We sell Generative Engine Optimization. We tell founders that the buying question has moved from Google’s blue links to ChatGPT, Perplexity and Google AI Mode, and that if an answer engine doesn’t name you, you’re not in the shortlist. Then we built a free tool to measure exactly that, pointed it at upgrowth.in, and scored 0 out of 100.
Here’s the run, exactly as our own system recorded it on 9 September 2026 at 12:54 IST. 1 query: “best B2B SaaS growth marketing agencies in India”. 8 engines: ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, Google AI Overviews, Google organic and Bing. Result: 8 misses out of 8. Score 0.
We could have quietly fixed it and published the case study in December with a nice number at the end. That’s the normal agency move. But a growth consultancy that hides its own diagnostic is selling a diagnosis it won’t take. So we’re publishing the bad number first, then the work, then whatever the number does next. You can run the same check on your own domain with the AI Answer Gap tool and see your version of this page in about 3 minutes.
What follows is what the data actually said, what surprised us, and what we’re changing. Some of it contradicts the tidy story most GEO content tells.
What the run measured, and what came back
The tool sends one buyer question to each selected engine, captures the raw answer and the sources that answer cited, then scores whether the brand was named and how prominently. A brand named first scores 100. Top 3 scores 75. Named lower down scores 45. Not named scores 0. The run score is the average across every question and engine. The full scoring method is documented here, including what it deliberately excludes.
For the 9 September run, every cell came back 0. Our own system logged the miss against all 8 engines individually: ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, Google AI Overviews, Google organic and Bing all answered the question without naming upGrowth once.
That’s the part worth sitting with if you run marketing at a company that thinks it has decent SEO. We rank for plenty of terms. Our domain has been publishing since 2017. None of that put us in the answer. Organic rank and answer-engine presence are different inventories, and we had just proved it on ourselves.
The score only moved when the question contained our own name
A second run the same afternoon put 5 questions through 7 engines and scored 21 out of 100. That looks like improvement. It isn’t. The per-question data shows where those points came from.
ChatGPT scored 20 on that run. Our system recorded ChatGPT missing on the category questions and hitting on exactly 1: “upGrowth vs Gozoop which agency is better for growth marketing”. Gemini, Claude, Google AI Mode and Google organic all landed on the same 20. Same pattern.
So the engines know upGrowth exists. Ask them about us by name and they’ll talk about us. Ask them who a B2B SaaS founder in India should hire, and we’re not in the room. That distinction is the whole game, because no buyer types your name into ChatGPT before they’ve heard of you. Branded visibility is a vanity metric in answer engines. It measures the traffic you already had.
If your own AI visibility check comes back looking healthy, check whether your brand name is sitting inside the questions. Most tools default to brand-name prompts, which is why most tools tell you you’re doing fine.
Perplexity named us on questions where 6 other engines didn’t
On the 5-question run, Perplexity scored 46 while every other engine scored 20 or 0. Our system logged Perplexity as the only engine that named upGrowth for “best growth marketing agencies in India for B2B SaaS” and for “how to choose between SEO agency and growth marketing agency”. ChatGPT, Gemini, Claude, Google AI Mode, Google AI Overviews and Google organic all missed both.
Same questions. Same country setting. Same brand. 1 engine says we belong on the list, 6 say we don’t.
That kills the idea of a single AI visibility number being enough to act on. If you’re only checking ChatGPT because it’s the one you personally use, you’re seeing 1 of 7 surfaces, and possibly the least flattering one. If your agency reports “AI visibility” as a single figure with no engine breakdown, ask which engines are in it.
Why the divergence happens is a longer answer, and it comes down to each engine drawing on a different set of source documents at the moment you ask. We pulled the source data apart in a separate piece on what the engines were citing.
The same engine changed its answer about us in 56 minutes
This is the finding we didn’t expect and the one that most changes how we’d advise a client to use any of this.
The query “best growth marketing agencies in India for B2B SaaS” ran twice on 9 September 2026, from the same account, with the same brand and market settings. The first run finished at 12:35 IST and our system recorded Perplexity not naming upGrowth. The second finished at 13:31 IST and recorded Perplexity naming us. 56 minutes apart, opposite results.
We haven’t established the cause and won’t pretend otherwise. Answer engines retrieve live documents at query time, so the source set can shift between 2 identical prompts. What matters commercially is the consequence: a single AI visibility check is a snapshot, not a measurement. Treat one run the way you’d treat one day of ad data.
It also means you should be sceptical of any AI visibility score, ours included, presented as a precise figure. The honest use of a one-shot diagnostic is directional. It tells you whether you’re absent, occasionally present, or consistently recommended. Anything finer than that needs repeated runs over weeks.
Who got named instead of us
Across the 2 category runs on 9 September 2026, our system logged the brands the engines named in our place. On the 12:54 IST run, GrowthSpree and The Smarketers each came up 5 times, TripleDart 3 times and growth.cx 2 times. On the earlier 12:35 run it was TripleDart and GrowthSpree at 4 each, with growth.cx at 3.
None of that is a complaint. Those are real agencies doing real work, and on the question of who AI recommends for B2B SaaS growth marketing in India, they’re winning and we’re not. The useful question isn’t whether they deserve it. It’s what the engines were reading when they decided.
We logged the cited sources too, and the pattern there was blunt enough that it became its own article. The single most-cited source domain across these runs belonged to one of the agencies being recommended. The engines were reading a competitor’s own roundup and repeating it.
The 3 fixes we picked out of the 7 the tool suggested
The tool generated 7 recommended fixes for this run. We’re doing 3. The other 4 were variations on “publish a comparison page”, which is what happens when you ask a model to produce 7 items when the honest answer is 3.
First, a B2B SaaS case study page with actual numbers on it. Not logos. Client, starting position, what we changed, what moved, over what period. The engines cite pages that contain specific figures because specific figures are extractable. Our current case studies read like brochures.
Second, a comparison page that names the agencies the engines are already naming and says plainly where we win and where we don’t. Most agencies won’t publish an honest comparison because it means conceding something. That reluctance is exactly why the category has no trustworthy comparison content, and why the engines fall back on competitor-written lists.
Third, getting upGrowth into the third-party roundups the engines actually cited. Not link buying. Being genuinely reviewable: a real profile, real client references, and a pitch to the specific publications that showed up in our source data.
We’ll publish the score again as these ship. If it doesn’t move, we’ll publish that too, because a GEO practice that only reports its wins is a marketing department.
Start with the self-interested reason, since it’s the obvious one. A free diagnostic tool is only credible if the people selling it have run it on themselves and published what came back. Any agency can show you a screenshot of a client scoring well.
The reason that actually matters is that a 0 is more useful to you than a 70 would be. It proves the thing we’ve been telling clients: traffic, rankings and brand recall don’t carry into answer engines automatically. We’re a 9-year-old agency with a content library and a decade of domain history, and we’re invisible on our own category question. Your Series B SaaS company with 40 blog posts is not in better shape, and neither is your competitor.
There’s also a harder test we’ve now committed to. The fixes below are the same fixes we’d sell you, so we should be able to show them working in a category full of agencies who all know SEO. If they only work where the competition is asleep, they aren’t worth buying.
Run the same check on your own domain and you’ll get the engine-by-engine grid, the brands named instead of you, and the source domains those engines trusted. It’s free, it takes about 3 minutes, and the only thing we ask for is a business email so we know who we’re talking to.
6 Common Questions About Auditing Your AI Visibility
Q: What does a score of 0 out of 100 actually mean?
A: It means no engine named the brand for any question in that run. The AI Answer Gap score awards 100 when a brand is named first in an answer, 75 for a top 3 mention, 45 for a mention lower down and 0 for no mention. A run score is the average across every question and engine, so 0 means every single cell was a miss.
Q: Doesn’t ranking well on Google mean you’ll show up in AI answers?
A: Not reliably. In our 9 September 2026 run, upGrowth scored 0 on Google organic and on all 7 other engines for a category question, despite ranking for many related terms. Answer engines assemble responses from documents they retrieve and trust at query time, which is a different selection process from the ranked list.
Q: Why did different AI engines give different answers about the same brand?
A: Each engine retrieves and trusts a different set of source documents. On our 5-question run, Perplexity named upGrowth for 2 category questions where ChatGPT, Gemini, Claude, Google AI Mode, Google AI Overviews and Google organic all did not. Checking a single engine gives you a partial and possibly misleading picture.
Q: How reliable is a single AI visibility check?
A: Directionally reliable, not precisely. We recorded the same query on the same engine producing opposite results 56 minutes apart on 9 September 2026. Use a single run to find out whether you’re absent or present. Use repeated runs over several weeks before you claim a trend.
Q: Should I use my brand name in the test questions?
A: Only for 1 or 2 of them. Branded questions almost always return a mention, which inflates the score without telling you anything useful. Buyers ask category questions before they know your name, so the category questions are the ones that predict pipeline.
Q: How long does it take to move an AI visibility score?
A: We don’t have a defensible number yet, which is why we’re publishing our own score as we work on it rather than quoting an industry average. The inputs that change it are content the engines can extract, third-party sources they already trust, and time for those sources to be recrawled.
Your Next Move: Find Out What Your Number Is
The uncomfortable part of this exercise wasn’t the score. It was realising we’d never actually measured it, while advising other companies to. Most marketing teams are in the same position right now. They have a rankings dashboard, a traffic chart, and no idea whether an answer engine would put them on a buyer’s shortlist.
Run your own questions through the AI Answer Gap tool. You’ll get your score by engine, the brands named instead of you, and the source domains those engines cited. It’s free, capped at 8 questions a day, and needs a business email.
If the result is bad enough that you want the full picture, we audit every page the engines cited, map the gap against each competitor that beat you, and hand back a prioritised plan. Scope depends on your site size, the number of questions and the markets you care about, so we quote those individually. Ask for a quote from inside the tool, or book a 30 minute call and we’ll walk through your grid live.
About the Author: I’m Amol Ghemud, Chief Growth Officer at upGrowth Digital. We help SaaS, fintech, and D2C companies shift from traditional SEO to Generative Engine Optimization. This shift has generated 5.7x lead volume increases for clients like Lendingkart and a 7x increase in AI Overview ranking power for Vance.
For Curious Minds
Generative Engine Optimization (GEO) redefines visibility as being named directly in the AI-generated answers that potential customers receive. Unlike traditional SEO, which focuses on ranking in a list of links, GEO is about becoming a direct recommendation from engines like ChatGPT and Perplexity. This distinction is vital because if you are not mentioned, you are not part of the buyer's initial consideration set. Your brand must move from being a source to being the answer itself. For instance, upGrowth found that despite years of content, its rank did not translate into a mention; it scored 0 out of 100 on a key query. The new goal is not just to provide information for the engine to cite, but to shape the engine's understanding of the market so it names your brand as the solution. Discovering how your brand is perceived by these engines is the first step to ensuring you exist where modern B2B buying decisions begin.
An 'answer engine' refers to a large language model like ChatGPT, Perplexity, or Google AI Mode that provides direct, synthesized answers to user questions rather than a list of links. Your absence from these answers is a major blind spot because it means you are completely invisible during the crucial discovery phase of the buyer journey. When a potential customer asks for a recommendation, the engine curates a shortlist, and if your name isn't on it, you have lost a lead before you ever knew it existed. The upGrowth case study demonstrated this perfectly, showing 8 misses out of 8 engines on a core business query. This is not about being a cited source in the footnotes; it is about being the explicit, named entity that the AI recommends. Failing to appear here is the modern equivalent of not being listed in the phone book, as this article further explores.
The variance in performance, where Perplexity scored 46 while others scored 0, highlights that not all answer engines are built the same. This difference likely stems from their distinct data sources, indexing processes, and how they weigh different signals like recency, source authority, or specific data points found in online content. Perplexity, for instance, often emphasizes real-time web search and citation quality more heavily than models trained on a more static dataset. For your B2B GEO strategy, this means a one-size-fits-all approach is insufficient and you must analyze and tailor your content for the specific engines your target audience uses.
To account for this variability, you should:
Diagnose your visibility separately on each major platform.
Analyze the sources cited by each engine for your target queries.
Adjust your content and digital PR strategy to align with the unique preferences of high-priority engines.
Understanding these nuances is key to building a resilient presence across the entire AI ecosystem.
You should view visibility on branded queries as a measure of existing brand equity, while visibility on non-branded queries is a direct measure of new customer acquisition potential. A high score on a query like 'upGrowth vs Gozoop' simply confirms that AI engines know you exist when prompted by name; it measures the traffic you already earned. In contrast, the 0 out of 100 score for a non-branded query reveals a critical failure to attract new buyers who do not know your name yet. The entire game in Generative Engine Optimization is to win the non-branded, category-level questions that precede a purchase decision. Relying on branded query performance creates a dangerous false sense of security, masking your invisibility to the vast majority of your total addressable market. The article details how to shift focus from these vanity metrics to the queries that actually drive growth.
The 0 out of 100 score uncovered a fundamental failure: despite having a long-standing domain (since 2017) and solid organic rankings for many terms, upGrowth's content was not structured to influence AI recommendations. This proves that legacy SEO and answer-engine presence are entirely different inventories. The diagnostic showed that strategies built for ranking on a list of blue links do not automatically translate to being named as the answer by generative AI. The AI engines, including Google AI Overviews and ChatGPT, did not perceive the company as a definitive or authoritative solution for 'best B2B SaaS growth marketing agencies in India'. This result demonstrates that your domain authority and keyword rankings are no longer sufficient guarantees of visibility where high-intent buyers are now looking for solutions. The full story explores this critical disconnect and what it takes to bridge the gap.
The score for upGrowth jumped to 21 out of 100 only because the second test run included a brand-specific question: 'upGrowth vs Gozoop which agency is better for growth marketing'. This result reveals how easily AI visibility metrics can be inflated by branded queries. The engines knew the company existed but only mentioned it when explicitly asked. They still failed to name upGrowth for the more valuable, non-branded category questions that actual prospects use. This highlights a critical flaw in many visibility tools that default to using brand-name prompts, creating a false positive that masks a real problem. True visibility is not about being recognized when someone asks for you, but about being recommended when someone asks for a solution like yours. This distinction is central to understanding your actual performance, a point the full analysis makes clear.
If you suspect your brand is invisible on AI, you can establish a clear baseline and find your gaps with a disciplined approach. Using a tool like the AI Answer Gap provides the necessary diagnostic data to move from suspicion to strategy. The key is to test how a real buyer would ask, not how you would search for yourself. The initial run by upGrowth used one high-intent query across 8 engines to get its stark 0/100 score.
Here is a practical three-step process:
Define Your Core Buyer Question: Identify the single most important non-branded question a prospect would ask when looking for a solution in your category.
Run the Diagnostic: Input your domain and that core question into the tool, selecting a representative mix of answer engines like ChatGPT, Perplexity, and Google AI Mode.
Analyze the Gaps: Review the results to see which engines mention you and which do not. Pay attention to competitors who are named, as their content provides clues for your own strategy.
This process gives you an objective starting point, which is essential before you begin to invest in optimizations.
Discovering a low GEO score is the starting point for a strategic overhaul, not just a content refresh. The first actions should focus on understanding why the engines are ignoring you and then building a foundation of authority for your category. After its 0 out of 100 score, upGrowth had to re-evaluate how its expertise was being communicated digitally. The goal is to shift from just publishing content to creating a clear, consistent narrative about your brand's position in the market that AI models can easily parse and trust.
Your immediate actions should include:
Competitor Source Analysis: For your target query, analyze which sources the AI engines cite when recommending your competitors.
Entity-Centric Content: Create and update content that clearly defines who you are, what problems you solve, and why you are a leader in your specific niche.
Structured Data and Knowledge Graphs: Implement schema markup and contribute to knowledge graphs like Wikidata to provide engines with structured, unambiguous information about your brand.
These foundational steps are crucial for beginning the long-term work of shaping AI perception, as the full journey details.
This situation implies that the value of your existing content library is no longer guaranteed by its SEO performance; its worth now depends on its ability to influence AI models. The upGrowth case, scoring 0 out of 100 despite a content library dating back to 2017, proves that a vast archive of blog posts is not enough. Content must be adapted to be machine-readable and explicitly state your authority and place in the market. Your library must evolve from a collection of keyword-targeted articles into a cohesive knowledge base that answers category-level questions definitively. To adapt, you should audit and update your key content to be more direct, use structured data, and build stronger connections between related concepts on your site. The future value of your content lies in its utility for training and informing AI, a strategic pivot the rest of the article explores.
This trend will force a significant shift in marketing priorities from traffic acquisition to direct recommendation, fundamentally altering budget allocation. As buyers get their shortlists from ChatGPT or Perplexity, the value of a top-ranking link on Google's organic results or a paid search ad diminishes. Budgets will migrate from bidding on keywords to activities that build brand authority in the eyes of AI models. This includes digital PR, expert content creation, structured data implementation, and influencing key knowledge sources. upGrowth's experience of being invisible to 8 out of 8 engines despite having SEO traffic is a leading indicator of this change. The new priority is not to win the click, but to win the mention within the AI's answer, as that is the new point of market entry. Exploring this budgetary and strategic shift is essential for staying competitive.
The most critical mistake is measuring visibility using brand-name prompts instead of the non-branded, category-level questions that real buyers use for discovery. When a tool defaults to queries containing your brand name, it almost always returns a positive result, as the AI is simply confirming what it already knows about that specific entity. This leads to a dangerous false sense of security. The upGrowth analysis highlighted this perfectly: their score jumped from 0 to 21 when a branded query was introduced, masking the fact they were still invisible for the questions that generate new leads. True visibility is measured by your ability to be recommended to users who have never heard of you. You must rigorously test your presence with the exact phrases your ideal customers would use to find a solution, not your name. The full article explains how to avoid this common pitfall.
A company with strong SEO can score 0/100 on a GEO test because the two systems value different signals. SEO rewards content that matches keywords and earns backlinks, making you a good source for a list of links. GEO, however, rewards entities that are unambiguously established as authoritative solutions within a category, making you the answer itself. Your content might rank well organically but fail to provide the clear, definitive statements that an AI model needs to confidently recommend you. The upGrowth case study, where a decade of publishing resulted in zero mentions across 8 engines, is a prime example of this disconnect. The solution is to create and structure your digital presence to build an undeniable association between your brand and its category leadership. This involves a dedicated strategy of entity optimization, contributing to knowledge bases, and crafting content that explicitly states your market position, as detailed further in the analysis.
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