We logged every source that 8 AI and search engines cited when asked which growth marketing agencies are best for B2B SaaS in India. The single most-cited domain across those runs belonged to one of the agencies the engines were recommending. The lists AI reads are largely written by the companies that appear on them, which changes what your content plan should contain.
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When an answer engine tells a buyer which vendors to consider, it’s summarising documents it retrieved seconds earlier. Those documents are the actual battleground, and almost nobody looks at them. Everyone looks at the answer.
On 9 September 2026 we ran upGrowth through the AI Answer Gap tool and stored not just the answers but every source URL each engine cited. We scored 0 out of 100, which we have written about separately. The more useful output was the source list, because it showed where the engines were getting their opinions.
Across 3 runs that day, the domain cited most often was growthspreeofficial.com, logged 9, 4 and 11 times. GrowthSpree is one of the agencies those same engines named as a recommendation. The engines were reading a competitor’s own page about the best agencies in the category, and repeating the conclusion.
That’s not a scandal. It’s a content strategy, and it’s working. The question for everyone else is what to do about it.
What the citation data showed
Across the 3 runs on 9 September 2026, a small set of domains supplied most of the citations for the question of who does B2B SaaS growth marketing well in India.
Several belonged to agencies being recommended in the same answers. growthspreeofficial.com was cited 9, 4 and 11 times across the runs, and GrowthSpree was named as a recommended agency 4, 5 and 4 times. growth.cx was cited 4, 4 and 5 times while being named 3, 2 and 4 times. thesmarketers.com and tripledart.com show the same shape at smaller volume.
The rest came from roundup publishers most marketers in this category have never heard of. syncgtm.com was cited 3, 5 and 4 times. zeroado.com, brandstory.in, onemetrik.com, wearetilth.com and aimarketinglab.in all showed up repeatedly. None of them are household names. All of them were feeding the answer.
Our own domain appeared in none of it. That’s the whole result in one sentence: we were absent from the answer because we were absent from the documents behind it.
The mechanism is simple enough that it’s easy to miss. An agency publishes a page titled something like “best B2B SaaS marketing agencies in India”. The page lists 10 agencies, describes each one fairly, and puts the publisher somewhere near the top. It’s genuinely useful, structured cleanly, and updated.
An answer engine retrieving documents for a buyer’s shortlist question finds that page highly relevant, because it is. It extracts the list. The publisher’s name comes along with it.
Nothing about this is manipulation. The page is real, the information is mostly accurate, and readers do find it helpful. It’s just that the category’s reference document was written by a participant, and the engines have no strong preference for neutrality. They have a preference for relevance, structure and extractability, all of which a competitor’s roundup satisfies better than your services page does.
Your services page tells the engine what you sell. A roundup tells the engine who the options are. Only one of those answers a shortlist question.
The aggregator layer nobody in the category has heard of
The second cluster in our data is stranger and more actionable. Domains like syncgtm.com, zeroado.com, onemetrik.com and wearetilth.com showed up repeatedly across engines. They aren’t big publications. Some are small agencies, some look like content operations built specifically to rank for category roundups.
They matter because they’re doing the job that trade publications used to do, and the engines are treating them as reference material. If your category has these sites, being absent from them is a measurable gap. Being present is often just a matter of asking, since most of them are actively looking for agencies to feature.
This is the part of a citation audit that surprises clients most. They expect the fix to be “write more blog posts”. The fix is frequently “get listed on the 4 obscure roundup sites that the engines are actually reading in your category”, and there’s no way to know which 4 without logging the citations.
It also means the work is unglamorous. Nobody’s marketing team wants a quarterly objective called “appear on zeroado.com”. That reluctance is precisely why it’s still available.
On the 5-question run, linkedin.com was cited 9 times, youtube.com 5 times and reddit.com 4 times. Those are 3 of the most-cited domains in the run, sitting alongside the agency sites and the roundups.
What that tells you is that the engines fall back to whatever public discussion exists when the professional content is thin. A LinkedIn post where a founder describes which agency they used, or a Reddit thread arguing about vendors, is retrievable evidence. Your case study PDF sitting behind a form is not.
There’s a practical implication for anyone running a LinkedIn strategy. Posts that name your category, name the alternatives and take a position are retrievable documents about your market. Posts that celebrate a company milestone are not. Same channel, same effort, different asset.
We’re not going to claim a measured relationship between LinkedIn posting and citation share, because we haven’t measured one. What we observed is that on this question, in this market, on this date, the engines cited LinkedIn 9 times and cited our own domain zero times.
What this changes about your content plan
If the engines are assembling shortlists from roundups, competitor pages and public discussion, then most B2B content plans are aimed at the wrong target. A quarterly calendar of thought leadership posts optimised for keywords produces documents that answer “what is X” questions, when the commercial question is “who should I hire for X”.
The first change is to publish the comparison content your category is missing. Name your real competitors, describe honestly where each one is stronger, and put your own position in context. Most companies refuse because it means conceding something. That refusal is why the only comparison content in your category was written by whoever had the least to lose.
The second is to make your proof extractable. An engine can cite “reduced cost per lead by 30% while scaling spend 4x over 11 months”. It cannot cite a logo wall. Specific figures with a time period and a starting position are what get quoted, which means gated case studies are invisible by design.
Then there’s the placement work: finding the specific roundups and directories the engines cite in your category and getting genuinely reviewed on them. Real profile, real references, real client outcomes. That’s a business development task more than a content one, and it usually sits with nobody.
Do the citation audit before any of it. The 4 sites worth pitching in your category are almost certainly not the 4 you’d guess, and ours certainly weren’t.
The uncomfortable version, for anyone selling services
We’re an agency writing about how agencies win AI citations by publishing lists that include themselves. The obvious next move would be for us to publish our own “best growth marketing agencies in India” roundup with upGrowth on it.
We’re going to publish a comparison page, and we’ll be on it, and you should discount our placement accordingly. What we can do differently is state the conflict on the page rather than hiding it, name the agencies that beat us in our own audit, and publish the measurement that showed we were losing. That’s a lower bar than neutrality and a higher one than the category currently clears.
The broader point holds regardless of who publishes it. Answer engines are building consideration sets from documents written by interested parties, because those are the documents that exist. The correction isn’t to complain about it. It’s to make sure accurate, specific, checkable information about your company exists in the places the engines read, and to keep checking whether it’s working.
6 Common Questions About AI Citations and Source Data
Q: Which sources do AI engines cite when recommending vendors?
A: In our 9 September 2026 runs on B2B SaaS growth marketing agencies in India, citations clustered around 3 kinds of source: pages published by agencies that were themselves being recommended, small roundup publishers such as syncgtm.com and zeroado.com, and public discussion on LinkedIn, Reddit and YouTube. Our own domain was cited zero times.
Q: Is it legitimate for a company to publish a roundup that includes itself?
A: It’s common and it works. The pages we saw cited were genuinely useful and reasonably fair to the other companies listed. The problem isn’t the practice, it’s that buyers reading an AI answer have no visibility into who wrote the underlying list. Publishing your own comparison is defensible if you disclose the conflict and describe competitors accurately.
Q: How do I find out which sources AI engines cite in my category?
A: Ask each engine your category’s shortlist question and record every source URL in the answer, then count the domains across engines and questions. The AI Answer Gap tool does this automatically across 8 engines, and the full method is documented here. Doing it manually for 8 questions takes roughly 90 minutes.
Q: Why do AI engines cite competitor blogs instead of my website?
A: Because a competitor’s roundup answers the buyer’s question and your services page answers a different one. Shortlist questions need a document that names and compares multiple providers. If nobody neutral has written that document in your category, the engines will use one written by a participant.
Q: Does getting listed on small roundup sites actually help?
A: It puts you into documents the engines are demonstrably reading, which is a precondition for being named. We can’t yet quote a measured lift, and we’d rather say so than invent one. What we can say is that in our own runs, every brand the engines recommended appeared in those roundups and we did not.
Q: Should case studies be gated?
A: Not if you want them cited. Answer engines can only quote what they can retrieve, so a case study behind a form is invisible to them regardless of how good the results are. Publish the specific numbers openly and gate something else if you need the lead capture.
Your Next Move: Look at Your Own Citation List
The score is the headline and the source list is the instruction. Most companies looking at an AI visibility report read the number, feel bad or relieved, and never open the sources. The sources are where the work is.
Run your category’s shortlist questions through the AI Answer Gap tool and you’ll get the domains each engine cited alongside the brands it named. It’s free, capped at 8 questions a day per company, and needs a business email. Then open every one of those domains and read what they say about your category.
If you’d rather have that done properly, our paid audit reads every cited page, maps what each one has that your equivalent doesn’t, identifies the specific publications worth pitching in your category, and returns an ordered plan. Scope depends on your site size, question count and markets, so we quote individually. Ask from inside the tool, or book a 30 minute call.
For Curious Minds
The AI Answer Gap describes the critical disconnect between being a competent vendor and being visible in the AI-generated answers buyers now use for discovery. Your company is absent from an AI's answer because it is absent from the specific documents the AI reads to create that answer. For B2B SaaS firms, this means that even a superior product or service is irrelevant if your domain does not appear in the source material an answer engine cites. The provided analysis proves this point: the test company scored 0 out of 100 on visibility because its domain was never cited. The battle for consideration has moved from the search results page to the source documents behind the AI's summary. To bridge this gap, your content strategy must focus on creating the kind of comparative, category-level content that these AI models are trained to find and synthesize. Understanding this gap is the first step toward building a content plan that ensures you are part of the conversation.
Answer engines determine the 'best' vendors by synthesizing information from relevant, well-structured documents that directly address the user's query. They do not have a preference for neutrality; they have a preference for relevance, structure, and extractability. A company's own services page is great at explaining what that single company sells, but it fails to answer a comparative question like “who are the best agencies?” In contrast, a market roundup article titled “Best B2B SaaS Marketing Agencies in India” directly matches the query, presents information in a list format that is easy for an AI to parse, and thus becomes a trusted source. The data showed that agency GrowthSpree was cited up to 11 times from its own roundup page. This shows that the engines are not conducting independent evaluations but are instead summarizing the most useful and topically relevant documents they can find, even if those documents are written by a participant in that market.
A 'Best Agencies' roundup is vastly more effective for influencing AI-driven shortlists because it directly answers the comparative question a buyer is asking. Your services page tells an engine what you sell, but a roundup tells the engine who the viable options are in the entire category. This distinction is critical. An answer engine's primary goal is to provide a comprehensive answer, and a well-structured list is a perfect format for that. The data reinforces this: domains publishing roundups, like growthspreeofficial.com and growth.cx, were frequently cited as sources and also named as recommended agencies. In contrast, companies relying solely on self-descriptive content were invisible. The key factors driving this performance difference are:
Topical Relevance: The roundup directly matches the user's comparative intent.
Structural Format: Lists and comparisons are easy for AI to parse and extract.
Apparent Authority: Authoring a category overview positions you as a knowledgeable guide.
Dive deeper into our analysis to see how these factors play out across different engines.
The data clearly shows that GrowthSpree successfully influenced AI recommendations by authoring one of the key source documents the AI used to form its opinion. Their domain, growthspreeofficial.com, was the single most-cited source, appearing up to 11 times across the test runs, while the agency itself was recommended up to 5 times in the same answers. This demonstrates a direct link between creating category-defining content and being featured by AI. The core lesson is that the most effective content strategy for AI visibility is to become the reference source for your own market. Instead of just promoting their own services, they published a genuinely useful roundup of the best agencies, including themselves. This content is highly relevant to a buyer's query, cleanly structured for AI extraction, and positions them as an authority. This strategy shows that you can shape the AI's understanding of your market by providing it with the best map.
These aggregator domains represent a new and highly influential layer in the information ecosystem, acting as the digital equivalent of niche trade publications for answer engines. Sites like syncgtm.com, which was cited up to 5 times, are often small agencies or dedicated content operations built specifically to rank for lucrative roundup-style queries. Their prominence shows that AI doesn't prioritize brand recognition or domain authority in the traditional sense; it prioritizes content that is highly relevant and perfectly structured to answer a specific question. For established brands, this is a major threat and an opportunity. It means your brand equity offers little defense if you are not creating this type of content. It also means you can gain visibility rapidly by adopting the same tactics, as these smaller players have proven that the barrier to becoming an AI source is low. The full data set reveals just how significant these unfamiliar players are in shaping buyer perceptions.
If your brand is not showing up in AI answers, your immediate goal is to create content that makes you a citable source for your industry. Focusing on your own services is not enough; you must provide a map of the entire category that an AI can easily read and reference. Here is a three-step plan to get started:
Identify Key Competitor Queries: Research the exact 'best of' or 'top alternatives' questions potential buyers are asking answer engines in your niche.
Create a Definitive Roundup Post: Publish a well-researched, genuinely helpful article that lists and fairly describes the top 10-15 players in your market, including your own company. Structure it with clear headings and summaries for easy AI extraction.
Update and Maintain the Asset: Keep the content current. An updated, high-quality resource is more likely to be repeatedly trusted and cited by AI models over time, solidifying your position as an authority.
This approach, modeled on the success of firms like GrowthSpree, shifts your content from being self-promotional to being a foundational document for your entire category.
The increasing reliance on AI for vendor discovery signals a fundamental shift in B2B marketing, diminishing the influence of traditional channels and gatekeepers. As buyers receive instant, synthesized shortlists from AI, the value of paying for placement on third-party review sites or courting analyst firms may decline. The new battleground is the AI's source list. The data shows that small, unknown domains like onemetrik.com and wearetilth.com can become key sources, bypassing established media entirely. This implies that influence is becoming decentralized, rewarding those who create the most useful, machine-readable content, not just those with the biggest budgets or longest history. In the future, a company's ability to be a primary source for AI will be a more valuable asset than a top-quadrant placement in a traditional analyst report, forcing a strategic reallocation of marketing resources toward creating definitive category content.
Marketing leaders must pivot their content strategies from a brand-centric to a category-centric model to maintain visibility in an AI-driven world. The current preference of answer engines for relevance over neutrality is not a flaw to be fixed but a reality to be addressed. This means your primary strategic goal should be to create and own the most useful reference documents for your market. This requires a shift in mindset and resources. Over the next few years, successful strategies will include:
Mapping the Information Landscape: Proactively identify the comparative and shortlist-style questions that define your category.
Becoming a Category Publisher: Dedicate resources to creating and maintaining high-quality, structured roundup content that fairly represents the market, with your brand naturally included.
Focusing on Extractability: Train content teams to write for machines as much as for humans, using clear headings, lists, and factual summaries.
As the data shows, being absent from the source documents is the same as being absent from the answer. The full article explores how to build this capability within your team.
Publishing market-level analysis directly solves the AI visibility problem by changing the nature of the question your content answers. A services page answers “What do you do?,” while a market roundup answers “Who are the best options for X?” Buyers using AI are asking the second question, making your self-focused content irrelevant to their query. By creating a comprehensive market overview, you are producing a document that is highly relevant to a shortlist-style query, making it a prime candidate for an AI to cite. The provided data proves this: our own domain was absent because we only had self-focused content, while GrowthSpree was highly visible because it published a market analysis. This strategic shift repositions your brand from just a participant in the market to a helpful guide for the market, which is exactly the role an answer engine is looking for in a source. You solve the problem by creating the exact asset the AI needs to do its job.
The data provides clear, quantitative evidence of the link between being a source and being a recommendation. Across three separate runs, the domain growthspreeofficial.com was cited a total of 24 times (9, 4, and 11), making it the most frequent source. In those same runs, the agency GrowthSpree was named as a recommended vendor a total of 13 times (4, 5, and 4). This consistent pattern is not a coincidence. It shows that the AI is not just finding the roundup page but is also extracting the names of the companies listed on it, including the publisher. The mechanism is simple: the AI reads the list you wrote and repeats it back to the user as a credible answer. This direct correlation proves that you can significantly increase your chances of being recommended by authoring the source material that answer engines rely on. The complete data reveals this pattern across multiple agencies, solidifying the finding.
The evidence decisively shows AI recommendations are not impartial; they are a summary of the most relevant and machine-readable documents available, regardless of who wrote them. The key finding that the most-cited source for 'best agencies' was an agency itself (GrowthSpree) is the strongest proof. The AI did not seek out a neutral third-party report; it found a well-structured, topically relevant page written by a competitor and used it as a primary source. This reveals a bias not towards a specific company, but towards a specific type of content. To correct your strategy, you must stop assuming objectivity and start creating the biased-by-design content the AI prefers. This involves publishing your own authoritative, fair, and comprehensive market roundups that naturally position your company as a leading option. By becoming the author of the reference document, you align your goals with the AI's operational logic, making your inclusion in its answers a near certainty.
Creating an ethical and effective roundup involves a commitment to genuine user value, transparency, and comprehensive research. The goal is to become a trusted guide for your category, not to trick the user or the AI. Here is how to do it correctly:
Conduct Thorough Research: Profile a wide range of competitors based on objective criteria like specializations, client reviews, and notable work. A list of 10-15 vendors is more credible than a list of 3.
Provide Balanced Descriptions: Write fair, accurate, and genuinely helpful summaries for every company listed, not just your own. Acknowledge the strengths of your competitors.
Be Transparent About Your Placement: Place your own agency on the list, but do not automatically put yourself at number one without justification. You can add a subtle disclosure like, “As the authors, we have deep expertise in X, making us a strong choice for…”
Structure for Extractability: Use clear H2/H3 tags for each company, consistent formatting, and summary tables to make the information easy for both humans and AI to parse.
This approach works because it prioritizes helping the reader, which in turn creates the exact kind of high-quality, relevant source material that answer engines are designed to reward.
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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