out.

      This works at two levels. Across your site, topic clusters with focused pages for each subtopic outperform a single pillar page surrounded by thin supporting content. Within a single page, going three layers deep on a subject (the basics, the edge cases, and the practitioner-level tradeoffs) gives the system more high-quality passages to select from.

      A domain with strong general authority but shallow coverage of a specific subject will lose passage-level retrieval to a smaller site that covers that subject exhaustively. AI systems evaluate authority at the topic level, not just the domain level.

      How to assess topic depth manually:

      1. Create a simple topic map. Put your main topic in the center, then list the subtopics, adjacent questions, use cases, objections, comparisons, and technical details a buyer or practitioner would need.  
      2. Assign each subtopic to a URL. If several important subtopics are crammed into one broad guide, they may need dedicated pages or stronger sections.  
      3. Look for thin or missing coverage. Prioritize gaps where competitors have specific, useful content and your site only has a passing mention. 

      How to Diagnose Why Your Content Isn’t Getting Cited In AI Answers

      When AI visibility underperforms, the instinct is to produce more content. That’s often the wrong move.

      The first diagnostic question is simpler: is this a retrieval problem or a quality problem? Each has different symptoms, different causes, and different fixes.

      Signs Your Content Never Reaches the AI’s Candidate Pool

      If your content isn’t appearing in AI responses at all, even for queries where you have relevant, published material, the issue is upstream. The content isn’t reaching the candidate pool.

      Audit for these signals:

      • Crawl access restrictions or rendering failures preventing indexing.
      • Missing or broken semantic structure: heading hierarchy, section markers, descriptive markup.
      • Passages that are too long, too short, or too loosely structured to be extracted cleanly.
      • Content buried inside tabs, accordions, or interactive elements that don’t render for crawlers.

      In practice, this looks like a page that performs reasonably in traditional search but generates zero AI citations. The content might be strong. The system just can’t access or parse it at the passage level.

      Retrieval failures are technical. They’re also the fastest to fix, because the content itself may already be competitive. It just needs to reach the candidate pool.

      Signs You’re in the AI Search Citation Pool but Losing to Competitors

      If your content is being retrieved but not selected, or selected less often than competitors for the same queries, the issue is downstream. The system can see your content. It’s choosing something else.

      Audit for these signals:

      • Passages that are vague, indirect, or take too long to reach the point.
      • Coverage gaps where competitors address sub-questions your content ignores.
      • Lack of original data, examples, or practitioner-level specificity.
      • Generic treatment of a topic that other sources cover with equal or greater depth.

      The telltale sign is finding competitor citations for queries your content should own. When you compare the retrieved passages side by side, the competitor’s passage answers the question more directly, with more specificity, in fewer words.

      Quality failures require content investment. They can’t be solved with technical fixes alone.

      Fix This First, Then Move to Quality

      Start with retrieval. Technical fixes are lower effort and unlock everything downstream. A page that isn’t being crawled or chunked properly can’t benefit from content improvements at any level.

      Once retrieval is confirmed, shift to passage-level quality. Identify the specific queries where competitors are winning selection, compare the actual passages head-to-head, and close the gap at the individual passage level rather than rewriting entire pages.

      The highest-ROI work sits at the intersection: passages that are already being retrieved but aren’t winning selection. They’re close. They just need to be more direct, more specific, or more useful than the alternatives.

      How to prioritize fixes manually:

      1. Create a simple two-column audit. Label each issue as either “retrieval” or “quality.” Retrieval issues include crawl blocks, broken structure, hidden content, and poor extractability. Quality issues include vague answers, missing examples, shallow coverage, and weak differentiation.  
      2. Fix retrieval blockers first. There is no point improving a passage that systems cannot access, parse, or associate with the right topic.  
      3. Then improve near-miss passages. Focus on pages that already rank, receive impressions, or cover the right topic but lose citations to more specific competitor content. 

      What to Track Instead of Citation Screenshots

      If the old metrics (mention counts, citation screenshots, brand-name tracking) don’t tell the full story, what does?

      Track retrieval presence separately from citation selection. Retrieval presence asks whether your content appears anywhere in the system’s candidate set for a given query cluster. Citation selection asks whether it was chosen for the final synthesized answer.

      A page with high retrieval presence but low citation selection has a quality problem. A page with low retrieval presence for queries it should match has a technical problem. That distinction tells you exactly where to invest.

      The challenge is that most teams piece this together across disconnected tools: one for accessibility auditing, another for content analytics, a third for search performance. By the time you’ve correlated the data, you’ve lost the thread between cause and effect.

      This is where Siteimprove’s approach matters. Because accessibility auditing, content quality scoring, and search analytics live in one platform with native analytics, you can trace a retrieval failure back to its structural cause without jumping between tools or reconciling data sets. A broken heading hierarchy flagged in an accessibility audit connects directly to the search performance data showing that page’s declining AI visibility. A content quality score on a specific page maps to its passage-level competitiveness for the queries you’re targeting.

      That closed loop between accessibility, content, and search performance is what turns the retrieval-vs-quality framework from a diagnostic concept into an operational workflow.

      How to track AI visibility manually:

      1. Build a query-tracking spreadsheet. Include the query, topic cluster, your best-matching URL, whether your brand appeared, whether you were cited, which competitors appeared, and what type of issue you suspect.  
      2. Track patterns, not one-off screenshots. AI answers can vary, so look for repeated behavior across multiple prompts, systems, and dates.  
      3. Separate visibility from selection. A page that appears in related answers but rarely gets cited likely has a quality problem. A page that never appears for relevant prompts likely has a retrieval or coverage problem. 

      What It Takes to Get AI to Pick You

      The question brands should be asking isn’t “Can AI find us?” It’s “Does AI find us useful?”

      That shift reframes content strategy entirely — from visibility tracking to retrieval mechanics, from page-level optimization to passage-level precision, and from generic authority-building to topic-specific depth.

      Three principles hold across every AI search system operating today.

      First, treat technical accessibility as non-negotiable infrastructure. It doesn’t differentiate you, but its absence disqualifies you.

      Second, build content for the query network, not the individual keyword. AI systems resolve clusters of related questions simultaneously. Your content architecture should map to that same structure.

      Third, prioritize information gain. Original research, proprietary data, and first-person expertise are the hardest assets for an AI system to source elsewhere — and a strong signal that your content deserves selection.

      The brands that win in AI search won’t be the ones that figured out how to get mentioned. They’ll be the ones whose content was too useful to leave out.

      FIND THE GAPS IN YOUR CONTENT SYSTEM

      Image Credits

      Featured Image: Image by Siteimprove. Used with permission.

      Ecommerce MGMT 0 Comments
      May 1 2026
      How AI Overviews Surface Negative Reviews, Without Anyone Searching for Them via @sejournal, @EraseDotCom

      This post was sponsored by Erase.com. The opinions expressed in this article are the sponsor’s own.

      Why is my brand appearing in AI comparisons I didn’t ask to be in?
      How do I find out what AI tools are saying about my brand?
      What’s the difference between traditional reputation management and AI reputation management?

      Any issues with your brand’s reputation are what AI decides to show searchers, unprompted.

      Throughout Q1 2026, we’ve seen a behavioral shift in how prospects discover brand reputation issues. AI-assisted research tools now autonomously surface negative content, such as reviews, complaints, forum threads, social media discussions, inside comparison queries, without users deliberately searching for problems.

      When someone asks ChatGPT “which CRM should I choose,” these AI engines don’t just list features. They pull in user complaints, Reddit gripes, and years-old forum threads as part of their comparison. Your brand’s negative signal can appear in an answer about your competitor. Even more concerning, as Fast Company recently reported, there’s growing evidence of AI engines misquoting or misrepresenting brand statements, compounding the challenge of maintaining an accurate reputation in AI-generated summaries.

      AI Comparison Queries Are Now Reputation Audits. Here’s What That Means.

      Traditional reputation management focused on suppressing results when someone searched “[your brand] + reviews.” That’s still important, but it’s no longer sufficient.

      It’s time for a reputation audit.

      AI Overviews and LLM-powered search engines treat every product comparison as an opportunity to synthesize user sentiment. When evaluating options, these tools actively scan for negative reviews on complaint sites, Reddit discussions, forum threads, gripe site entries, and customer support complaints that made it into public view.

      The critical difference: users aren’t asking about problems. They’re asking about solutions. But AI engines interpret “helping” as including negative signals from your brand footprint.

      Why Some Complaints Show Up in AI Answers & Others Don’t

      Not every negative mention gets pulled into AI-generated answers, but certain patterns increase surfacing likelihood:

      • Recency + volume: Fresh complaints with multiple corroborating sources rank high.
      • Specificity: Vague posts get filtered out. Detailed complaints that include product names and outcomes are weighted as valuable context.
      • Platform authority: Reddit, Trustpilot, G2, and industry forums get treated as trusted sources.
      • Recurrence across sources: If the same issue appears in multiple places, AI engines treat it as a verified pattern.

      The 4-Step Framework: How to Audit, Remove, Rebuild, and Suppress Your Brand’s AI Reputation Signals

      Understanding what’s in your negative signal footprint, prioritizing what can and should be addressed, and building a positive content layer that represents your brand accurately when AI tools pull information is the key to success.

      Map what AI engines can access about your brand across platforms where complaints surface.

      1. Open ChatGPT or Perplexity and type: “What are the pros and cons of [your brand] vs [top competitor]?” Take a screenshot of the response and note any negative claims.
      2. On Google, search site:[key platform].com “[your brand name]” + “scam” OR “complaint”. This forces the search engine to show you only the filtered conversations AI models are currently scraping.
      3. Search for your brand on Google and check the featured snippets for anything negative, other SERP features like People also ask for negative or adversarial searches.

      Key platforms to check:

      • Review platforms (Trustpilot, G2, Capterra, Yelp, Google Business Profile).
      • Reddit (search your brand name + product category + complaint terms).
      • Industry forums (Stack Overflow for tech, niche communities for specialized services).
      • Facebook groups and community pages (particularly industry-specific or local groups where your customers congregate).
      • Social media (Twitter/X, LinkedIn discussions, TikTok comments).
      • Legacy gripe sites (RipoffReport, Complaintsboard); while largely deindexed, content may still be cited by AI engines.

      Document these details:

      • Content type and platform.
      • Date posted.
      • Specific claims made.
      • Factual accuracy.
      • Current visibility in Google and AI summaries.

      Focus on detailed complaints with enough context that AI engines might treat them as credible sources.

      Step 2: Prioritize Based on Surfacing Likelihood

      Focus on:

      • High priority: Recent complaints with specific details, issues mentioned across multiple platforms, content on high-authority platforms (Reddit, major review sites), complaints naming features or pricing specifically.
      • Medium priority: Older complaints (1-2 years) still in search results, isolated reviews without corroboration.
      • Low priority: Very old content (3+ years) with low engagement, complaints about discontinued products.

      How To Create A Priority Matrix

      Create a simple scoring matrix to decide what to tackle first:

      • High Priority: Content that appears in AI summaries AND has high organic visibility (check Semrush or Ahrefs for estimated monthly visits to that specific URL) or compare them against queries for those keywords that you have available in search console – if it’s a branded search, you should have full visibility on this from search console.
      • Verified Impact: For platform-specific reviews (G2, Trustpilot, Google Business), use your internal analytics to track how many users are clicking “Helpful” on negative reviews. A review with 50+ “Helpful” votes is a massive signal that AI engines will not ignore.

      Step 3: Remove or Respond Where Possible

      Some negative content can be removed outright. Some deserve a response, and some require both.

      How to Get Negative Content Taken Down

      If the content violates platform policies (false information, impersonation, harassment), request removal through the platform’s reporting process.

      For legacy complaint sites and gripe sites, professional content removal services can often negotiate takedowns based on inaccuracies or policy violations, though as reputation defense strategies evolve for AI, the focus has shifted from simply removing content to building stronger positive signals.

      For content that mentions you but doesn’t necessarily focus on your brand (like a Reddit thread comparing five tools where yours gets one negative mention), removal usually isn’t an option, but you can dilute its impact by ensuring positive mentions appear more frequently in similar discussions.

      When Responding Publicly Actually Helps You

      Legitimate complaints about real issues, misunderstandings you can clarify with facts, or service failures where an explanation adds credibility. Keep responses factual, non-defensive, and focused on resolution. AI engines can pull your response into summaries, giving you a chance to reframe the narrative.

      When Engaging Makes Things Worse — Skip It

      Fake reviews, emotional rants without substance, old complaints about discontinued products, or situations where engagement will amplify visibility.

      Step 4: Build a Positive Content Layer That AI Engines Prefer

      This is where ongoing reputation management becomes critical. You need owned and earned content that AI engines will preferentially cite when answering comparison queries.

      What Goes Into A Positive Content Layer

      • Structured FAQ content: Create pages answering common objections and questions with clear headers and schema markup.
      • Case studies: Detailed examples with metrics, timelines, and direct customer quotes give AI engines concrete data to cite.
      • Community presence: Contribute to Reddit and forums where your audience asks questions. Build credibility through value, not promotion.
      • Third-party validation: Get featured in roundups and comparison articles on authoritative sites.
      • Regular content updates: AI models prioritize recent content. Keep your owned content fresh.
      • How this plays into broader online reputation management: What you’re building isn’t just an AI strategy—it’s a defensible reputation infrastructure. Comprehensive, recent, authoritative content across multiple touchpoints creates a buffer that makes it harder for isolated negative signals to dominate.

      How To Build A Positive Content Layer 

      1. Turn your FAQ into a knowledge base that addresses common objections (e.g., “Is [your brand] worth the price?”). Depending on how much reach and authority your brand has, it can be worthwhile to publish these as their own pages with a clear H1 question as the headline and breadcrumb the Q and As in a format like /faq/[service area]/[objection] to create more internal linking opportunities and depth rather than just having everything on a massive FAQ page.
      2. Reach out to some of your satisfied customers and ask for a 2–3 sentence quote about a specific outcome they achieved. Publish these as a case study snippet on your site. Specificity (metrics, timeframes) helps to ensure LLMs treat content as credible evidence rather than marketing copy. Link to their LinkedIn or business website, if possible, to help reinforce that it is a real review for a real customer.
      3. Identify high-authority “Best of” lists or industry roundups where your brand is missing and email the editors to provide a unique expert insight or updated product data for inclusion. These seed high-trust citations that AI engines prioritize when synthesizing brand comparisons and reputation summaries. The higher they rank on Google, the better.

      Monitoring becomes essential at this stage. Track which keywords trigger AI Overviews that mention your brand, watch for new complaints surfacing in high-authority platforms, and measure whether your positive content is getting cited in AI-generated comparisons. This isn’t a one-time project; it’s an ongoing program.

      Start Here: Your Easy Steps to Managing Your AI Reputation

      If you’re dealing with high-stakes reputation issues where missteps could amplify problems, specialized online reputation management services and experts like our team at erase.com can help you move faster and avoid pitfalls. The goal isn’t just reacting to what’s already out there; it’s building a system where positive signals consistently outweigh isolated negatives when AI engines scan for information.

      The shift is already here. The question is whether you’re managing it proactively or discovering it reactively when a prospect mentions “something they saw in ChatGPT.”

      LEARN MORE

      Image Credits

      Featured Image: Image by Erase.com. Used with permission.

      Ecommerce MGMT 0 Comments
      Apr 30 2026
      The 90-Day GEO Playbook for Local Search: How To Show Up When AI Does The Searching

      This post was sponsored by Uberall. The opinions expressed in this article are the sponsor’s own.

      Local consumers have stopped searching the way we built our marketing around.

      This significant change in buyer habits has been quietly happening in the last 18 to 24 months.

      According to recent Uberall research into AI search behavior, an estimated $750 billion in consumer spend is already shifting toward AI-powered search. Roughly 60% of all searches now end without a single click to a website. And in a finding that should stop every marketer cold, or at least those working for multi-location businesses, 68% of brands are missing entirely from the recommendations AI engines generate in their category.

      That problem goes beyond channels. It’s a fast-moving visibility problem that risks affecting conversions and revenue.

      Generative Engine Optimization (GEO) is the discipline built for this moment. Where SEO optimized pages for a ranking, GEO optimizes entities for a recommendation.

      The goal is no longer just to be found in Search Engine Results Pages (SERPs). It’s to be cited, summarized, and trusted when a model answers on your customer’s behalf.

      Get Your Checklist & Follow Along

      In GEO, three pillars carry the weight. If you’ve worked in SEO for any length of time, the shape will look familiar — compounding visibility isn’t new, it’s the surface that’s changed.

      • Source of truth. The basic facts about your brand (name, address, hours, services) need to match everywhere a model might look. Inconsistent signals train AI engines to trust you less.
      • Context engineering. Your content has to answer the questions customers actually ask, in the language they ask them. Of course, conversational answers should take priority over keyword clusters.
      • Orchestration. You measure citations, refresh content, and compound visibility over time.

      Here is how those three pillars translate into a realistic 90-day plan teams can actually run.

      Phase 1 (Week 1): Foundational Analysis

      You cannot optimize what the model cannot parse. The first week is a data hygiene sprint, rather than a content sprint.

      Start with the local SEO basics most teams assume are already clean:

      • Audit your NAP details (Name, Address, Phone) across Google Business Profiles, Apple Maps, Yelp, Bing Places, and the major data aggregators. Even small inconsistencies — a missing suite number, an old phone format, a rebrand that never propagated — train AI engines to treat your brand as a lower-confidence entity.
      • Check your location pages, about page, and product pages for structured data. Schema isn’t a magic AI switch — recent tests suggest LLMs largely read it like any other on-page text. What it does is reduce ambiguity about what your business is and does, and that clarity is what helps a model interpret and cite you correctly.
      • Type the questions your customers actually ask into ChatGPT, Gemini, Perplexity, and Google AI Overviews. Not branded queries – real ones like “best orthodontist near Lincoln Park,” “which EV charger works with a Ford Lightning,” “coffee shops in Berlin that allow dogs.” Note where you appear, where you don’t, and which competitors show up instead.

      That gap list becomes your brief for the next 80 days. It’s also where most brands discover the blind spots they didn’t know they had.

      Phase 2 (Days 7–30): Context Engineering And Targeted Content

      Once you know which prompts you’re missing from, the work becomes specific. For each blind spot, you are building the content a model would actively want to cite.

      A few patterns that hold up across industries:

      • One prompt, one page. If “best family dentist in Austin with Saturday hours” returns three competitors and none of your locations, build or optimize the pages that answer exactly that. Don’t bury the answer three scrolls down.
      • Write for the question, not the keyword. AI engines extract complete answers, not phrases. A well-structured FAQ with direct, factual responses often outperforms a 2,000-word, keyword-stuffed guide that dances around the point
      • Cite yourself credibly. Include dates, local details, original data, named authors, and explicit comparisons. Models reward specificity and downgrade vague claims.

      This is the phase where content that actually gets cited starts to look different from content built for the old ranking game. It is tighter, more factual, and structured around how someone would ask a question out loud.

      Phase 3 (Days 30–60): Surgical Placement & Off-Page Authority

      Off-page authority still matters. The economics, however, have flipped.

      The instinct is to chase top-tier publishers. For GEO, that is usually the wrong move.

      The sites that generative engines pull from most often aren’t always the ones with the highest domain authority. These are the ones relevant to your business and are cited more frequently, even if they’re not huge publications.

      A more effective approach:

      • Focus on sites that already rank in Google for the prompts your customers use — the kind of credible, topical sources you’d want them to find when they’re researching. Top-tier placement isn’t the goal; any authoritative site that actually serves your audience counts.
      • The publishers AI engines already cite in your category are the ones models trust enough to source from. Re-run your Phase 1 prompts, track which domains keep appearing in the citations, and that’s your shortlist.
      • Size and prestige aren’t reliable proxies for AI citation rates. A specialist publication with real topical authority in your category often earns more AI citations than a bigger, more generic name.

      The goal isn’t link volume. It is being mentioned, in context, in the sources your category’s models already trust.

      Phase 4 (Days 60–90): Orchestration And Compounding

      By day 60, you should have new content live, citations starting to show up on publisher sites, and enough signal to measure. Phase 4 is where GEO stops being a project and starts being a system.

      Three metrics worth tracking weekly:

      • AI citation rate — how often your brand is named in AI-generated answers for your priority prompts.
      • Share of Voice — your citation rate relative to competitors across the same prompt set.
      • Content decay — which cited pages are losing citations over time and need refreshing with new data, dates, or insights.
      Image created by Uberall, April 2026

      The compounding effect here is profound. Brands that treat GEO as an ongoing loop — audit, publish, place, measure, refresh — see substantially higher citations and conversion rates. A recent Search Engine Journal webinar, featuring Uberall with AthenaHQ, states that GEO-savvy brands see 2x as many citations and 3–9x higher conversion rates within 90 days compared to brands still optimizing purely for classic search.

      That delta matters more than it looks. As zero-click behavior grows, the citation inside the AI answer is the conversion surface.

      For a concrete example, Audika France, a multi-location hearing-care brand and Uberall customer, ran this orchestration loop as an early adopter. They used it to track how AI engines described their clinics, spot the attributes models were missing, and close the gap between visible and recommended. Their results show how one multi-location brand went from an AI blind spot to a consistent recommendation.

      What To Do Next

      The pattern is consistent across multiple industries, including retail and restaurants. Brands that start now build a structural advantage that is hard to unwind once the category catches up. The ones that wait end up explaining to their board a year from now why a competitor became the default recommendation in every model their customers use.

      If you want a snapshot of how your locations are performing in AI search, check out our AI Visibility Grader tool. It gives you a quick view of your AI visibility and the factors shaping it.

      Or if you want to take this further and get a higher definition picture of where you stand in AI search, GEO Studio’s free trial will map your brand’s presence across the major generative engines.

      Local search has changed. This is how you become the default answer.

      TAKE CONTROL OF AI SEARCH

      Image Credits

      Featured Image: Image by Michelle Azar/ Uberall. Used with permission.
      In-Post Image: Image by Uberall. Used with permission.

      Ecommerce MGMT 0 Comments
      Apr 9 2026
      Breaking Content & SEO Silos To Build Entity Authority in AI Search

      This post was sponsored by Victorious. The opinions expressed in this article are the sponsor’s own. 

      Improving search visibility across traditional and AI search requires evolving our methods and updating how teams work together to improve outcomes.

      Content teams and SEO teams have always needed each other. But with AI search raising the bar on entity authority, the cost of operating in silos has never been higher. This framework is how you close that gap.

      Why AEO Makes SEO & Content Collaboration Non-Negotiable

      Historically, content and SEO teams have both pursued organic visibility, though they often worked independently. While it’s always been ideal for these teams to collaborate effectively, with answer engine optimization (AEO), it’s more critical than ever that they work together to strengthen a site’s entity associations and improve its retrieval opportunities.

      What Is AEO?

      AEO, which is also called generative engine optimization (GEO), is the process of improving a website’s content and technical foundations to make it easier for AI crawlers to read and extract content. AEO aims to improve brand citations and mentions and requires SEO and content teams to work together to improve entity targeting, semantic associations, content quality, content comprehensiveness, and content structure, among other things.

      Without entity-level coordination, brands may fail to gain traction in AI search surfaces and lose AI citation and mention opportunities to competitors. Let’s break it down. AI Overviews (those AI generated snippets at the top of Google search results) cite websites that demonstrate concentrated authority (backed by external sources) on specific entities. Websites with consistent messaging around their core services and products backed by external corroboration like backlinks and PR mentions appear in knowledge panels and other search features. So, when content depth and external link validation operate independently, sites miss retrieval opportunities across AI-powered search.

      Entities provide the framework for this collaboration. When content and SEO strategies align around building authority for the same entities, teams can execute coordinated work that strengthens both content comprehensiveness and external validation.

      How Entities Provide a Shared Framework

      Entities are distinct concepts that search systems can uniquely identify and connect. Unlike keywords, entities are semantic concepts with attributes and relationships. “Customer onboarding” as an entity connects to “user adoption,” “product activation,” “time to value,” and “customer success.” To get cited, brands need to build entity authority.

      What Is Entity Authority?

      Entity authority is the degree to which search systems recognize your brand as a credible, well-corroborated source on a specific entity. A site with strong entity authority for “resource planning” has comprehensive content on the topic, earns links from sources that also discuss it, and structures that content so search systems can map the relationships between related concepts.

      Search systems evaluate entity authority on three dimensions:

      • Recognition: Can they identify which entities your content addresses?
      • Relationships: Do they understand how those entities connect?
      • Corroboration: Do external sources validate your entity representations?

      These evaluation criteria create natural points of coordination. When both teams work toward the same entity authority goals, their work reinforces the same recognition, relationship, and corroboration signals that search systems use to evaluate expertise.

      Why Neither Team Can Do This Alone

      SEO teams could identify target entities and pursue entity-focused optimization independently. But without comprehensive content coverage, the technical infrastructure (schema, internal linking, site architecture) would connect thin, scattered content that doesn’t demonstrate depth. Conversely, content teams could create full-funnel entity coverage independently. But without the technical entity infrastructure and external corroboration through entity-relevant backlinks, the content lacks the structural and external signals that strengthen entity authority.

      The coordination creates what neither discipline can build alone: comprehensive content backed by both technical entity infrastructure and external sources.

      Putting Entity Authority Into Practice

      Start by choosing 3–5 core topics your business wants to be known for, then consistently build content and links around those topics. Instead of spreading effort across dozens of disconnected ideas, SEO and content teams focus on reinforcing the same few areas until search systems clearly associate your brand with them.

      Entities work as an organizing principle because they’re specific enough to guide both disciplines. Instead of content planning around vague topics and SEO chasing domain authority, both teams can focus on, say, “resource planning,” specifically.

      Content creates guides, research, and comparisons on resource planning. SEO builds links from publications discussing resource planning. Both reinforce the same entity signals, and the compounding effect of that alignment is what separates brands that gain AI retrieval from those that don’t.

      What an Entity-Focused Collaboration Workflow Looks Like

      We propose a four-phase workflow that enables teams to test entity strategies and adapt based on performance.

      Image created by Victorious, March 2026

      Phase 1: SEO Conducts Entity Research

      SEO begins by identifying entities aligned to the business’s services or products. Through vector embedding analysis (using tools like Google’s Natural Language API or Semrush to create a numerical representation of semantic associations), the team identifies related topics (entity associations) that would build authority for these main entities. This analysis reveals patterns of topic similarity and competitive gaps.

      During this phase, SEO also analyzes link velocity requirements for each main entity, with the understanding that link building will be distributed across the entity cluster. This entity cluster would include pages with different search intents that cover different aspects of the same concept (entity). The output is a shortlist of main entities with their associated entities, aligned with business objectives and realistic resource constraints.

      For a project management platform, the main entity might be “project management,” with associated entities like “resource planning,” “capacity management,” and “project forecasting.” Focusing on a limited number of main entities allows both teams to commit sufficient resources to build depth rather than scattering effort across too many targets.

      Phase 2: SEO and Content Teams Analyze Content Gaps and Prioritize Impact

      The teams review existing content coverage for each target entity together. They identify gaps across the buyer journey (awareness, consideration, decision) and prioritize which assets to create based on competitive need, business impact, and available resources. This isn’t content asking “what should we write?” or SEO saying “we need these pieces.”

      Both teams evaluate comprehensiveness together:

      • Does the entity coverage span formats (research, guides, comparisons, how-tos)?
      • Does it address different stages of the buyer journey?
      • Does it create the depth that AI systems recognize as authority?

      At this point, the teams also align on success metrics. Each team needs to agree on what entity authority looks like for the target entities and which signals will indicate progress, taking into account current content performance. This shared measurement framework ensures both teams work toward the same definition of success.

      At the end of this phase, the teams should have a prioritized content plan showing which assets support which entities, target publication dates, and metrics for measuring entity authority growth.

      Where Most Teams Break Down

      Content and SEO often report into different leaders, operate on different timelines, and measure success differently. Content teams may focus on production and engagement, while SEO teams may focus on rankings and links. Without a shared framework, priorities drift and execution becomes fragmented.

      Aligning around entities gives both teams a common target, so decisions about what to create, what to promote, and what to fix all point in the same direction.

      Phase 3: Both Teams Execute on the Plan

      Content creates and publishes the planned assets. SEO implements schema markup to highlight entity relationships, analyzes and fixes internal linking between entity clusters, and executes backlink building using entity-relevant anchor text and targeting publications that discuss those entities.

      When prioritizing internal linking fixes, SEO focuses first on pages that already have topical relevance to the target entity but lack incoming links from related content, as these represent the fastest wins for entity cluster cohesion. For anchor text, the goal is to show natural variation rather than exact-match repetition to avoid over-optimization. Links also may not necessarily point to newly published content. What matters is that link velocity, anchor text, and link sources all reinforce the same entity associations that the content is building.

      The goal here is entity-level coordination over piece-level coordination. Content and SEO teams work toward improving entity authority together.

      Phase 4: Teams Assess Performance and Refine Plan

      Together, the teams track implementation progress and entity authority signals to determine whether their efforts are improving brand visibility and ultimately, the bottom line for the business.

      They’ll monitor ranking increases for related terms, since organic visibility influences AI citation opportunities. They also track AI Overview citations when users search entity-related queries (e.g., “[entity] best practices,” “[entity] solutions”) and frequency of brand mentions in AI-generated responses.

      Traditional metrics like traffic and conversions emerge later as lagging indicators. Teams use the early signals to refine the plan: maintain the current approach, accelerate investment in high-performing entity clusters, or adjust tactics for underperforming entities.

      Example: Resource Planning Entity in Action

      Vector embedding analysis at a SaaS project management platform reveals “resource planning” as an entity association with strong similarity to their main “project management” entity. Building authority on resource planning would strengthen their overall project management authority. Competitive analysis shows they need consistent link velocity over six months to reach parity. (This six-month timeline assumes a moderately competitive landscape. In more saturated categories, building to parity may take longer, and teams should calibrate expectations based on their specific competitive environment before committing to a roadmap.)

      A joint review of existing coverage reveals one surface-level blog post on resource planning basics. Competitive sites have research on resource allocation trends, comprehensive guides on capacity planning, comparison content evaluating resource planning approaches, and implementation how-tos. The gap is clear.

      Together, they prioritize:

      • Awareness: Original research on resource planning practices
      • Consideration: A comprehensive resource planning guide
      • Consideration: A comparison of resource planning methodologies
      • Decision: Implementation guides for different team structures

      Over three months, the content team publishes the planned assets while SEO implements schema, tightens internal linking across the entity cluster, and builds links from project management publications to pages across the site, not just the new content. They start looking for organic ranking changes, branded traffic changes, and AI citation rates.

      After four months, visibility increases for resource planning queries across multiple pages, not just the newly published content. The research piece earns two AI Overview citations. These results reflect the entity strategy working as designed: content depth, technical infrastructure, and external corroboration all reinforcing the same entity signals together. Neither outcome would have happened on the same timeline if the teams had executed independently. That’s the compounding effect of entity-level coordination in practice.

      It’s Time To Move Toward Structured Experimentation

      Entity-focused collaboration isn’t a fixed formula, but rather, a framework for structured experimentation. Teams will need to test which entity associations drive the strongest authority signals, which content formats generate the most AI citations, and which link-building strategies accelerate entity recognition most effectively.

      Though the workflow outlined here provides a starting structure, iteration is expected. You’ll likely find that entity clusters don’t build authority at the same pace, buyer journey stages that seem less critical may drive unexpected retrieval, link velocity requirements vary by competitive landscape, and the measurement signals themselves evolve as AI search capabilities change.

      Flexibility is essential. Teams need space to test approaches, measure what works, and adapt quickly. Tighter coordination between content and SEO enables faster learning cycles. When both teams work from the same entity framework and shared success metrics, they can identify what’s working and shift resources accordingly. The brands that establish entity authority now, before AI search surfaces fully mature, will be significantly harder to displace later.

      BOOK YOUR AEO STRATEGY SESSION

      Image Credits

      Featured Image: Image by Victorious. Used with permission.

      Ecommerce MGMT 0 Comments
      Apr 7 2026
      ChatGPT Now Crawls 3.6x More Than Googlebot: What 24M Requests Reveal

      This post was sponsored by Alli AI. The opinions expressed in this article are the sponsor’s own. 

      Everyone assumes Googlebot is the dominant crawler hitting their website. That assumption is now wrong.

      We analyzed 24,411,048 proxy requests across 78,000+ pages on 69 customer websites on Alli AI’s crawler enablement platform over a 55-day period (January to March 2026). OpenAI’s ChatGPT-User crawler made 3.6x more requests than Googlebot across our data sample. And that’s not even counting GPTBot, OpenAI’s separate training crawler.

      A note on methodology: Crawler identification used user agent string matching, verified against published IP ranges. Request metrics are measured at the proxy/CDN layer. The dataset covers 69 websites across a variety of industries and sizes, predominantly WordPress-based. Full methodology is detailed at the end.

      Finding 1: AI Crawlers Now Outpace Google 3.6x & ChatGPT Leads the Pack

      Image created by Alli AI, April 2026.

      When we ranked every identified crawler by request volume, the results were unambiguous:

      Rank Crawler Requests Category
      1 ChatGPT-User (OpenAI) 133,361 AI Search
      2 Googlebot 37,426 Traditional Search
      3 Amazonbot 35,728 AI / E-Commerce
      4 Bingbot 18,280 Traditional Search
      5 ClaudeBot (Anthropic) 13,918 AI Search
      6 MetaBot 10,756 Social
      7 GPTBot (OpenAI) 8,864 AI Training
      8 Applebot 6,794 AI Search
      9 Bytespider (ByteDance) 6,644 AI Training
      10 PerplexityBot 5,731 AI Search

      ChatGPT-User made more requests than Googlebot, Amazonbot, and Bingbot combined.

      Image created by Alli AI, April 2026.

      Grouped by purpose, AI-related crawlers (ChatGPT-User, GPTBot, ClaudeBot, Amazonbot, Applebot, Bytespider, PerplexityBot, CCBot) made 213,477 requests versus 59,353 for traditional search crawlers (Googlebot, Bingbot, YandexBot). AI crawlers are now making 3.6x more requests than traditional search crawlers across our network.

      Finding 2: OpenAI Uses 2 Crawlers (And Most Sites Don’t Know the Difference)

      Image created by Alli AI, April 2026.

      OpenAI operates two distinct crawlers with very different purposes.

      ChatGPT-User is the retrieval crawler. It fetches pages in real time when users ask ChatGPT questions that require up-to-date web information. This determines whether your content appears in ChatGPT’s answers.

      GPTBot is the training crawler. It collects data to improve OpenAI’s models. Many sites block GPTBot via robots.txt but not ChatGPT-User, or vice versa, without understanding the distinct consequences of each.

      Combined, OpenAI’s crawlers made 142,225 requests: 3.8x Googlebot’s volume.

      The robots.txt directives are separate:

      User-agent: GPTBot      # Training crawler — feeds OpenAI's models
User-agent: ChatGPT-User # Retrieval crawler — fetches pages for ChatGPT answers

      Finding 3: AI Crawlers Are Faster & More Reliable, But Their Volume Adds Up

      Image created by Alli AI, April 2026.

      AI crawlers are significantly more efficient per request:

      Crawler Avg Response Time 200 Success Rate
      PerplexityBot 8ms 100%
      ChatGPT-User 11ms 99.99%
      GPTBot 12ms 99.9%
      ClaudeBot 21ms 99.9%
      Bingbot 42ms 98.4%
      Googlebot 84ms 96.3%

      Two likely reasons. First, AI retrieval crawlers are fetching specific pages in response to user queries, not exhaustively discovering site architecture. They know what they want, they grab it, and they leave. Second, while all crawlers on our infrastructure receive pre-rendered responses, Googlebot’s broader crawl pattern means it requests a wider range of URLs, including stale paths from sitemaps and its own legacy index, which adds latency from redirect chains and error handling that retrieval crawlers avoid entirely.

      But there’s a catch: while each individual request is lightweight, the sheer volume means aggregate server load is substantial. ChatGPT-User at 11ms × 133,361 requests is still a real infrastructure cost, just distributed differently than Googlebot’s fewer, heavier requests.

      Finding 4: Googlebot Sees a Different (Worse) Version of Your Site

      Image created by Alli AI, April 2026.

      Googlebot’s 96.3% success rate versus near-perfect rates for AI crawlers reveals an important structural difference.

      Googlebot received 624 blocked responses (403) and 480 not found errors (404), accounting for 3% of its requests. Meanwhile, ChatGPT-User achieved 99.99% success. PerplexityBot hit a perfect 100%.

      Image created by Alli AI, April 2026.

      Why the gap? The most likely explanation is index age and crawl behavior, not site misconfiguration.

      Googlebot maintains a massive legacy index built over years of continuous crawling. It routinely re-requests URLs it already knows about — including pages that have since been deleted (404s) or restructured (403s). This is normal behavior for a search engine maintaining an index of this scale, but it means a meaningful percentage of Googlebot’s requests are directed at URLs that no longer exist.

      AI crawlers don’t carry that baggage. ChatGPT-User fetches specific pages in response to real-time user queries, targeting content that’s currently relevant and linked. That’s a structural advantage that produces near-perfect success rates.

      Industry Reports Confirm AI Crawling Surged 15x in 2025

      These findings align with broader industry trends. Cloudflare’s 2025 analysis reported ChatGPT-User requests surging 2,825% YoY, with AI “user action” crawling increasing more than 15x over the course of 2025. Akamai identified OpenAI as the single largest AI bot operator, accounting for 42.4% of all AI bot requests. Vercel’s analysis of nextjs.org confirmed that none of the major AI crawlers currently render JavaScript.

      Our data shows this crossover may already be happening at the site level for properties that actively enable AI crawler access.

      Your New SEO Strategy: How To Audit, Clean Up & Optimize For AI Crawlers

      1. Audit your robots.txt for AI crawlers today

      Most robots.txt files were written for a Googlebot-first world. At minimum, have explicit directives for ChatGPT-User, GPTBot, ClaudeBot, Amazonbot, PerplexityBot, Applebot, Bytespider, CCBot, and Google-Extended.

      Our recommendation: Most businesses benefit from allowing both retrieval crawlers (ChatGPT-User, PerplexityBot, ClaudeBot) and training crawlers (GPTBot, CCBot, Bytespider), training data is what teaches these models about your brand, products, and expertise. Blocking training crawlers today means AI models learn less about you tomorrow, which reduces your chances of being cited in AI-generated answers down the line.

      The exception: if you have content you specifically need to protect from model training (proprietary research, gated content), use granular Disallow rules for those paths rather than blanket blocks.

      2. Clean up stale URLs in Google Search Console

      Our data shows Googlebot hits a 3% error rate, mostly 403s and 404s, while AI crawlers achieve near-perfect success rates. That gap likely reflects Googlebot re-crawling legacy URLs that no longer exist. But those failed requests still consume the crawl budget.

      Audit your GSC crawl stats for recurring 404s and 403s. Set up proper redirects for restructured URLs and submit updated sitemaps.

      3. Treat AI crawler accessibility as a distinct SEO channel

      Ranking in ChatGPT’s answers, Perplexity’s results, and Claude’s responses is emerging as a distinct visibility channel. If your content isn’t accessible to these crawlers, particularly if you’re running JavaScript-heavy frameworks, you’re invisible in AI search.

      We’ve published a live dashboard showing how AI crawler traffic breaks down across a real site: which platforms are visiting, how often, and their share of total traffic; if you want to see what this looks like in practice.

      4. Plan for volume, not just individual request weight

      AI crawlers send light, fast requests, but they send many of them. ChatGPT-User alone accounted for more than 133,000 requests in 55 days. The aggregate server load from AI crawlers is now likely exceeding your Googlebot load. Make sure your hosting and CDN can handle it, the low per request response times in our data reflect the fact that Alli AI serves pre-rendered static HTML from the CDN edge, which is exactly the kind of architecture that absorbs this volume without taxing your origin server.

      Methodology

      This analysis is based on 24,411,048 HTTP proxy requests processed through Alli AI’s crawler enablement platform between January 14 and March 9, 2026, covering 69 customer websites.

      Crawler identification used user agent string matching, verified against published IP ranges. For OpenAI crawlers specifically, every request was cross-referenced against OpenAI’s published CIDR ranges. This confirmed 100% of GPTBot requests and 99.76% of ChatGPT-User requests originated from OpenAI’s infrastructure. The remaining 0.24% (requests from spoofed user agents) were excluded.

      Limitations: The dataset is scoped to Alli AI customers who have opted into crawler enablement. Crawlers that don’t self-identify via user agent are not captured. Response time measurements are at the proxy layer, not the origin server.

      About Alli AI

      Alli AI provides server-side rendering infrastructure for AI and search engine crawlers. This analysis was produced using data from our proxy infrastructure to help the SEO community better understand the evolving crawler landscape.

      Want to see this data in action? See the breakdown firsthand by visiting our AI visibility dashboard.

      TRY ALLI AI

      Image Credits

      Featured Image: Image by Alli AI. Used with permission.

      In-Post Iamges: Images by Alli AI. Used with permission.

      Ecommerce MGMT 0 Comments
      Apr 6 2026
      How AI Is Changing Lead Generation: 3 Key Things SEO & PPC Teams Need To Do Now via @sejournal, @CallRail

      1. Identify Which AI Platforms Are Driving Your Visitors

      Each LLM and answer engine has different logic, leading to different outputs for the same prompts. It’s important to understand which AI chatbots are aligned with your brand before making decisions that inform a larger AI search or SEO strategy.

      Different LLMs Are Driving Leads In Different Industries

      Not all AI platforms send leads the same way.

      • ChatGPT = Speed. ChatGPT dominates overall lead volume at 90.1% of AI-referred leads, with especially strong numbers in healthcare and automotive industries, where people want instant options.
      • Perplexity = Research. Perplexity accounts for 6.3%, but it punches well above its weight in high-consideration sectors. In Travel & Hospitality and Manufacturing, nearly one in ten AI leads comes from Perplexity, roughly ten times the rate seen in other industries.
      • Google’s Gemini holds 2.4% of AI-referred leads and is gaining traction in Business Service and Manufacturing, likely because users lean on its Google Workspace integration.
      • Claude, with 1.2% of lead generation, is carving out a niche in both Real Estate verticals and also with Marketing Agencies. Especially in areas where consumers tend to do more specific and detailed research before reaching out.

      How To Accurately Track AI Prompt Visibility

      AI search isn’t one channel. It’s a set of distinct platforms, each with different behaviors and industry strengths. So, repeat this AI prompt research phase for each LLM.

      1. Identify the LLMs that matter most for your vertical. Use the data above as a starting point. If you’re in healthcare or automotive, prioritize ChatGPT visibility. High-consideration service? Pay attention to Perplexity. B2B or manufacturing? Gemini should be on your radar.
      2. Test how each platform describes your business. Go to ChatGPT, Perplexity, Gemini, and Claude and ask them questions your customers would ask. “Who’s the best [your service] in [your market]?” See if you’re being recommended. If not, note who is and what content those competitors have that you don’t.
      3. Create content that answers the questions AI platforms are fielding. LLMs favor well-structured, authoritative, fact-rich content. Publish service pages, FAQs, comparison guides, and local content that directly answer the kinds of questions consumers ask these platforms.

      2. Connect AI Traffic To Actual Conversions

      Connecting AI-driven leads to actual revenue in your reporting is key to understanding how to prioritize your marketing activities. Without visibility into AI lead attribution, you’re making decisions in the dark, which is an expensive place to be.

      However, if you can identify AI as the source of your best leads, you instantly know how to pivot your SEO strategy.

      How To Track AI Traffic & Attribute Conversions Across ChatGPT, Gemini, and Perplexity

      As more money flows through AI search, the ability to attribute leads from specific LLMs isn’t a nice-to-have. It’s the difference between knowing what’s working and throwing budget at a black box.

      What you need is the ability to trace a lead from the AI platform where it originated, through the call, form, or chat where it converted, all the way to the revenue it generated. That full-funnel visibility is what separates data-driven teams from everyone else.

      1. Implement LLM-specific attribution. Use a platform that can identify which AI model referred each lead. CallRail’s AI search engine attribution, for example, automatically tags whether an inbound call came from ChatGPT, Perplexity, Gemini, or Claude, not just “AI.” That level of granularity is what makes it possible to actually optimize by channel.
      2. Create custom GA4 channel groups for AI traffic. In Google Analytics, go to Admin > Data Display > Channel Groups and create a custom channel group that isolates AI referral traffic by source. This lets you compare AI-driven sessions and conversions against your other channels.
      3. Add “How did you hear about us?” to your intake process. Self-reported attribution (SRA) is a simple but powerful complement to digital tracking. Add it to your intake forms and train front-desk or sales staff to ask on calls. CallRail’s SRA feature lets you capture this data at the conversation level, so you can compare what callers say against what your analytics show. The gaps will reveal exactly where your tracking is falling short.

      See what’s changing: The 2026 Outlook for Marketing Agencies

      Connect AI Traffic to Calls, Forms & Sales Pipelines

      Call tracking lives in one platform. Form submissions in another. Text conversations somewhere else entirely. Sound familiar?

      When your lead data is fragmented like that, it’s surprisingly hard to answer basic questions. Which campaigns drive your best leads? Is AI search actually improving results? Where are leads falling off between first contact and conversion?

      Make sure you are monitoring every lead interaction for complete funnel visibility. Teams need clear insight into every conversation-whether it comes through calls, forms, texts, or chats. And by channel- Paid Search, Video, SEO, Paid Social, and Content, for example.

      Unifying those touchpoints isn’t just a reporting upgrade. It’s the foundation for any AI-ready lead strategy. Without it, every optimization decision you make is based on an incomplete picture. And in a landscape moving this fast, incomplete data leads to costly missteps.

      How To Attribute Calls & Form Fills To AI Search

      Take a good look at what is happening with your Voice Assistants. Are forms going to a shared inbox and being missed? Are calls not being answered while another line is in use or after business hours? How long is it taking to follow up with leads? Are those leads going to the competition after you miss the first call?

      1. Consolidate your lead tracking into one platform. If calls, forms, texts, and chats are living in separate tools, you’re creating blind spots. CallRail’s unified lead intelligence platform captures every touchpoint in a single dashboard, so you can see the full customer journey from first AI search to closed deal, and finally answer the question: which channels are actually driving revenue?
      2. Map every conversion point to a marketing source. For each way a lead can reach you -phone call, web form, text, live chat- make sure you can trace it back to the campaign, channel, or keyword that drove it. Use dynamic number insertion for calls and hidden fields on forms to capture source data automatically.
      3. Build a weekly reporting cadence around lead quality, not just volume. Don’t just count leads, score them. Review which sources produce leads that actually convert to appointments and revenue. This is the reporting your clients care about, and it’s how you prove the value of your work

      Build the foundation: The Agency Roadmap for 2026 and Beyond

      3. Respond Faster To High-Intent AI Traffic

      28% of business calls go unanswered. Many of those leads never call back.

      Take a good look at your Voice Assistants here. Are your forms going to a shared inbox where they sit unread? Are calls going unanswered because another line is busy or it’s after hours? How long does it take your team to follow up with a new lead? And if you miss that first call from an AI-referred prospect who already has high intent and is ready to buy. Are they going straight to your competitor?

      Right now, AI search can understand your customers in real time and answer any question they need, making them perfectly ready to convert into a lead.

      Now, it’s you who has to be ready.

      Dig into the full data: What 20M Leads Reveal About AI Search and High-Intent Calls

      AI Leads Convert Faster. Respond Immediately.

      Think about how the traditional funnel used to work. Someone searches, browses a few sites, reads some reviews, maybe sleeps on it, then reaches out. There were days, sometimes weeks, of consideration built into the process.

      AI has collapsed that timeline dramatically, and AI-directed callers skip the browsing phase entirely.

      They’ve already done their research inside the LLM. By the time they call, they’re ready to make a decision. And they expect you to be ready, too. When a prospect has been pre-qualified by an AI recommendation, every minute of delay costs you revenue.

      And the stakes go beyond individual calls.

      On platforms like Google, answer speed directly impacts your ad rankings. Faster response times earn better placements on Local Service Ads and PPC -meaning slow follow-up doesn’t just lose you a lead, it quietly erodes your visibility and drives up your cost per lead over time. The agencies winning in an AI-search world aren’t just the ones showing up in LLM recommendations. They’re the ones ready to convert the moment the phone rings -day or night.

      Get the playbook: 6 Ways To Prepare Your Business for AI in 2026

      Apply AI Where Your Team Is Stretched Thinnest: Use AI to Capture & Qualify Leads Automatically

      You can’t automate everything. But knowing where to apply AI, specifically, where your agency or internal team is most stretched, is the difference between using it effectively and adding technology for its own sake.

      For most agencies and SMBs, the highest-impact bottleneck is follow-up.

      If your clients are missing calls, responding slowly, or losing leads somewhere between the first touch and a booked appointment, that’s exactly where AI can deliver immediate, measurable value.

      The key to success here is utilizing AI-powered platforms that can answer inbound calls around the clock, qualify leads in real time, capture intake details, and even book appointments automatically. Early adopters have seen answered calls increase by 44%. That’s not a marginal improvement. It’s the kind of shift that directly impacts revenue and client retention.

      How To Set Up AI-Assisted Lead Handling

      When you can connect your AI-assisted lead handling back to attribution data and revenue outcomes, you’re no longer just reporting on activities. You’re proving ROI. And that’s what earns long-term client trust- and moves agencies from being seen as just a lead source to being a true growth partner.

      1. Deploy an AI voice agent for after-hours and overflow calls. Start with the windows where your team is least available -evenings, weekends, and lunch hours. CallRail’s Voice Assist answers, qualifies, and captures lead details automatically, so no high-intent caller falls through the cracks. Early adopters have seen answered calls increase by 44%.
      2. Automate follow-up texts immediately after missed calls. If a call does go unanswered, trigger an automatic text within seconds: “Hi, we just missed your call -how can we help?” This simple automation recovers a meaningful percentage of leads that would otherwise be lost.
      3. Connect your AI lead handling back to attribution. Make sure the leads captured by AI tools feed into the same reporting dashboard as your other channels. If your AI agent books an appointment at 9 pm on a Saturday, you should be able to trace that back to the Google Ad or AI search referral that started the journey.

      Go deeper: Why The Top Marketers Pair Data With Story

      Start Tracking & Optimizing AI-Driven Leads Now

      The shift isn’t on the horizon. It’s already here.

      It’s time to build AI-aware attribution so you can see what’s actually driving leads, unify your data so you can act on it, and respond fast enough to capture the high-intent leads AI search is already sending your way.

      Ecommerce MGMT 0 Comments
      Mar 23 2026
      5 GEO Strategies To Make AI Search Engines Recommend Your Brand In 2026

      This post was sponsored by Geoptie. The opinions expressed in this article are the sponsor’s own. 

      The way people search is changing faster than most marketers realize. ChatGPT alone now has over 900 million weekly active users. Google AI Overviews appear in one out of every four search results.

      Each of these contains the potential for AI to cite your brand.

      This isn’t a future trend. It’s happening right now. And if your brand isn’t showing up in those AI-generated answers, you’re invisible to a rapidly growing audience, even if you rank #1 on Google.

      That’s where Generative Engine Optimization (GEO) comes in: the practice of optimizing your online presence. So, AI engines cite, reference, and recommend your brand when users ask questions in your space.

      1. Start By Measuring Your AI Visibility

      Before changing a single word on your website, you need to know where you stand. Which AI platforms mention your brand? For which queries? How often are your competitors getting cited instead of you?

      You can’t optimize what you don’t measure.

      How To Measure AI Visibility

      Most marketers skip this step because it feels unfamiliar. But the process is straightforward.

      1. List 10–15 questions your ideal customer would ask an AI engine, things like “best [your category] for [use case]” or “how to solve [problem you address].”
      2. Run each query in ChatGPT, Perplexity, and Gemini.
      3. Note whether your brand is mentioned, which competitors show up instead, and whether sources are cited.

      Repeat monthly, because AI-generated answers shift as models update and new content gets indexed. Doing this manually across multiple platforms gets tedious fast, which is why dedicated GEO platforms exist to automate the tracking and monitor changes over time.

      The best place to start? Run a free geo rank check on your brand. In under a minute, you’ll see which AI engines mention you, which ones don’t, and where your competitors show up instead.

      This baseline is essential. Without it, you’re optimizing blind.

      2. Don’t Abandon SEO. It Still Feeds AI

      Here’s an important nuance: traditional search rankings still matter for GEO.

      AI engines frequently pull from top-ranking Google results when generating their responses. If your page ranks well for a relevant query, there’s a higher chance an AI engine will reference it as a source. Google’s own AI Overviews heavily favor content that already performs well in organic search.

      So keep doing what continues to drive SERP rankings:

      • Producing high-quality content
      • Building backlinks
      • Technical SEO.

      But think of SEO as the foundation, not the full strategy. The brands that win in AI search are those that layer GEO tactics on top of a solid SEO foundation.

      3. Make Sure Your Content Follows GEO Best Practices

      This is where most of the work happens. AI engines are selective about what they cite, and the structure and quality of your content play a massive role. Here’s what to focus on:

      • Write for citability, not just readability. AI engines look for content that makes clear, specific claims backed by data or expertise. Vague, fluffy paragraphs get skipped. Concrete statements like definitions, statistics, step-by-step processes, and expert opinions are far more likely to be pulled into a generated response.
      • Structure content around questions. Conversational AI is driven by user questions. Structure your content to directly answer the questions your audience asks. Use clear headers, concise paragraphs, and FAQ When an AI engine scans your page and finds a clean, authoritative answer to a specific question, you become a prime candidate for citation.
      • Leverage schema markup and structured data. Help AI engines understand what your content is about by implementing proper schema FAQ schema, How-To schema, and Organization schema all give AI systems stronger signals about your content’s topic and structure.
      • Build topical authority, not just keyword-specific content. AI engines favor sources that demonstrate deep expertise on a topic. Rather than publishing scattered blog posts across dozens of topics, build comprehensive content clusters that cover a subject thoroughly. This signals to AI engines that your brand is a reliable authority worth citing.

      Pro Tip: Leverage a comprehensive GEO platform. Optimizing your content for AI search involves many moving parts: content structure, schema markup, topical authority, and technical SEO. Keeping track of all these signals manually across every page on your site isn’t realistic, especially as AI engines update how they evaluate sources. A dedicated GEO platform lets you regularly scan your entire website, monitor your optimization scores, and catch issues before they cost you citations.

      Want to see where you stand right now? Run a free GEO audit and get actionable insights on your site’s AI readiness in under a minute.

      4. Show Up In Reddit & UGC Discussions

      Here’s a strategy most brands overlook: AI engines love Reddit.

      If you’ve noticed Reddit threads showing up in Google results more frequently, that’s not a coincidence. Google and AI platforms increasingly treat user-generated content, especially Reddit, as a trusted and authentic source of information. When someone asks an AI engine for a product recommendation or solution comparison, the response often draws from Reddit discussions.

      This means your brand’s presence in relevant threads matters more than ever. But you can’t just show up and start promoting yourself. Here’s how to approach it the right way:

      • Find where your audience is already talking. Search Reddit for your product category, your competitors’ names, and the problems you solve. Identify 5–10 active subreddits where these conversations happen. Look for threads like “what tool do you use for [your category].”  These are the discussions AI engines pull from.
      • Contribute before you promote. Spend at least 2–3 weeks genuinely participating before your brand ever comes up. Reddit users check post history, and if your account is nothing but product mentions, you’ll get flagged as spam.
      • Be honest, not salesy. When a relevant recommendation thread comes up, share your product as one option among others. Mention what it’s good at and where it might not be the best fit. AI engines weigh authentic, nuanced mentions far more heavily than obvious self-promotion.
      • Check what AI engines are citing. Run your core queries in ChatGPT and Perplexity and see which Reddit threads appear. If your brand isn’t in those threads, that’s where to focus.

      5. Get Featured In Listicles On Trusted Sites

      When users ask AI engines for recommendations like “best project management tools,” the AI doesn’t generate that list from scratch. It synthesizes from existing listicle articles on authoritative websites. A single placement in a well-ranking listicle can get your brand recommended across ChatGPT, Perplexity, and Google AI Overviews simultaneously.

      • Find the listicles AI engines are already citing. Run your target recommendation queries in ChatGPT and Perplexity and note which articles they reference. These are the exact listicles you need to be in.
      • Build a hit list of publishers. Identify publications that come up repeatedly across both AI and traditional search results for “best [your category]” queries. Prioritize sites with strong domain authority.
      • Make inclusion easy. Make sure your product pages have a clear one-liner, obvious differentiators, social proof, and transparent pricing. Then pitch authors with something valuable, such as a free account, a demo, or data they can use.

      Listicles get updated regularly and AI engines re-scan them, so a placement you earn today could start driving AI citations within weeks.

      The Window Is Open, For Now

      Generative Engine Optimization is still in its early stages. Most brands haven’t even started thinking about it, which means the opportunity to establish an early advantage is enormous.

      The brands that start measuring their AI visibility, optimizing their content for citability, building community presence, and earning placements in authoritative listicles today will be the ones AI engines default to recommending tomorrow.

      The question isn’t whether AI search will matter for your business. It’s whether you’ll be visible when it does.

      Start Optimizing For AI Search Today

      Every strategy in this article comes down to one thing: making your brand the obvious choice when AI engines look for sources to cite and recommend. You don’t need to tackle everything at once, but you do need to start.

      Geoptie brings all five strategies together in one platform, from tracking your AI visibility across ChatGPT, Perplexity, and Google AI to auditing your content and monitoring your optimization scores over time. It’s built specifically for GEO, so you can stop guessing and start seeing exactly where your brand stands in AI search.

      The early movers will own this space. Make sure you’re one of them.

      Start Tracking Your AI Visibility, Now

      Image Credits

      Featured Image: Image by Tor App. Used with permission.

      Ecommerce MGMT 0 Comments
      Feb 18 2026
      5 Ways Emerging Businesses Can Show up in ChatGPT, Gemini & Perplexity via @sejournal, @nofluffmktg

      This post was sponsored by No Fluff. The opinions expressed in this article are the sponsor’s own.

      When ChatGPT, Gemini, and Perplexity mention a company, these large language models (LLMs) are deciding whether that business is safe to reference, not how long it has existed.

      Most business leaders assume one thing when they don’t show up in AI-generated answers:

      We’re too new.

      In reality, early testing across multiple AI platforms suggests something else is going on. In many cases, the problem has less to do with company age and more to do with how AI systems evaluate structure, repetition, and trust signals.

      It is possible for new brands to be mentioned in AI search results.

      Even well-built products with real expertise are routinely missing from AI recommendations. Yet when buyers ask who to trust, the same legacy names keep appearing.

      Why Most New Businesses Don’t Show Up In AI Search Results

      This isn’t random.

      AI systems lean on existing training data and visible digital footprints, which favor brands that have been cited for years. Because every answer carries risk, these systems act conservatively.

      They don’t look for the most optimized page; they look for the most verifiable entity. If your footprint is thin, inconsistent, or poorly supported by third parties, the AI will often swap you out for a competitor it can trust more easily.

      Most new businesses launch with:

      • Minimal historical signals
        Very little online content or mentions, so AI has almost nothing to work with.
      • Few credibility signals
        Few backlinks, reviews, or press, so you don’t “look” trustworthy yet.
      • Blending brand names
        Similar or generic brand names are easier for AI systems to confuse, misattribute, or skip entirely if trust signals are weak.
      • Unclear positioning
        Unclear positioning or ideas that appear only once on a company website are less likely to be trusted.

      Together, these create unreliable signals.

      In generative search, visibility is less about ranking and more about reasoning.

      This is why most new brands aren’t evaluated as “bad,” but as too uncertain to reference safely.

      That distinction matters. Being referenced by AI is not just exposure; it influences who buyers consider credible before they ever reach a website. AI-referred visitors often convert at higher rates than traditional organic traffic.

      For new businesses, the lack of legacy signals isn’t “just a disadvantage.” Handled correctly, it can be an opening to establish clarity and trust faster than older competitors that rely on outdated authority.

      There’s surprisingly little guidance on whether a new or growing brand can actually appear in AI-generated answers. Given how much these systems depend on past signals, it’s easy to assume established companies appear by default.

      To test that assumption, a brand-new B2B company was tracked from launch as part of a 12-week AI search visibility experiment. The findings below reflect the first six weeks of that ongoing test. The company started with no prior history, no backlinks, and no press coverage. A true zero.

      Visibility was measured across 150 buyer-style prompts in ChatGPT, Google AI Overviews, and Perplexity rather than inferred from third-party dashboards.

      Using weekly GEO sprints focused on technical foundations, answer-first content, and reinforcing signals like social, video, and early backlinks, the goal was to see how far a best-practice GEO playbook could move a truly new brand.

      Within six weeks, the emerging business saw the following results:

      • Appeared in 5% of relevant AI responses.
      • Showed up across 39 of 150 questions.
      • Mentioned 74 times, with 42 cited mentions.
      • 6% citation accuracy, ~11% pointing to the brand’s own site.

      6 Patterns Observed in Early AI Visibility Testing

      Across the first six weeks, six patterns consistently influenced whether the brand was included, replaced by a competitor, or excluded entirely from AI-generated answers:

      Pattern 1: Structure Matters More Than Topic

      Image created by No Fluff, February 2026

      Content that wandered (even if it was thoughtful or “robust”) consistently lagged in AI pickup. The pages that were picked up were tighter: they answered the question up front, broke the content into clear steps, and stuck to one idea at a time.

      Pattern 2: The Social “Amplifier” Effect

      AI is more likely to cite sources it already trusts. In the first two weeks, most citations came from the brand’s LinkedIn and Medium posts rather than its website. For a new brand, publishing key ideas first on high-authority platforms, including LinkedIn or Medium, often triggers AI pickup before the same content is indexed on your own website.

      Image created by No Fluff, February 2026

      Pattern 3: Hallucinations are Often Signal Failures

      Image created by No Fluff, February 2026

      When AI systems misidentify a new brand or confuse it with competitors, the cause is typically thin, slow, or conflicting signals. When pages failed to load within roughly 5–15 seconds, AI systems issue broader “fan-out” queries and assemble answers from adjacent or incorrect sources. Following improvements in site speed, crawl reliability, and entity clarity, the share of answers that correctly referenced this company’s own domain increased, while misattributed mentions declined.

      Pattern 4: The 3-Week Indexing Window

      The first AI pickup from a new domain can happen within three to four weeks. In this experiment, the first page was discovered on day 27. After that initial discovery, subsequent pages were picked up faster, with the shortest lag around eight days.

      Image created by No Fluff, February 2026

      Early inclusion wasn’t driven by content volume. It was driven by structure: a solid schema, consistent metadata, a clean, crawlable site, and machine-readable files such as llms.txt.

      Pattern 5: Win the Explanatory Round First

      New brands typically will not start by winning highly competitive, decision-stage prompts like “best” or “top” lists, unless the offering is truly unique or non-competitive. Before a brand can realistically be shortlisted, it must first be sourced as a primary authority for definitional or educational questions.

      In the first 45 days, the goal wasn’t comparison visibility, but recognition and trust: getting AI systems to associate the brand with the right topics and sources. Early success is best measured by citation frequency, or how often a brand is used as the primary source for a given topic.

      Pattern 6: Solve the Unfinished Trust Gap (Most Important)

      Even with a well-structured site and strong content, brands struggle to get recommended without outside validation. The initial stages of this experiment showed AI answers defaulted to familiar domains and replaced newer brands with competitors that had clearer third-party mentions. This validates the importance of press and authoritative coverage early on. Waiting to “add it later” only slows trust.

      5 Steps To Set A New Business Up For AI Visible Success

      By now, the takeaway is clear: AI visibility doesn’t happen automatically once a site is live or a few campaigns are running. The good news is that this can be influenced deliberately. The steps below reflect the sequence that consistently moved a new brand from zero visibility to being cited in AI-generated answers. Rather than treating AI visibility as a side effect of SEO, this approach treats it as an operational problem: how to make a brand easy for AI systems to recognize, verify, and reuse.

      Step 1: Map Your Brand Entity

      Before building a site, you must define your brand in a way machines understand. ChatGPT, Gemini, and Perplexity don’t read your website the way humans do. They connect facts, names, and relationships into entities that define who you are. If those connections are missing or inconsistent, your brand simply won’t appear (no matter how much content you publish).

      • Define your business clearly using semantic triples: Use the [Subject] → [Predicate] → [Object] format (e.g., “Brand X” → “offers” → “Service Y”) to provide machine-readable facts.
      • Stick to public, widely understood language: Pull terminology from widely accepted sources like Wikipedia or Wikidata. If you describe your product using internal jargon that doesn’t match how the category is commonly defined, you risk being misclassified or overlooked.
      • State your authority: Define why your brand deserves trust. What facts, evidence, and proof back you up? Write 3–5 simple, factual claims you want to be known for.
      • Define your competitive counter-position: Be clear about what makes you different. Scope the specific niche you own (audience, problem, angle, or offering) that sets you apart from alternatives.

      Step 2: Engineer Your Benchmark Prompt Set

      You cannot rely on traditional SEO tools designed to track AI visibility. Most rely on inferred data or simulations, not on real prompts.

      • Map the competitive landscape: Identify which brands AI systems already reference, which buyer questions are realistically winnable, and where category language creates confusion.
      • Reverse-engineer buyer questions: Identify how buyers phrase real questions using keyword and competitor analysis (SEO tool data, People Also Ask, Google SERPS, and asking multiple AI engines themselves)
      • Lock your data set: Create a fixed set of 150 buyer-authentic questions across six clusters: Branded, Category, Problem, Comparison, and Advanced Semantic.
      • Start testing: Run these prompts weekly across ChatGPT, Gemini, and Perplexity to track your mentions and citation growth.

      Step 3:  Make the Brand Machine-Readable

      Make your site machine-readable to ensure AI bots don’t skip your content. AI systems don’t care about your website’s aesthetic; they care about how easily they can parse your data. If your technical signals are thin or conflicting, AI will hallucinate or substitute your brand with a competitor.

      • Implement JSON-LD Schema: Use Organization, Service, and FAQ schemas to tell AI exactly who you are and what you do.
      • Deploy an txt File: Place this at your domain root to provide a plain-text guide for AI crawlers, telling them how to describe your company and which pages to prioritize.
      • Eliminate crawling issues: Make sure your site is fully crawlable via robots.txt and that no content is hidden in gated PDFs or images. Most importantly, check site speed using PageSpeed Insights. Models don’t patiently wait for slow pages!

      Step 4:  Publish “Retrieval-Ready” Content

      Write for the impatient analyst (the AI bot). Start with high-leverage prompts, questions with real buyer intent that AI already answers, but only using a small and weak set of sources, making them easier to influence before trust fully locks in.

      • Lead with the answer: Start every section with a direct, factual answer.
      • Chunk semantically: Divide content into logical, independent sections that can be extracted and reused by AI without requiring the context of the entire page.
      • Consider the freshness factor: AI favors content updated within the last 60–90 days. For high-competition sectors like SaaS or Finance, content should be refreshed every three months to remain a “trusted” recommendation.

      Step 5:  Earn External Validation

      AI systems cross-check your site’s claims against the rest of the web.

      • Claim directory profiles: Align your entity data across Crunchbase, G2, LinkedIn, and Yelp. Inconsistencies across these profiles are a primary cause of AI hallucinations.
      • Target authoritative mentions: Secure mentions in industry-specific publications with consistent pickup throughout your prompts and or a strong domain rating.
      • External reinforcement: For every important page on your site, aim for at least three intentional external link-backs from authoritative sources to trigger AI pickup.

      The Biggest Takeaway: Prioritize Authority as a Long-Term Game

      For new brands, the limiting factor in AI search is not optimization. It’s authority.

      AI systems are more likely to surface unfamiliar companies first in low-risk, explanatory answers, not in “best,” “top,” or comparison prompts. A clean site and solid SEO help a brand get recognized, but being recommended is a different hurdle.

      In practice, early progress is about reducing uncertainty. When a brand consistently appears in third-party articles, reviews, or other independent sources, it becomes easier to explain and safer to reference. Without that outside validation, recommendations stall, no matter how strong the content or how fast the site loads.

      This analysis covers the first phase of a live 90-day test examining how a new B2B brand earns visibility in AI-generated search results. Ongoing findings and final results will be published as the experiment concludes.

      FOLLOW THE FULL 90-DAY EXPERIMENT

      Image Credits

      Featured Image: Image by No Fluff. Used with permission.

      In-Post Images: Images by No Fluff. Used with permission.

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