Contributors:
Amol Ghemud Published: September 10, 2026
Summary
An AI visibility score is only meaningful if you know which engines it covers, how position is weighted, and which questions were asked. This is the full method behind the AI Answer Gap score: 8 engines, 4 position tiers, a country setting, and a stated list of things the number cannot tell you. You can run the same method manually without any tool.
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Most AI visibility tools hand you a number between 0 and 100 and don’t tell you how it was built. That number is unusable. A brand can score 80 because the test questions contained its own name, or score 20 because the tool only checked 1 engine on a bad afternoon. Without the method, the score is decoration.
We published our own method because we had to look at a 0 on our own domain and decide whether to believe it. On 9 September 2026 upGrowth scored 0 out of 100 across 8 engines for a single category question, and the first thing we did was interrogate the scoring rather than the result. The method held up. We published the score.
This page documents how the AI Answer Gap score is calculated, what it deliberately excludes, and how to reproduce the whole thing by hand if you’d rather not use anyone’s tool. Nothing here is proprietary. The method is the part worth copying.
If you take one thing from this page, take the section on questions. The questions you test change the score more than anything you do to your website.
Updated 24 October 2026: We changed one rule in this method. The AI Answer Gap score now averages category questions only, and answers to questions that contain your brand name are shown beside the score, never inside it. On our own runs, the old blended average scored upGrowth 20.9 where the category-only score was 4.6. The sections below reflect the new rule.
What an AI visibility score should actually measure
An AI visibility score should measure whether an answer engine names your brand when a buyer asks a purchase question that doesn’t contain your name. Everything else is a proxy.
Several other things get measured instead, and each one flatters the brand paying for the report. Brand mention volume counts how often your name appears in answers to prompts that contained your name. Sentiment scores how positively you’re described in conversations you were already winning. Crawler access checks whether AI bots can reach your site, which is necessary but says nothing about whether they picked you over someone else.
The measurement that predicts pipeline is narrower. A buyer asks “best X for Y in Z”. The engine returns a shortlist. You’re on it or you aren’t, and your position on it matters. That’s the event worth scoring, because that’s the moment your name enters or misses a consideration set.
Everything in the method below exists to measure that one event across enough surfaces and enough questions to be worth acting on.
The 8 engines, and why each one earns a slot
The AI Answer Gap queries 8 surfaces: ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, Google AI Overviews, Google organic results and Bing. Each is queried through an official API, and each raw answer is stored with the sources it cited.
The 4 chat assistants are in the set because they’re where the question now gets asked directly. They differ from each other more than people expect. On our 9 September 2026 run, Perplexity named upGrowth for 2 category questions where ChatGPT, Gemini, Claude and the Google surfaces all did not. One engine is not a sample.
Google AI Mode and Google AI Overviews are separate entries because they’re separate products with separate behaviour, and because an AI Overview does not appear for every query. When Google shows no AI answer for a question, that result is excluded from the score rather than counted as a miss. Counting a non-existent answer as a failure understates the score, which is a bug we found in our own first version and corrected.
Google organic and Bing stay in the set for a reason that sounds old-fashioned. The classic ranked list is still where a large share of the documents that feed AI answers get discovered, and comparing your organic position against your answer-engine position tells you which of the 2 problems you have. Ranking well and being absent from answers is a content extractability problem. Being absent from both is an authority problem.
Every run records a country setting, defaulting to India. This matters more than it sounds. Answer engines localise, and a visibility report generated from a United States IP address describes a market your buyers don’t live in.
Each engine answer is scored on where the brand appears among the providers named, using 4 tiers. Named first or as the clear primary recommendation scores 100. Named within the first 3 providers scores 75. Named lower down scores 45. Not named scores 0. An answer that explicitly recommends the brand, rather than merely listing it, adds 10, capped at 100.
The gap between 100 and 75 is deliberately large. In a spoken or scrolled answer, the first name carries most of the weight, and the difference between being the recommendation and being an option is commercial, not cosmetic.
For Google organic and Bing, position comes from the ranked list rather than from reading prose. Position 1 maps to 100, positions 2 and 3 map to 75, positions 4 to 10 map to 45, and absence from the top 10 scores 0. That keeps the classic engines on the same scale as the answer engines without pretending they work the same way.
The run score is the mean of every scored cell across every engine, for the category questions only. A question counts as branded if it contains the brand name or its domain, and answers to branded questions appear in the grid but are never averaged into the score. We made that change on 24 October 2026, after finding that branded questions had lifted our own score from 4.6 to 20.9 on the same run. The gap is 100 minus the score, which is the number worth putting in front of a board, because it’s the share of the buying conversation you’re currently absent from.
Reading the answers is done by a language model working to a fixed instruction: name the brand only if it or its domain literally appears, record the other providers named, and give a one-sentence reason. It’s instructed to ignore generic software tools, publications and aggregators when listing competitors, because an early version of ours cheerfully reported ChatGPT as a competitor to a marketing agency.
Why the questions decide the score more than your website does
The single biggest determinant of an AI visibility score is whether the test questions contain the brand’s own name. A set of branded questions will return a comfortable score for almost any company with a website.
Our own data shows the size of the effect. On a 5-question run on 9 September 2026, ChatGPT scored 20 out of 100 for upGrowth. Our system recorded the misses individually: ChatGPT failed to name us on every category question and named us on exactly 1, “upGrowth vs Gozoop which agency is better for growth marketing”. The entire score came from the question with our name in it.
A useful question set is mostly category questions with at most 2 branded ones. Category questions are the ones a buyer asks before they’ve heard of you, phrased the way they’d actually type them: “best business loan for MSMEs in India”, “which agency should a Series A fintech hire for performance marketing”, “how much does a fractional CMO cost”. Include the market in the question when the market matters. Even 2 branded questions are enough to skew an average, which is why the AI Answer Gap no longer counts them in the score at all.
Keep 1 comparison question and 1 reputation question in the set, because they measure something different and still worth knowing. “X vs Y which is better” tells you how the engines frame you against a named rival. “Is X any good, reviews” tells you what third-party material the engines have found about you. Neither belongs in the same bucket as category demand.
A single run is a snapshot, not a measurement. We recorded the same query on the same engine returning opposite results 56 minutes apart on 9 September 2026: Perplexity did not name upGrowth for “best growth marketing agencies in India for B2B SaaS” at 12:35 IST, and named us for the identical query at 13:31 IST. Answer engines retrieve live documents at query time, so 2 identical prompts can draw on different sources.
That volatility sets a hard limit on precision. A one-shot score tells you reliably whether you’re absent, occasionally present, or consistently recommended. It does not support a claim that you improved from 34 to 41. Treat any single run the way you’d treat a single day of ad spend data.
The score also says nothing about volume. It doesn’t know how many buyers ask that question, and a perfect score on a question nobody asks is worth nothing. Pair the score with whatever demand data you have, and weight the questions accordingly.
There are measurement artifacts worth knowing about if you’re building this yourself. Some engines return source URLs wrapped in their own redirect domain rather than the publisher’s, which will pollute a “most cited domains” list with the engine’s own infrastructure unless you resolve them. Search APIs occasionally return results with no relationship to the query, and a citation list you haven’t sanity-checked will make you look foolish in front of a client. We check ours before anything reaches a prospect.
What counts as a good score
We don’t have a defensible benchmark yet and won’t invent one. Publishing an industry average built on a few dozen runs would be the kind of number this article is arguing against.
What we can say is what the bands mean structurally. A score at or near 0 on category questions means you are not in the consideration set at all, and the fix is an authority problem before it’s a content problem. On a tool that blends branded and category questions, a score in the 20s usually means the branded ones are carrying you, which is exactly what happened to ours before 24 October 2026. Consistent scores above 70 on genuine category questions mean you’re being recommended, and the work shifts from getting named to defending position.
The number that matters more than the score is the spread between engines. A brand at 60 everywhere has a different problem from a brand at 100 on Perplexity and 0 on everything else. The second one has content that a specific retrieval system likes, and no presence in the sources the others read.
How to run this method manually, without any tool
You can reproduce the whole thing by hand in about 90 minutes. It’s tedious, not difficult, and doing it once by hand teaches you more than reading any report.
Write 8 questions first, before you open any engine. 6 category questions phrased as a buyer would type them, 1 comparison question naming a real rival, 1 reputation question. Include your market in the wording where it’s relevant.
Then open each engine in a fresh session so your history doesn’t influence the answer, set your location if the product allows it, and paste each question in unchanged. Record 4 things per answer: whether your brand was named, where it sat among the providers named, which other brands were named, and which sources were cited. Screenshot the answer, because you’ll want the evidence later and the answer won’t be there tomorrow.
Score each cell using the 4 tiers above, average them, and subtract from 100 to get your gap. Then do the part most people skip: open every source the engines cited and read it. That’s where the actual instruction sits, because those pages are what the engine believed. Our own citation data pointed at competitor-written roundups more than at anything we could have guessed.
Repeat the same question set weekly for a month before you draw a trend line. Given the volatility documented above, 4 runs is the minimum before a change means anything.
A: In the AI Answer Gap method, each engine answer scores 100 if the brand is named first or as the primary recommendation, 75 if named in the first 3 providers, 45 if named lower down and 0 if not named, with 10 added for an explicit recommendation and a cap of 100. Since 24 October 2026, the run score is the mean across every engine for the category questions only. Answers to questions containing your brand name appear in the grid but never count.
Q: Which AI engines should be included in a visibility check?
A: At minimum ChatGPT, Perplexity, Gemini and Google AI Mode, because their answers diverge substantially for the same question. Adding Claude, Google AI Overviews, Google organic and Bing gives you the comparison between answer-engine presence and classic ranking, which tells you whether you have a content extractability problem or an authority problem.
Q: Should test questions include my brand name?
A: Mostly not. Branded questions almost always return a mention and inflate the score. On our 9 September 2026 run, ChatGPT’s entire score for upGrowth came from the single question containing our own name, while every category question missed. Use category questions for 6 of 8 slots.
Q: Why do AI visibility results change between runs?
A: Answer engines retrieve live documents when you ask, so the source set behind 2 identical prompts can differ. We recorded Perplexity giving opposite results for the same query 56 minutes apart on 9 September 2026. Run the same question set weekly for a month before treating any change as a trend.
Q: Does an AI visibility score account for search volume?
A: No. The score measures whether you’re named for the questions you chose, not how many people ask them. Weight your question set toward the questions your buyers actually ask, and read the score alongside your demand data rather than on its own.
Q: Can I measure AI visibility without paying for a tool?
A: Yes. Write 8 questions, ask each engine in a fresh session with your location set, and record whether you were named, your position among named providers, the other brands named and the sources cited. Score with the 4 tiers and average them. Budget about 90 minutes.
Q: What is a good AI visibility score?
A: There’s no credible published benchmark yet. Structurally, near 0 on category questions means you’re outside the consideration set, and consistent scores above 70 on category questions mean you’re being recommended. On a tool that blends branded and category questions, a score in the 20s usually means the branded ones are carrying you. The spread between engines matters more than the average.
Your Next Move: Run the Method on Your Own Domain
The method above is free to copy and we’d rather you used it than trusted a number you can’t interrogate. If you’d rather not spend the 90 minutes, the AI Answer Gap tool runs it for you across all 8 engines and gives you the engine-by-engine grid, the brands named instead of you, and the source domains each engine cited. It’s free, capped at 8 questions a day per company, and needs a business email.
Where it gets useful is the layer underneath the score. Reading every page the engines cited, working out what those pages have that yours don’t, and turning that into an ordered plan is the actual job, and it’s what we do as a paid audit. Scope depends on your site size, question count and markets, so we quote each one individually. Request a quote from inside the tool, or book a 30 minute call and we’ll read your grid together.
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
An effective AI visibility score isolates one critical event: your brand being named in an answer to a purchase-intent question that did not include your brand name. This focus is crucial because it directly measures your ability to enter a buyer's consideration set at the moment of discovery, which is the true predictor of pipeline. Metrics like simple brand mention volume are often inflated by queries that already contained your name, while sentiment scores only tell you how you are perceived in conversations you were already a part of.
A truly diagnostic score is built on a transparent method that evaluates your performance across multiple surfaces. The AI Answer Gap score, for instance, is based on:
Multiple Engines: It queries 8 distinct engines, including ChatGPT, Perplexity, and Google AI Overviews, because performance varies significantly between them.
Unbiased Questions: It uses non-branded, purchase-oriented questions (e.g., "best X for Y") to simulate a real buyer's journey.
Positional Weighting: It understands that being the first brand mentioned is more valuable than being the last.
This multi-faceted approach prevents the score from becoming a decoration and instead turns it into an actionable tool for identifying where you are failing to meet potential customers. Discover the full methodology to see how this focus on unprompted discovery can reshape your content strategy.
The AI Answer Gap method provides a reliable visibility metric by grounding its score in a transparent, multi-layered framework designed to reflect real-world buyer behavior. Its credibility comes not from a single number, but from its documented process of querying 8 distinct AI and search surfaces, from ChatGPT to classic Bing results, and weighting the position of any brand mention. This is fundamentally different from an opaque score that offers no context.
The methodology's strength lies in its three core pillars:
Comprehensive Surface Coverage: Querying a diverse set of engines like Perplexity, Gemini, and Google AI Mode acknowledges that there is no single source of AI truth. As upGrowth's own test showed, one engine might name you while four others do not.
Focus on Buyer Intent: It exclusively measures performance on unprompted, purchase-related questions, which is the moment that directly impacts sales pipeline.
Positional Analysis: The score reflects where you appear in an answer, correctly valuing a top mention over a citation buried at the end.
By combining these elements, the score moves beyond a simple pass-fail and helps diagnose specific issues, like whether you have an authority problem or a content extraction problem. See how you can apply these same principles to build a visibility measurement you can actually trust.
Comparing performance across conversational chat assistants and integrated search AI reveals distinct strategic gaps because they operate on different data and serve different user contexts. Chat assistants like ChatGPT and Claude often synthesize information from a broader, pre-trained corpus, making them a test of your brand's overall authority and mindshare across the web. If you are absent here, it may signal a deep-seated authority problem. In contrast, Google AI Overviews are more tightly coupled with real-time search results, making them a test of your content's immediate relevance and extractability for a specific query.
A brand like upGrowth might find itself mentioned by Perplexity but be absent from a Google AI Overview for the same query. This suggests its content is discoverable but not yet structured optimally for Google's answer synthesis. A comprehensive strategy requires you to:
Analyze chat assistant results to gauge your foundational brand authority.
Examine search AI results to diagnose content-level extraction issues.
Track both to ensure your strategy addresses short-term content optimization and long-term brand building.
This comparative analysis is essential for moving from simply knowing your score to understanding precisely why it is what it is. Learn more about how the 8 engines in the AI Answer Gap framework provide this diagnostic clarity.
The example of upGrowth scoring a 0 out of 100 on 9 September 2026 is a powerful illustration of why a transparent methodology must come before any result. Confronted with a perfect failure, the team's first action was not to panic about their content but to interrogate the scoring process itself. Because the method was open and verifiable, they could confirm its validity and trust the harsh result, which they then published. An opaque score would have left them guessing if the tool was broken or if their brand was truly invisible.
This evidence highlights a crucial lesson for marketers: a score is only as valuable as the method behind it. Without transparency, you cannot distinguish a genuine performance issue from a flaw in the measurement tool. The upGrowth case demonstrates that a trustworthy framework allows you to:
Validate Results: Confirm that a low score is a real signal, not noise from a poorly designed test.
Build Confidence: Trust the data enough to make strategic decisions, even when the news is bad.
Diagnose Problems: Use the methodological components, like the check across 8 engines, to pinpoint the source of the failure.
This commitment to a transparent process is what makes a visibility score a strategic asset rather than an arbitrary number. Explore the full method to understand how to build this same level of trust in your own measurements.
The finding that Perplexity mentioned upGrowth when other major engines like ChatGPT, Gemini, and Claude did not is direct evidence of significant variability and fragmentation in the AI landscape. It proves that there is no single, monolithic AI; instead, we have a diverse ecosystem of models with different training data, update cycles, and algorithms for synthesizing answers. This makes relying on a single engine for visibility measurement a critical strategic error.
This example powerfully illustrates that your brand's visibility is not a fixed attribute but is highly contextual to the platform being queried. For marketing leaders, this has profound implications:
Single-Engine Blind Spots: A tool that only checks one engine can give a false sense of either security or failure. upGrowth's score would be completely different if it only measured Perplexity.
Strategic Diversification: You must optimize for a range of behaviors, as what works for Google's AI may not work for a different chat assistant.
The Need for Broad Measurement: An accurate picture of AI visibility requires testing across a curated set of surfaces, which is why the AI Answer Gap method queries 8 distinct engines.
This variability is not a temporary bug but a feature of the current market. Learn how to build a measurement strategy that accounts for this diversity and avoids dangerous oversimplifications.
To manually replicate the AI Answer Gap assessment, a company can follow a disciplined process focused on consistent questioning and objective scoring. This approach ensures the resulting metric is a reliable indicator of performance on non-branded, purchase-intent queries, rather than an arbitrary number. The key is to standardize every step to ensure your findings are comparable over time.
Here is the essential four-step plan to implement this manually:
Define Your Question Set: Curate a list of high-value, non-branded questions your ideal buyers would ask (e.g., "best CRM for small businesses in finance"). The quality of these questions will determine the value of the entire exercise.
Query All 8 Surfaces: For each question, query all specified engines: ChatGPT, Perplexity, Gemini, Claude, Google AI Mode, Google AI Overviews, Google organic, and Bing. Record the full, raw answer from each.
Score Brand Mentions: Review each answer and score your brand's appearance based on position. For example, assign points based on a tiered system (e.g., 3 points for first mention, 2 for second, 1 for any other mention). A non-mention receives a 0.
Aggregate and Analyze: Sum the scores across all questions and all engines to get a total visibility score. Compare your AI answer score to your Google organic position to diagnose content extractability versus authority issues.
This manual process, while intensive, provides a transparent and actionable baseline for your visibility. Discover more about the nuances of this method and how to interpret your results.
Brands that continue to prioritize only traditional organic search rankings face a significant risk of becoming invisible to the next generation of buyers. As users shift from scanning blue links to consuming synthesized AI answers, ranking on a search engine results page will no longer guarantee entry into a customer's consideration set. The long-term implication is a gradual but decisive erosion of market relevance and pipeline.
Focusing exclusively on organic rankings ignores the two new problems brands must solve: authority and extractability for AI models. A brand like upGrowth might rank well organically but still score a 0 in AI answers if its content is not easily parsed and trusted by engines like Gemini or Google AI Overviews. The strategic imperatives must shift:
From Ranking to Being Named: The goal is no longer just to be a source link but to be explicitly mentioned as a solution within the AI-generated text.
Content for Machines and Humans: Content must be structured for easy extraction by AI while still being compelling for human readers.
Authority Across the Web: AI models synthesize information from many sources, so brand authority must extend beyond your own domain.
Neglecting this shift means optimizing for a discovery behavior that is declining in influence. Learn how tracking your visibility across 8 different AI surfaces can help you adjust your strategy before you are left behind.
The distinct evolutionary paths of AI models like Gemini and Claude mean that a one-size-fits-all approach to AI visibility is destined to fail. As these engines develop unique training methodologies, data sources, and logical reasoning, they will increasingly provide different answers to the same user query. This divergence will make it impossible to secure visibility by optimizing for a single, hypothetical "AI algorithm."
This trend demands a strategic shift toward portfolio-based performance tracking. Just as you would not invest in a single stock, you cannot bet your brand's visibility on a single AI engine. The impact on marketing strategy is profound:
Fragmented Customer Journeys: Different customer segments will use different AI tools, and your brand must be present across the ones relevant to your audience.
Engine-Specific Optimization: You may need to adapt content and authority signals to align with the specific preferences of different models.
Continuous Monitoring: The only way to keep up is through consistent measurement across a broad set of surfaces, like the 8 engines in the AI Answer Gap score.
As upGrowth's experience showed with Perplexity, a win on one platform can be a miss on another. Future-proofing your strategy requires embracing this complexity. Discover how to build a resilient visibility plan in a multi-engine world.
The most common mistake companies make after receiving a low AI visibility score is immediately jumping to tactical content fixes without first validating the measurement itself. They rush to rewrite pages or build new links based on a number that may be flawed due to poor question selection or an opaque methodology. This reaction is inefficient and often solves the wrong problem.
The AI Answer Gap framework prevents this by making its methodology, especially question selection, the central focus. It asserts that the questions you test change the score more than anything you do to your website. By adopting this principle, a company like upGrowth, when faced with a score of 0, correctly interrogated the test before changing its strategy. This approach leads to a more effective response:
Problem Validation: It forces you to confirm you are measuring what matters, specifically non-branded, purchase-intent queries that reflect real buyer journeys.
Accurate Diagnosis: A score based on strong questions allows you to correctly identify the root cause, such as an authority issue versus a content extractability issue.
Efficient Action: It ensures your resources are spent on solutions that address a verified visibility gap, not a ghost created by a bad test.
Before you act on any score, you must first be able to trust how it was made. Learn how to design a question-first measurement process to guide your AI visibility strategy.
Providing a single, unexplained AI visibility score is a significant problem because it divorces the number from the context needed to act on it. A brand might receive a score of 20, but without knowing which of the 8 engines were checked, what questions were asked, or how position was weighted, the metric is useless for strategic planning. It becomes a vanity number that cannot be trusted to predict actual pipeline impact, as it could be low because the tool had a bad run or used irrelevant questions.
The solution is to reject any metric that is not accompanied by a transparent, reproducible methodology. A meaningful score is not just a number; it is the output of a clear process. The AI Answer Gap framework provides the necessary components for a trustworthy metric:
Engine Transparency: It clearly states which engines (ChatGPT, Perplexity, etc.) are included.
Query-Level Detail: It is built on performance in response to specific, purchase-intent questions that do not contain the brand name.
Open Methodology: The entire scoring system is documented so that a company like upGrowth can replicate it manually.
By demanding this level of transparency, brands can ensure their AI visibility score is an actionable diagnostic tool, not just decoration. See how to adopt this methodological rigor for your own measurement.
This exact pattern indicates a content extractability problem, not an authority problem. Strong organic rankings show that Google's classic algorithms view your domain as authoritative and relevant for the query. However, your absence from AI answers, including Google AI Overviews, means the content on your high-ranking pages is not structured in a way that AI models can easily parse, understand, and synthesize into a concise answer.
The strategic solution is to shift focus from just keywords and backlinks to structuring content for machine consumption. Your pages must be both discoverable by search crawlers and digestible for answer engines. Key actions include:
Clear, Concise Summaries: Add direct, factual summaries at the top of your pages that plainly answer the core question.
Structured Data: Use schema markup and well-defined HTML structures (headings, lists, tables) to give your content semantic meaning.
Factual Language: Prioritize clear, unambiguous language over marketing jargon, as AI models favor verifiable statements.
This is a different skill set than traditional SEO, and as upGrowth's analysis shows, it requires tracking both organic and AI performance to diagnose correctly. Learn how to adapt your content to be cited directly in the answers your future customers will see.
The selection of test questions is the most critical factor because it aligns the entire measurement process with a specific business outcome: capturing new customers at the point of consideration. A technically perfect score that measures visibility on branded queries (e.g., "upGrowth reviews") is not a growth metric; it only confirms that people who already know you can find you. In contrast, focusing on non-branded, purchase-intent queries (e.g., "best analytics tool for marketers") directly measures your ability to win new business.
This choice of questions fundamentally changes what is being measured:
It Simulates Real Discovery: It mirrors the journey of a prospective buyer who has a problem but does not yet know your solution.
It Predicts Pipeline: Success on these queries is a leading indicator of pipeline growth, making the score a strategic business tool.
It Provides Actionable Insight: Failing on these questions provides a clear mandate to improve content and authority for specific use cases.
The AI Answer Gap method emphasizes that the query list is more influential than any website tweak. While testing across 8 engines is important, a flawed question set will render those results irrelevant. Explore how to build a query set that makes your visibility score a true measure of market capture.
Amol has helped catalyse business growth with his strategic & data-driven methodologies. With a decade of experience in the field of marketing, he has donned multiple hats, from channel optimization, data analytics and creative brand positioning to growth engineering and sales.
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