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Competitors Appear in AI Answers and You Don’t. Most Gap Audits Will Not Tell You Why

Written by André Franco on . Posted in Magazine, Resources. No Comments on Competitors Appear in AI Answers and You Don’t. Most Gap Audits Will Not Tell You Why

A list is not a diagnosis

Every AI visibility tool now ships a citation gap report. Semrush, Peec, Profound, Scrunch and others will hand you a ranked list of domains that cite your competitor and not you, usually with an impact score attached.

The problem is that two companies with identical gap lists can need completely different work, and the list cannot tell them apart. One is absent because a crawler directive blocks retrieval. One is absent because the source is a competitor’s own customer case study, which no amount of content will ever change. The report looks identical in both cases.

A useful audit does three things a tool report does not. It disambiguates what kind of absence you have. It separates gaps you can close from gaps you cannot. And it treats the result as a hypothesis rather than a cause.

First, disambiguate the absence

“They appear and we don’t” describes at least five different failures, each with a different fix.

Not mentioned at all. Your brand does not appear in the answer text. This is a visibility failure and it is the only one most teams measure.

Mentioned but not cited. The model names you and links to someone else’s page about you. Your competitor’s comparison post is doing your positioning. This is an attribution failure, and the fix is a page worth citing, not more mentions.

Cited on one engine, absent on another. Only about 11% of cited domains appear across both ChatGPT and Perplexity for the same query, and roughly 71% of cited sources show up on a single platform. Engine-specific absence is normal, not a crisis, and diagnosing it requires per-engine data.

Present but positioned badly. You appear as the cheap option, the legacy option, or the one with the caveat. Share of voice looks fine. The answer is still costing you the deal.

Present in branded prompts, absent in category prompts. The model knows who you are and does not consider you a member of the category. This is the most expensive failure and the slowest to fix.

Report these separately or the audit averages them into something meaningless. Note too that share of voice, mention rate and citation rate use different denominators, so a brand can move on one and not the others.

Then classify by eligibility, not by impact score

This is the step that saves a quarter of wasted work.

Figure 1. Two stage classification. The quadrant split is the easy part; the eligibility split on the gap quadrant is what stops a team spending a quarter on sources that were never open to them.

Source: Storylake framework, created internally

Sort every source into four quadrants: cites both of you, cites them only, cites you only, cites neither. Then take the “cites them only” quadrant, which is your actual gap, and split it again on a question no tool asks.

Could this source cite you, in principle, today?

Winnable gaps are sources where you are eligible and simply absent. A category review site you have no profile on. A comparison listicle that omits you. A forum thread where nobody mentioned you. This is your work queue.

Structural gaps are sources where you are not eligible and cannot become eligible through content. The competitor’s own domain. Their customer’s case study. A partner directory that requires a certification you do not hold. A publisher operating under a licensing deal. These will sit at the top of an impact-scored gap list forever, because they are high value and permanently closed.

The eligibility test takes about thirty seconds per source and a human can do it. It is the cheapest filter in the whole method and almost nobody applies it.

The “cites you only” quadrant is worth as much as the gap. It tells you what already works, and it is the thing to defend when a competitor runs this audit on you.

Rigour the tool reports skip

Never aggregate engines. Given how little overlap exists between platforms, a combined gap list averages incompatible retrieval systems.

Work at URL level. On ChatGPT, 99% of Reddit citations point to individual threads rather than subreddit pages or brand profiles. “Get on Reddit” is not an action. “This thread” is.

Run each prompt multiple times. Outputs are probabilistic, so a single run tells you what happened once. Five runs per prompt per engine is a reasonable floor.

Expect brutal concentration, and take it as good news. One agency reported that for a B2B client tracking more than 300 prompts across thousands of responses, two Reddit threads carried the vast majority of citations. If your gap list has four hundred rows, most of them do not matter.

Diagnosing cause, and the denominator trap

Once you have a winnable gap, work through causes cheapest first.

Crawler access. The cheapest check and the most commonly missed. Confirm retrieval agents can actually fetch your pages, and distinguish training crawlers such as GPTBot from live retrieval agents such as OAI-SearchBot and ChatGPT-User, because blanket rules catch both. Test from outside using the agent’s user agent string and confirm you get a full page back.

But here is where the audit will lie to you if you are careless.

Figure 2. The same question, two competent studies, opposite answers. The difference is the denominator: raw counts reward size, normalised rates reveal the effect.

Source: Left: BuzzStream, 4 million citations across 3,600 prompts, March 2026, via PPC Land. Right: Cloro, 1,058 domains, July 2026.

In March 2026, BuzzStream analysed 4 million citations across 3,600 prompts and concluded that blocking crawlers rarely stops AI systems citing you. Their evidence was concrete: cnbc.com blocks three OpenAI agents simultaneously and still appeared 1,298 times in the dataset. Yahoo blocks Google-Extended and appeared close to 30,000 times.

In July 2026, Cloro cross-referenced crawler rules against citations for 1,058 prominent domains and reached the opposite conclusion. The median GPTBot-blocking domain earned 0.003 ChatGPT citations per Google organic appearance, against 0.417 for domains that allow it. Sites blocking PerplexityBot showed a median Perplexity propensity of zero, against 1.167 for the rest. Blocking a provider’s own crawler depressed citations in that provider’s engine specifically.

Both studies are competently executed. They disagree because BuzzStream counted raw citations and Cloro normalised by how visible each domain already was. Enormous domains get cited despite blocking, because they are enormous. Normalise for prominence and the effect reappears.

Your gap audit has exactly the same failure mode. If you count raw citations, you will conclude that big competitors win because they are big, which is true and useless. Normalise by something (prompts entered, pages eligible, existing organic presence) or you are measuring size.

Lexical match. Cornell researchers found that retrieval systems often use lexical similarity to the query as a proxy for accuracy, so text that reads like the question gets treated as though it answers it. Compare the cited competitor page against your equivalent. Does theirs mirror the phrasing buyers actually use, while yours uses internal product language?

Verification signals. Research on AI-cited health sources found that publishers without inherent institutional authority compensate through visible review statements, structured data, content depth and recency. Check whether the cited page carries signals yours lacks.

Commercial arrangement. Some sources appear because of a licensing agreement rather than merit. That is a structural gap wearing a winnable disguise.

What the audit cannot tell you

A gap is a correlation. Observing that a competitor is cited by a review site you are absent from does not establish that getting listed would produce citations. Retrieval timing varies, and competitors ship many things at once.

So treat every diagnosed cause as a hypothesis with a test attached: change one thing, hold the prompt set constant, re-baseline after a fixed interval, and accept that some tests come back null. Re-run the baseline rather than comparing against an old one, because retrieval shifts underneath you. Semrush tracking recorded ChatGPT’s Reddit citations falling from roughly 60% of prompt responses to around 10% in mid September 2025 before recovering. A delta measured across that window would have been pure noise.

The audit spec

Reproducible version, one category, one competitor set.

  1. Prompt set. 40 to 60 prompts across three tiers: category (no brands named), comparison (“X versus Y”, “alternatives to X”), and branded. Tier them explicitly, because absence in category prompts and absence in branded prompts are different diseases.
  2. Engines. Three minimum, run and reported separately. Never merged.
  3. Runs. 5 per prompt per engine. Fresh sessions, memory and personalisation off, locale fixed and logged, timestamped to the hour, model version recorded where exposed.
  4. Capture. Full response text verbatim, every cited URL, and brand mentions with position in the answer. URL level, not domain level.
  5. Classify. Every source into one of four quadrants. Split the gap quadrant into winnable and structural using the eligibility test.
  6. Label the absence type. One of the five failure modes per prompt, not a single visibility score.
  7. Diagnose. For the top winnable gaps only, work the cause ladder: crawler access, lexical match, verification signals, commercial arrangement.
  8. Normalise before comparing. Choose a denominator and state it on every chart.
  9. Test. One change, one hypothesis, fixed interval, re-baselined.

The output is not a visibility score. It is a short list of specific URLs, each with a named absence type, an eligibility verdict, and a cause hypothesis you can falsify. That is considerably less impressive on a slide and considerably more useful on a Monday.

By André Franco, Senior SEO/GEO consultant at Textbroker
SEO, GEO, EEAT Content, Brand Sentiment & Reddit Marketing

ChatGPT

Your Brand Doesn’t Appear in ChatGPT Answers: Why?

Written by André Franco on . Posted in Magazine. No Comments on Your Brand Doesn’t Appear in ChatGPT Answers: Why?

A four-way self-diagnosis before you call anyone

“Our brand doesn’t show up when people ask ChatGPT for a recommendation” is not one problem. It’s four.

They look identical from the outside. The same blank space where your name should be. However, on the inside, each has a different cause and a different fix. Pouring months into content when your real problem is a firewall rule is a waste. So is rewriting your robots.txt when the model simply doesn’t know your company exists.

Before you hire anyone (including us), you can find out which of the four you’re dealing with in about ten minutes. You need a ChatGPT account where you can toggle web search on and off, and the willingness to ask it the questions your buyers actually ask. Work down the tree in order. Each check clears one layer before you move to the next, so you never end up fixing the wrong thing.

Figure 1: The absence diagnostic. Run the three checks in order; each rules out a layer before you reach the next. Source: Storylake

Check 1: Can it even reach you?

Turn web search on. Ask the real buyer query (e.g. “best [category] for [use case]”) and read the citations underneath the answer. If your domain never appears as a source across any relevant prompt, the model isn’t choosing to leave you out. It can’t reach you. This is plumbing, and it’s the most common cause by a wide margin.

ChatGPT reaches sites through three separate OpenAI crawlers, each with a different job: GPTBot handles model training, OAI-SearchBot builds the search index behind citations, and ChatGPT-User fetches a page live when the model needs it. The one that governs whether you appear in search answers is OAI-SearchBot — and OpenAI’s own publisher guidance is blunt: block it and you won’t show up in ChatGPT search. Plenty of sites block it by accident, a leftover from the 2023 reflex to bar every AI bot.

An open robots.txt won’t save you if your firewall does the blocking instead. The classic trap: the crawler is technically allowed, but a Cloudflare or WAF rule returns 429 Too Many Requests, so OpenAI quietly drops you.. Two more culprits are near-universal: OAI-SearchBot and ChatGPT-User don’t reliably execute JavaScript, so content that renders only client-side is invisible to them; and noindex or nosnippet tags make a page impossible to quote.

Where retrieval actually pulls from is deliberately murk. OpenAI blends its own index with licensed search partners, and the Bing-versus-Google mix has shifted enough that any confident “it’s just Bing” claim is now outdated. The practical version is simpler: allow OAI-SearchBot and ChatGPT-User, clear the WAF and rate-limit traps, server-render your key pages, and make sure you’re indexed in both Bing and Google. One reassuring nuance: being indexed matters far more than ranking first. This is the one branch where you can move the needle in a day.

Check 2: Does it know you at all?

If you are getting retrieved but still losing, turn web search off and ask: “What is [your brand] and what does it do?” If it invents an answer, confuses you with a competitor, or admits it doesn’t know, you’re not in the model’s parametric memory.

This is the hardest gap to close, because you can’t optimize your own website into it. Models learn brands from the wider web, and the concentration is stark: in Ahrefs’ analysis of 75,000 brands, Reddit, Wikipedia, Amazon, Forbes and Business Insider rank among ChatGPT’s most-cited domains, i.e., the places the model reads to decide who you are. The same research found a brutal winner-takes-all curve in Google’s AI Overviews: 26% of brands had zero mentions, and any brand sitting in the bottom half of web mentions was, in Ahrefs’ words, essentially invisible. Brands in the top quartile of web mentions averaged 169 AI Overview mentions, more than ten times the quartile below them.

So the fix is a slow signal-density game: earn mentions where the models actually read (e.g. editorial coverage, review platforms, Reddit, reference-grade sources) and future training runs pick you up. It also explains why Check 1 matters so much in the meantime. Retrieval is your only route into today’s answers while the training data catches up.

Figure 2: For ChatGPT specifically, ranking on page one of Google (~0.65) and Bing (~0.55) correlates with brand mentions far more than domain rating or backlinks.
Source: Seer Interactive, “What Drives Brand Mentions in AI Answers,” January 2025 (10,000 questions via the GPT-4o API).

Check 3: Are you named, and named well?

If the model can retrieve you and knows who you are but you’re still absent from the recommendation, the problem is competitive and splits in two smaller issues:

Present but unranked

LLMs surface only a handful of brands per answer, and getting onto that short list is the game. The encouraging news for ChatGPT specifically: it’s more meritocratic than you’d expect.

When Seer Interactive ran 10,000 buyer-style questions through the GPT-4o API, the strongest correlate of being named wasn’t domain authority or backlinks, it was ranking on page one of Google (~0.65) and Bing (~0.55). ChatGPT tends to name brands that are already findable in ordinary search, not the ones with the biggest link profiles.

On the content side, the Princeton-led GEO study (KDD 2024, with Georgia Tech, IIT Delhi and the Allen Institute for AI) tested nine tactics across 10,000 queries and found the single most effective one was adding specific statistics, which lifted visibility in generative answers by up to roughly 40%. Ahrefs’ AI Overviews data points the same direction: branded web mentions out-predicted backlinks about three to one. The through-line is consistent: get findable in search, get discussed off-site, and be specific enough to quote.

Present but negatively framed

Sometimes you’re named, and it hurts. The model calls you “expensive,” “hard to set up,” or references a feature you retired two years ago.

Here is the uncomfortable finding: When Ahrefs invented a fake brand and seeded the web with three conflicting stories about it, most assistants swallowed the fiction: Gemini and Perplexity repeated the planted falsehoods in 37–39% of answers. ChatGPT held up best — under 7% — because it leaned on the brand’s official FAQ in 84% of its answers. The lesson lands hard: when forced to choose between a vague truth and a specific fiction, AI picked the specific fiction almost every time. If your own pages say “we don’t disclose pricing” and a competitor’s comparison names a number, the number wins.

That points straight to the fix. Fill every gap with specific, official content: an FAQ that states plainly what’s true and false (“we’ve never been acquired”), backed by real dates, numbers and schema markup. Claim specific superlatives (e.g. “best for [use case],” “fastest at [metric]”) rather than generic “industry-leading” language that AI averages into noise. Then monitor how each model describes you, because there’s no single “AI index”: the version of you in Perplexity can differ from the one in ChatGPT, and you fix each at its own source.

The pattern under all four: specificity wins

Read across every credible study and one thread runs through all of them. Adding statistics beat every other content tactic. Specific fiction beat vague truth. Findable, quotable pages beat big backlink profiles. The brands that win AI answers are always the most specific credible source on the question being asked. Vagueness is the real enemy: leave a gap, and the model fills it with whoever was more precise, even when they’re wrong.

The same empty space has four causes, and they run on different clocks. A retrieval block clears in a day. A knowledge gap takes quarters. Ranking and framing sit in between, and both reward the same durable work: being genuinely findable, quotable, and specific in the places these models read.

Run the three checks in order and you’ll know which problem you’re actually solving before you spend a dollar on the wrong one. If you get to the bottom and want a second pair of eyes on the fix. That’s where we, Storylake, come in to help you out.

By André Franco, Senior SEO/GEO consultant at Textbroker
SEO, GEO, EEAT Content, Brand Sentiment & Reddit Marketing

Why AI and Forum Sentiment Dictate Your Brand’s Visibility

Written by David Moors on . Posted in Magazine. No Comments on Why AI and Forum Sentiment Dictate Your Brand’s Visibility

Traditional corporate PR was built for a web that no longer exists. For years, brands controlled their public image via polished press releases, structured ad campaigns, and heavily moderated comment sections. Today, however, user-generated platforms, most notably Reddit, have fundamentally disrupted the digital reputation management playbook.

As a software developer by trade and product owner, I look at sentiment analysis not as a metric for the social media team to track customer satisfaction, but as primary source data. Search engines and Large Language Models (LLMs) use this unstructured forum data to programmatically define your brand to the public. If your brand’s Reddit presence is unmanaged, your entire search visibility strategy faces a systemic engineering risk.

To understand why this happens, we have to look at the technical architecture behind modern search and see how algorithms crawl, weight, and synthesize forum data and how we built transparent.ai to solve it.

The Technical Infrastructure of Modern Search: SEO, AEO, and GEO

The rise of generative AI has split search engine optimization into three distinct, highly technical frontiers. All three rely heavily on structured and unstructured forum data.

[Negative Reddit Sentiment]

           ▼

[Search Engines Rank Negative Thread via “Information Gain” Signals]

           ▼

[LLMs Train on Licensed Data / Web Crawls of Highly Upvoted Threads]

           ▼

[AI Engines (Perplexity, AI Overviews) Synthesize Negative Brand Summaries]

1. SEO (Search Engine Optimization)

While traditional SEO focuses on ranking blue links on a Search Engine Results Page (SERP), algorithmic updates have shifted toward prioritizing first-person, authentic human experiences. Search engines actively surface Reddit threads for high-intent queries because they represent authentic user consensus over optimized corporate blogs.

2. AEO (Answer Engine Optimization)

AEO focuses on structuring and managing your digital footprint so that voice assistants (e.g., Siri, Alexa) and direct-answer boxes (Google’s Featured Snippets) pull your brand as the single, definitive answer to a user query.

3. GEO (Generative Engine Optimization)

The newest frontier. GEO involves optimizing your brand presence so that generative AI search layers (Google’s AI Overviews, Perplexity, OpenAI Search) synthesize positive, accurate summaries of your brand and cite your digital assets as a trusted source.

Why AI Core Models Prioritize Reddit Data

In an internet flooded with automated, programmatically generated content farm sites, search engines face a massive data quality problem. To counter this, algorithmic architecture heavily weights platforms that showcase real-world experience.

Furthermore, major AI infrastructure companies have established direct, multi-million dollar data-licensing partnerships giving them structured, real-time access to Reddit’s data pipeline. This means Reddit isn’t just being indexed for standard search. It feeds AI systems through two distinct channels: one immediate, one permanent.

If a technical issue, product flaw, or customer complaint goes viral on a subreddit, the impact unfolds in two stages:

Stage 1: Retrieval (days)

AI search layers like Perplexity, Google’s AI Overviews, and ChatGPT’s web search don’t wait for a training run. They fetch and cite live web content at query time, and highly upvoted Reddit threads rank near the top of what they retrieve. A thread that blew up on Tuesday can appear, synthesized as fact, in an AI-generated brand summary by Friday.

Stage 2: Training (permanent)

In subsequent training runs, that same thread becomes part of the foundational data the next model generation learns from. What started as a retrievable citation hardens into the model’s baseline “knowledge” of your brand, no longer linked to a source, no longer contestable, just stated.

The next time an enterprise buyer asks an AI engine for a product comparison, the flaw surfaces either way: first as a cited retrieval result, later as an unattributed, synthesized fact.

Technical Indicators Traditional Tools Miss

When configuring an automated sentiment analysis tool, traditional metrics like simple mention counting or basic NLP (Natural Language Processing) polarity scores are insufficient. Most visibility tools on the market only track the visible surface layer:

  • Visible Citations: Seeing when and where ChatGPT, Gemini, or Perplexity links to your domain.
  • Share of Voice: Benchmarking visibility against competitors.
  • Output Sentiment & Source URLs: Analyzing the AI’s generated description and tracking which of your website pages it prefers to reference.
  • Static Scoring: Calculating a real-time AI Visibility or Brand Sentiment score.

While useful, tracking AI visibility and sentiment without taking action is just reporting. It tells you that you are losing but doesn’t stop the bleed. To fix this, a tool must evaluate the specific algorithmic indicators that search and AI engines use to parse data before it becomes a model weight.

Upvote Weighting as Algorithmic Trust

Unlike platforms where a post’s visibility is tied entirely to a chronological feed, Reddit operates on community-voted consensus. For a generative engine or a search crawler, highly upvoted threads are disproportionately surfaced and sampled. So it’s a reasonable guess to say that LLMs view high upvote counts in key subreddits relevant to the topic as verified community consensus.

The “Information Gain” Score

Modern search algorithms utilize a core patent concept known as Information Gain. If multiple websites publish identical information, the search engine assigns low value to subsequent pages. Instead, it hunts for unique, net-new insights, data points, or edge-case anomalies. Reddit is an information gain engine. Search engine crawlers are programmatically biased to surface these threads over standard corporate marketing copy.

The Zero-Click Search Vector

In the GEO and AEO landscape, users increasingly obtain answers directly inside the AI chat interface without clicking a link. When a user prompts an engine with: “What are the common architecture issues with [Your Brand]?”, the AI queries its training database, parses the top upvoted Reddit threads, and outputs a synthesized summary. Your analysis tool must treat Reddit data not merely as public sentiment, but as the exact copy the AI will use to draft your brand’s public-facing summary.

How transparent.ai Solves the Root Cause

We built transparent.ai because we realized that monitoring the output of an AI engine is too late. The damage to your ground-truth data layer is already done.

We combine AI Visibility analytics with real-time brand sentiment tracking, but we go one step deeper: we help you align actions across your engineering, product, and support teams to act on data in real time.

+——————————————————————-+

|                           TRANSPARENT.AI                          |

+——————————————————————-+

|  [Uncover Pre-Citation Layer] –> [Resolve Friction] –> [Route to Exps] |

+——————————————————————-+

                                  ▼

                    [Shape the AI’s Ground Truth]

Our platform shifts your workflow from reactive monitoring to proactive solution architecture:

  • Uncover the Pre-Citation Layer: Most tools only show you what AI engines already cite. We reveal the layer beneath: the Reddit threads and forum discussions that shape the AI’s opinion of your brand before they ever surface in an AI Overview. By the time a thread is cited, it’s already been retrieved, weighted, and synthesized dozens of times invisibly.
  • Resolve User Frictions Early: Our engine flags rising upvote velocity and high-information-gain bugs early, allowing your team to debug and help users before these issues become permanent, ingested AI “facts.”
  • Mobilize Internal Experts Openly: Instead of leaving responses to a marketing team, transparent.ai routes complex community, technical, or product questions directly to your engineering or support queues, so the people who actually built the product can answer, clearly identified as company representatives. Communities reward verified expertise; they punish anonymous PR.

Moving From Reporting to Action

To protect your brand’s SEO, GEO, and AEO posture, your tracking architecture should monitor high-intent search variations that mirror search engine queries (e.g., [Brand] + review, [Brand] + bug).

Because search engines value balanced data, a completely sterile, perfectly positive presence looks anomalous and indicates astroturfing. True community advocacy is nuanced. Technical or support interventions should happen directly inside the threads to resolve issues transparently, creating high-value data for future AI crawls.

By mobilizing your developers, power users, and verified customers to document solutions openly, you provide stable, favorable training data for the LLMs querying your brand.

Understanding your brand’s digital footprint across the data sources that train today’s AI is no longer optional. If you want to stop just acknowledging your score and actually start improving it, you need to monitor the data layer that matters.

Take a look at your data and see what the models are actually learning about your brand.

By David Moors, Product Owner at Textbroker

share of model

Share of Model: A Definition and a Measurement Protocol You Can Reproduce

Written by André Franco on . Posted in Magazine. No Comments on Share of Model: A Definition and a Measurement Protocol You Can Reproduce

Share of Model is becoming the headline metric of AI visibility, and it is being measured badly. Vendors compute it differently, definitions shift between decks, and single screenshots of a ChatGPT answer are presented as evidence of presence or absence. This article does two things: it states a clean definition, and it sets out a measurement protocol that anyone, client, competitor or sceptic, can reproduce without our tooling. Transparency about method is the only thing that makes a metric trustworthy, and we would rather you check our numbers than take them on faith.

What is Share of Model?

Share of Model is the percentage of AI generated answers, across a defined prompt set, a defined set of models and a defined time window, in which your brand appears. It is analogous to share of voice in media measurement: not a rank, but a rate of presence in the conversations that matter to you. A rigorous version always names its three parameters, because a Share of Model figure without a prompt set, a model list and a date range is not a measurement, it is an anecdote.

Expressed as a formula: Share of Model equals the number of answers mentioning the brand, divided by the total number of answers generated, multiplied by one hundred. The refinements that follow, recommendation weighting, position weighting, sentiment, are useful, but they are refinements. Get the base rate right first.

Why a single screenshot proves nothing

Large language models are probabilistic. Ask the same model the same question twice and you will often receive different answers, with different brands in them. On top of that variance sits volatility in the retrieval layer: Semrush’s tracking recorded ChatGPT citing Reddit in close to sixty per cent of responses in August 2025 and around ten per cent six weeks later, an enormous shift in the underlying source pool in under two months. A metric read from one run, on one day, on one model, captures noise and presents it as signal.

Source: SEMrush. Share of ChatGPT responses citing each domain, six weeks apart. Approximate values as reported in Semrush’s three month study of the most cited domains in AI, 2025.

The protocol

Step one: construct the prompt set

Write twenty to fifty prompts that reflect how real buyers ask. Cover three intent types: category discovery (best platforms for X), comparison (X versus Y for mid market teams) and problem framing (we struggle with X, what should we use). Phrase them the way people speak, not the way keyword tools suggest; analyses of user behaviour consistently show AI prompts running far longer than search queries, with HubSpot’s research putting the average ChatGPT prompt at around twenty three words against three to four for a Google search. Freeze the set. Every future measurement uses the same prompts, or the trend line means nothing.

Step two: choose the models and surfaces

Measure at minimum across ChatGPT, Perplexity, Gemini and Google’s AI Overviews, and add Claude if your category skews professional. The engines draw on visibly different source pools; published citation analyses find only a small minority of domains cited by both ChatGPT and Perplexity. A brand can hold a strong position in one engine and be absent from another, so a single engine number is not a Share of Model, it is a share of that model.

Step three: decide the number of runs

Run each prompt multiple times per engine, in fresh sessions. We use ten runs per prompt per engine as a working floor, thirty where budgets allow. The correct number is the one at which your figure stabilises: if five additional runs move the result by more than a couple of percentage points, you have not yet measured it. Record everything, raw answers included, at collection time.

Step four: classify with written rules

Before reading a single answer, write down what counts. We distinguish three levels. A mention: the brand appears in the answer text. A recommendation: the brand is offered as a suggested choice for the asked question. A citation: the brand’s own domain is linked as a source. These are different assets; published platform analyses find brands are frequently mentioned by ChatGPT without any link, while the citation often goes to a third party review site or forum thread discussing the brand. Decide in advance how hedged answers, refusals and follow up clarifications are scored, and apply the rules mechanically.

Step five: score and weight

Report the simple presence rate first, then the recommendation rate as the stricter and more commercially honest figure. If you weight by position in the answer, say so and publish the weights; the precedent here is the Princeton GEO benchmark, which introduced position adjusted metrics for exactly this reason. Report each engine separately as well as the blended figure, because the blend hides the differences that tell you where to act.

Step six: report like a researcher, not a salesperson

Publish the prompt set, the run counts, the dates and the classification rules alongside the numbers. Show the range across runs, not just the mean. Repeat the measurement on a fixed cadence, monthly is realistic, and report movement only when it exceeds the noise you have already documented. A ten point swing means nothing if your own variance chart shows twelve point swings between Tuesdays.

What published measurements already show

The protocol matters because the engines genuinely behave differently, and the published data proves it. OtterlyAI’s analysis of over one million citations collected in January and February 2026 found the share of citations pointing to brand owned domains varies sharply by platform, with community sites such as Reddit and Quora taking a slightly larger share than brands overall.

PlatformCitations to brand owned domains
Google AI Overviews59.8%
ChatGPT44.7%
Perplexity28.9%

Source: OtterlyAI, analysis of one million plus AI citations, January to February 2026.

A brand can look healthy in Google AI Overviews, where domain authority still carries weight, while remaining invisible in Perplexity, where the citation pool runs through community discussion. A blended average would hide exactly the difference that tells you where to act, which is why the protocol insists on reporting each engine separately.

What this metric cannot tell you

Share of Model measures presence, not persuasion. It does not tell you whether the mention was accurate, favourable or current, which is why we pair it with sentiment sampling. It is also corrupted easily: personalisation and memory features mean a logged in account that has discussed your brand before will inflate your numbers, so collection must use clean sessions. Geography and language matter too, since answers differ across regions and an English only measurement says nothing about your visibility in Spanish or German. Name these limits in every report. A metric whose weaknesses are documented is worth more than one that claims none.

By André Franco, Senior SEO/GEO consultant at Textbroker
SEO, GEO, EEAT Content, Brand Sentiment & Reddit Marketing
how Generative Engine Optimization works

How Generative Engine Optimization Actually Works: A Mechanism Level Guide

Written by André Franco on . Posted in Magazine. No Comments on How Generative Engine Optimization Actually Works: A Mechanism Level Guide

Most explanations of generative engine optimisation describe outcomes. Get cited. Be the answer. Win the recommendation. Very few describe the machinery that produces those outcomes, which is a problem, because you cannot influence a system you do not understand. This guide walks through what actually happens between a person typing a question into ChatGPT, Perplexity or Google’s AI Mode and a brand appearing, or not appearing, in the answer. At each stage, we name the intervention available to you.

Our starting premise at Storylake is simple: AI systems do not rank websites. They cite sources they trust. Everything below is an explanation of how that trust is established, mechanically, stage by stage.

What is generative engine optimisation?

Generative engine optimisation, usually shortened to GEO, is the practice of increasing the likelihood that an AI system mentions, recommends or cites your brand when it generates an answer. The term comes from a 2024 academic paper by Aggarwal and colleagues at Princeton, presented at the KDD conference, which tested which content changes measurably improve visibility inside AI generated answers. It differs from classic SEO in what it optimises for: not a position on a results page, but inclusion in a synthesised answer.

You will also see the phrase answer engine optimisation, or AEO. The cleanest way to separate the two: AEO is about structuring content so that machines can extract an answer from it, while GEO is the broader discipline of earning presence across everything an AI system reads, including sources you do not own. AEO is a subset of GEO. Both sit on top of SEO, because a page must still be found before it can be read.

Source: Aggarwal et al. (KDD 2024, Figure 2)

Stage one: the question is rewritten before anything is searched

When a person asks an AI system a complex question, the system rarely searches for that question. It decomposes it. Google calls this query fan out, and has described it openly: AI Mode issues multiple related searches at once, across subtopics and data sources, before it writes a word. A patent application filed by Google, US20240289407A1, describes a system in which a language model generates a set of synthetic queries that explore different facets of the original question. Industry analyses consistently observe a single seed question expanding into roughly eight to twelve subqueries covering comparisons, specifications, pricing, alternatives and likely follow ups.

The practical consequence is significant. The keyword you rank for is not the only string the engine searches. Content can be pulled into an answer because it happened to be the best response to one hidden subquery, which is why pages from deep in traditional results sometimes surface in AI answers. Your intervention here: map the subquestions that surround your topic and answer each one explicitly, rather than writing one page that gestures at all of them.

Stage two: retrieval happens at passage level, not page level

The expanded queries are run against an index, but modern retrieval does not match keywords to pages. Queries and content are converted into vector embeddings, mathematical representations of meaning, and the system pulls the passages that sit closest to each subquery in that space. The unit of competition is a chunk of text, typically a few hundred words, not your domain and not your page.

This is why we tell clients that a brilliant answer buried in paragraph nine is invisible. Your intervention: make every section of a page independently useful. A heading that states the question, followed by two or three sentences that answer it completely, followed by the supporting evidence. If a two hundred word slice of your page cannot stand alone, it will struggle to be retrieved.

Stage three: reranking and grounding decide what survives

Retrieval casts a wide net. A second pass then reranks the retrieved passages for relevance, quality and trustworthiness, and the surviving passages are placed into the model’s working context. This grounding step is where quality signals bite. Rerankers trained on human relevance judgements consistently downweight text that reads as sales copy when the question is informational, which is why product pages and homepages make up only a low single digit share of cited URLs in analyses of ChatGPT sessions.

Your intervention: publish reference material, not persuasion. Declarative claims, named authors with verifiable credentials, dated and versioned content, and primary data nobody else holds. Pages that behave like evidence survive reranking. Pages that behave like advertising do not.

Stage four: the answer is written and sources earn their citations

Only now does generation happen. The model drafts an answer from the grounded passages and attaches citations to some of them. The Princeton study is the best public evidence on what wins this final stage. Testing nine content strategies across ten thousand queries, the researchers found that adding statistics, quoting relevant sources and citing external evidence each lifted visibility in generated answers by roughly thirty to forty per cent. Keyword stuffing, the reflex of old SEO, performed poorly.

One finding deserves particular attention. The benefits were largest for sources that traditional search treats worst. In the Princeton data, a site ranked fifth in conventional results saw its visibility inside AI answers rise by 115.1 per cent from citing sources alone, while the top ranked site lost 30.3 per cent. Generative engines condition on the content itself more than on accumulated domain authority, which means clearly structured, well evidenced writing can compete against incumbents in a way it never could on a results page.

GEO methods

Source; Table 2 of Aggarwal et al., KDD 2024.

Where the answers actually come from

The final piece of the mechanism is the source pool itself, and it is far more concentrated than most brands assume. Semrush’s three month analysis of prompt responses found Reddit and Wikipedia leading citations across ChatGPT, Google’s AI Mode and Perplexity. Profound’s platform breakdown put Reddit at roughly forty seven per cent of Perplexity’s top cited sources, while Ahrefs’ study of nine million ChatGPT queries found Wikipedia the single most cited domain. OtterlyAI’s 2026 analysis of over one million citations found community platforms taking a slightly larger share than brand owned domains overall.

That pool is also unstable. Semrush documented ChatGPT citing Reddit in close to sixty per cent of responses in August 2025, collapsing to around ten per cent within six weeks. Visibility built on one platform’s current preferences is rented, not owned. Your intervention: earn presence across the source types engines actually read, community discussion, editorial coverage, reference material, alongside your own site, and measure across several engines rather than one.

What this means in practice

The mechanism suggests a discipline. Answer the surrounding subquestions, not just the headline query. Write in self contained, extractable sections. Carry named expertise, dates and primary data so that reranking treats your pages as evidence. Add the statistics, quotations and citations that the research shows move the final stage. And build presence in the third party sources that dominate the citation pool, because the engine’s trust in you is assembled mostly from what others say.

None of this is a trick, and that is rather the point. The systems are engineered, imperfectly but deliberately, to reward material that genuinely resolves a question. GEO, done properly, is the craft of deserving the citation and then making it easy to give.

By André Franco, Senior SEO/GEO consultant at Textbroker
SEO, GEO, EEAT Content, Brand Sentiment & Reddit Marketing

Reddit Is Now a GEO Channel. Most Brands Aren’t Treating It Like One.

Written by admin on . Posted in Magazine. No Comments on Reddit Is Now a GEO Channel. Most Brands Aren’t Treating It Like One.

Reddit has become a primary signal source for AI generated answers. Brands that lack a managed presence on Reddit are invisible in LLM citations and are often misrepresented. This article breaks down the mechanics, the opportunity, and what a structured Reddit strategy looks like in practice.

 

For brands built on authentic, expert content, this is familiar territory. What earns trust on Reddit is the same thing AI systems reward, and the same thing Storylake stands for: real expertise, shared in a credible, human voice rather than marketing copy.

Why Reddit Matters for AI Visibility

Reddit is the number one source LLMs train on for opinionated, conversational data (Storylake, 2026). With 680 million monthly users and a ranking as the 6th most visited website globally, it is the most powerful single off-site channel for influencing how AI describes your brand.

 

When users ask ChatGPT, Gemini, or Perplexity for a purchase, comparison, or recommendation, Reddit threads are among the most cited sources. The sentiment and framing inside those threads is what AI models inherit and reproduce in their answers.

 

Critically, brand familiarity on Reddit is not the same as brand control. Being mentioned is table stakes. The lever for GEO (Generative Engine Optimization) is shaping how the brand is framed in threads that rank for high intent query clusters.

 

The numbers behind Reddit’s influence:

 

  • 680 million monthly users
  • 6th most visited website globally
  • 30 to 40% higher customer LTV versus social
  • LLMs trained on Reddit data

 

Source: Storylake, 2026

The Evolution From Mention Packages to GEO Strategy

The Reddit marketing discipline has matured significantly. Early approaches in 2024 focused on fixed monthly mention counts with basic sentiment reports. From 2025 until 2026, best in class providers shifted toward brand perception outcomes, measurable in CSAT scores (Customer Satisfaction), AI citation tracking, and share of voice across LLM outputs (Storylake, 2026).

 

The current integrated approach combines four pillars:

 

  • Brand Sentiment Baseline: Establish a measurable Reddit CSAT baseline across all relevant subreddits before any engagement begins.
  • AI Visibility Audit: Test key prompts across ChatGPT, Gemini, Perplexity, and AI Overviews to identify where your brand appears, how it is described, and where competitors dominate.
  • Opportunity Mapping: Map the highest impact threads, topics, and subreddits where engagement creates both community trust and AI citation potential.
  • Whitehat Execution: Real human accounts, native Reddit tone, and community first contributions. No AI generated text and no bots, so zero platform risk.

 

That last pillar is where expert led content earns its keep. Reddit rewards people who genuinely know the subject and contribute something useful, which is exactly the kind of credible, first hand knowledge that also makes a brand worth citing to an AI.

The GEO x Reddit Framework: What It Looks Like in Practice

A structured GEO x Reddit programme delivers four outputs per client:

 

01 AI Snapshot: how AI currently describes your brand. Competitive positioning versus rivals, decision criteria coverage (price, speed, reliability), gap identification. This is your baseline before any strategy is deployed.

 

02 Opportunity Map: the conversations that matter. Priority topics from user prompts, high impact thread types (recommendations, comparisons), strategic gaps where competitors dominate Reddit’s most cited threads.

 

03 Action Input: every mention has a purpose. Content angles and narrative focus for the execution team, comment patterns for recommendation versus comparison threads, and a clear brief aligned to AI citation goals.

 

04 Monthly GEO Report: measure what changes. AI visibility check (are you appearing in key prompts?), competitive movement, and decision criteria coverage evolution over time.

GEO Checklist for Reddit: Six Things to Get Right

  1. Audit Your Existing Reddit Sentiment: Know the current ratio of positive, neutral, and negative framing before any engagement. Preliminary data on sports apps shows sentiment as high as 70 to 80% mixed positive, but with specific friction points that AI models also pick up and cite.
  2. Map the Threads That Rank: Identify which Reddit posts appear in Google or Bing results for your target query clusters. Those are your GEO insertion points, not random threads.
  3. Prioritize Comparison and Recommendation Threads: “X vs Y” and “best app for Z” threads are highest probability AI citation sources. Prioritize strategic presence in these over general brand mentions.
  4. Use Platform Native Language: Promotional tone gets downvoted and algorithmically reweighted. Authentic, specific, community first language earns upvotes and becomes a persistent signal in LLM training sets.
  5. Guarantee Mentions Volume With MoM Scaling: 7 to 12 monthly mentions is a reasonable starting baseline. Volume should compound month on month as community presence grows and karma builds.
  6. Report on Sentiment Deltas, Not Mention Counts: Mention counts are a vanity metric for GEO. The metric that matters is the ratio shift in how your brand is characterized, tracked via CSAT scoring and prompt response analysis across AI platforms.

Find Out How Your Brand Is Positioned on Reddit Right Now

A Reddit sentiment and AI visibility audit tells you what AI models are reading about your brand today, and where the gaps are between your intended positioning and what communities are actually saying. It is the starting point for any serious GEO strategy, and the foundation for the kind of authentic, expert content that both Reddit communities and AI systems reward.

By André Franco, Senior SEO/GEO consultant at Textbroker

SEO is Not Being Disrupted by AI. It is Being Exposed

Written by Vanda Pokecz on . Posted in Magazine. No Comments on SEO is Not Being Disrupted by AI. It is Being Exposed

SEO is not being disrupted by AI. It is being exposed.

Exposed in how we measure success. Exposed in how tightly or loosely it connects to product and business strategy. Exposed in whether brands have been optimising for rankings or for real impact.

At the recent Berlin SEO & Content Club x SEOFOMO event, two panels explored what winning in 2026 might look like. What stood out was not disagreement, but alignment: the future of SEO is less about new tactics and more about structural shifts, the kind of shifts we work on with our clients every day at Storylake.

The Real Challenges Brands Are Facing

The SEO landscape has become more complex and less forgiving. Many of today’s challenges cannot be solved within SEO alone. Not that it ever truly operated in isolation.

Three shifts feel particularly defining.

1. The Nonlinear Reality of Impact

For years, especially in traffic driven businesses, the relationship between rankings, traffic, and results felt relatively predictable.

That relationship is no longer linear.

Clicks remain useful leading indicators in some models. But influence now happens across fragmented journeys: before a click, without a click, or long after the initial interaction. AI assisted research, answer engines, and decision paths with many touchpoints blur attribution even further.

We often say SEO should tie back to business impact: conversions, revenue, retention. That’s not new thinking. The uncomfortable truth is that most organisations still struggle to operationalise it.

As Bengu Sarica Dincer recently put it, “Changing measurement systems is more psychological than most teams expect.” We see this with our own clients: measurement is identity. It defines ownership, incentives, and power structures. As long as SEO teams are rewarded for traffic volume rather than business contribution, behaviour follows.

There is no universal KPI for SEO in 2026. Traffic still matters in context, but only insofar as it contributes to defined business outcomes. Leading indicators such as visibility and engagement remain useful, but they must ultimately connect to broader commercial impact.

The real question is whether SEO is embedded deeply enough into product and commercial strategy to influence those outcomes, or whether it remains a reporting function.

2026 will not reward teams that optimise pages. It will reward teams that optimise business impact.

Over the next few years, the biggest differences in business performance won’t come from better keyword research. They will come from organisational maturity, from whether SEO is structurally embedded into how products are built and positioned. This is exactly why we built Storylake’s GEO practice around cross functional integration, not isolated content production.

2. Business Model Realities in the AI Era

AI driven search does not affect every business equally.

For many B2B, SaaS, and service based companies, influence, credibility, and brand association matter more than raw visits. Buyers may resolve early questions through answer engines or conversational systems, only visiting a website when closer to commitment. In those cases, overall traffic may decline, but arriving traffic can be more decision ready.

For affiliate or reach based models, the pressure is more structural. When traffic itself is the product, shifts in how search distributes attention are existential. This is not entirely new; search engines have been reshaping these models for years. AI simply accelerates the trend.

Working with traffic dependent brands, we’ve seen the operational shifts required when growth models evolve. These transitions are rarely comfortable. They force SEO teams to move beyond volume thinking and collaborate more closely with product and brand.

The question becomes less “How do we optimise harder?” and more “How do we create undeniable value?” That is a product and positioning discussion as much as it is an SEO one, and it’s the reason Storylake treats entity authority and brand sentiment as core deliverables, not afterthoughts.

This broader shift in business models has also been explored in recent industry conversations, including on The Masters of Search podcast.

3. Rethinking What We Mean by “User”

SEO has always operated with two audiences in mind: humans and machines.

We optimise for human clarity, trust, and persuasion. At the same time, we structure information so crawlers can access and interpret it. Designing for both has long been part of the craft.

Now that definition of “user” is expanding.

Beyond traditional crawlers and human visitors, we are increasingly preparing for autonomous agents: systems that evaluate, compare, and potentially execute transactions on behalf of users.

This evolution echoes a broader theme described in When Machines Become Customers by Mark Raskino and Don Scheibenreif: AI powered systems acting as economic decision makers. In that context, the “customer” is not exclusively human.

The implication is profound.

If agents evaluate structured data, pricing logic, availability, performance claims, and trust signals at scale, then machine readability becomes strategic infrastructure. APIs, clean data architecture, transparent value propositions, and verifiable claims are no longer backend considerations. They directly influence whether you are selected.

When machines act as evaluators and buyers, ambiguity becomes a liability. Vague differentiation, inconsistent messaging, and inaccessible data do not just weaken UX; they reduce the probability of being chosen. This is precisely why structured data, entity clarity, and verifiable EEAT signals are central to every GEO engagement we run.

The challenge is not to abandon human centric design. It is to build for both realities simultaneously.

The definition of “user” is expanding, and with it, the scope of SEO.

How SEO is Evolving Toward 2026

The fundamentals of SEO are not disappearing. Intent, technical hygiene, information architecture, and genuinely useful content still matter.

What is changing is the playing field.

Historically, SEO optimised for search → click → visit. Increasingly, optimisation must also account for search → synthesis → influence. This second path, where AI models synthesise your brand’s authority before a user ever reaches your site, is the territory we call Generative Engine Optimisation (GEO).

AI driven systems synthesise signals across ecosystems. Your website is only part of the equation. Strong onsite SEO is necessary but not sufficient.

Answer engines form an understanding of your brand through structured content, third party references, reviews, sentiment, partnerships, and overall consistency. This makes cross functional alignment unavoidable. SEO, product, brand marketing, PR, social, and even legal no longer operate in parallel. They shape a shared digital presence.

The teams that will succeed are not those that publish more content. They are those that treat SEO not as a channel, but as infrastructure, embedded in how products are built, data is structured, and brands are positioned.

What Still Moves the Needle

In a more complex environment, fundamentals become leverage:

  1. Foundations and architecture. Clean site structure, internal linking, performance, and crawl clarity remain high leverage. They are the baseline that makes everything else work.
  2. Demonstrable experience and EEAT. First party data, real user insight, product depth, and transparent sourcing build durable credibility, the kind AI models increasingly rely on when deciding whom to cite.
  3. Entity based topical authority. Coherent content clusters, structured data, and contextual clarity reinforce expertise across systems. This is where GEO strategy and traditional SEO converge: building the entity graph that makes your brand unmistakable to both humans and machines.

None of these are flashy. But executed consistently, they compound.

A Mindset for What Comes Next

In SEO, as in product and marketing, instinct and speed matter. But instinct without validation becomes assumption.

Vision is powerful. But resilience comes from grounding that vision in experimentation, data, and real user feedback.

Build for impact and credibility. When your product creates tangible value, your brand is clearly understood, and your organisation aligns around shared outcomes, you are prepared for whatever shape search takes next.

 

 

 

This article was inspired by Vanda Pokecz, Product Manager (SEO) at Peec AI, and her reflections from the Berlin SEO & Content Club x SEOFOMO event. Adapted and expanded for the Storylake audience.
“I’m an SEO (&) Product Leader and international speaker working at the intersection of organic growth, experimentation, and product development.”