How B2B Software Marketers Can Get Their Software Mentioned by AI Chatbots and LLMs

By Community Team February 20th, 2025

Product marketers at B2B software companies are now looking beyond traditional search engine optimization – they want their products to surface organically in AI chatbot responses. Whether it’s OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, xAI’s Grok, open models like DeepSeek, or AI search assistants like Perplexity, these systems are increasingly providing answers that mention specific products or brands. As a marketer at a software vendor, how can you ensure your B2B SaaS product is among them? This comprehensive guide explains the emerging practice of Generative Engine Optimization, how large language models (LLMs) gather information, why web presence and authority matter, and what content, SEO, and community strategies can boost your product’s mention rate over time.

How LLMs Learn: Training Data and Information Sources

To influence AI outputs, you must first understand how LLMs are trained and where they get their knowledge. Most foundation models (like ChatGPT, Gemini, Claude, etc.) train on vast text corpora that include:

Software Directories (e.g., SourceForge): SourceForge, as the largest B2B software review and comparison site¹, is an authoritative and highly visible site for both open-source and commercial tools. LLMs may pull information from categories, product listings, and reviews hosted on SourceForge, which can further enhance their knowledge of a particular software product. With a Moz Domain Authority of 93, SourceForge has an even higher Domain Authority than Reddit². Having a presence on SourceForge means your product is likely to be indexed and included in AI-generated answers, especially if the platform is mentioned in relation to software discovery or recommendations.
Curated content: Not all web content is treated equally. Training datasets often prioritize high-quality sources. A recent study by Ziff Davis³ found that curated datasets (like OpenWebText, derived from Reddit’s highly upvoted links) skew heavily toward high-authority sites, whereas raw web crawls contain more low-quality text. In fact, 84% of content in some curated training sets came from sites with Moz Domain Authority ≥60⁴, such as major websites and news publishers. In short, LLMs “prefer” content from reputable, popular websites and may downweight obscure sources.
Web crawls: Massive datasets such as Common Crawl and its refined versions (like C4) provide snapshots of the open web. These include everything from blog posts to news articles. For example, OpenAI’s GPT models are trained on data from Common Crawl, Reddit, Wikipedia, reference sites, and news content filtered for quality⁵. OpenAI themselves state that their foundation models, including ChatGPT, are developed using “publicly available information on the internet.”⁶
Forums and developer communities: Reddit has been explicitly cited as a key data source (Reddit’s own filings note many LLMs use its content). Technical Q&A forums like Stack Overflow and open-source repositories (GitHub, etc.) have also been ingested by models, especially ones tuned for coding help. These community sources often contain product mentions in context of real-world problems and solutions.
Books and reference material: Many LLMs include books, encyclopedias, and public documentation in their training mix. This means official product documentation and knowledge base articles (if publicly accessible) can become part of an LLM’s knowledge. For example, a well-structured API guide or developer docs on your website might be indexed during training.

It’s important to note that some chatbots also use Retrieval-Augmented Generation (RAG): they consult live data via search at query time. For instance, ChatGPT’s optional web browsing relies on Bing search results, and Google’s Gemini leverages Google’s search index⁷. Tools like Perplexity similarly fetch current web content for each answer. In these cases, being visible in search engine results in real-time can directly lead to mentions in answers. Overall, influencing AI chatbots requires both being part of the model’s static training data and maintaining a strong presence in the live web ecosystem that AI agents draw from.

What is Generative Engine Optimization (GEO)?

Generative Engine Optimization (GEO), also known as Answer Engine Optimization (AEO), refers to the practice of optimizing content and strategies to improve how generative AI models, such as large language models (LLMs), retrieve, process, and present information. While traditional SEO focuses on improving a website’s visibility on search engines, GEO tailors content specifically for AI-driven systems that generate human-like responses. This involves not only optimizing for keywords but also structuring content in a way that makes it easily digestible for AI models, ensuring that the content is accurate, authoritative, and relevant. GEO strategies include optimizing product listings, documentation, community engagement, and content creation to ensure that AI chatbots and virtual assistants can efficiently pull from this material. As generative AI systems increasingly become the go-to source for users looking for information, businesses need to adjust their content strategies to meet the evolving demands of these technologies.

Why Web Presence & Authority Shape AI Chatbot Answers

Having a strong web presence isn’t just for human search engines – it significantly shapes LLM outputs. Because models learn from patterns in their training data, the frequency and context of your product’s mentions online will determine whether an AI deems it relevant to a query. In essence, the more an LLM “sees” your product associated with certain topics, the more likely it will include it when responding to a related question. As marketing expert Rand Fishkin puts it, “The currency of large language models is not links… the currency is mentions (specifically, words that appear frequently near other words) across the training data.”⁸ If your SaaS product is consistently mentioned alongside key industry terms or solutions, the model statistically learns to output your brand in that context.

However, quantity of mentions isn’t everything – quality and authority of sources matter greatly. LLM training heavily favors trusted domains. High Domain Authority (DA) sites and well-established publications carry more weight in training datasets, as we referenced above. This means a single mention on a top-tier site (e.g. SourceForge or TechCrunch article) can count for more than dozens of obscure blog mentions. It’s akin to how a backlink from a high-PR site boosts SEO more: here, a mention in authoritative content more strongly influences the model.

Conversely, content from low-credibility sites may never make it into the model at all (either filtered out during training or not crawled). AI companies actively curate training data to improve output quality. OpenAI, Google, and others have even struck licensing deals with major publishers to include their archives. The practical takeaway: if your product is primarily talked about on low-authority pages or behind paywalls unaccessible to crawlers, chatbots probably won’t know about it. A robust, visible presence across the open web – especially on sites that LLM developers are known to include – is critical.

Moreover, brand popularity plays a role. An analysis by Growth Memo found that brand search volume had a positive correlation (~0.33) with AI chatbot mentions; in other words, the more popular and well-known the brand, the more often it appeared in LLM responses⁹. According to Kevin Indig at Growth Memo, ChatGPT in particular showed a strong correlation (r ≈ 0.54) between how widely recognized a brand is and how visible it is in answers. This implies that building your product’s overall popularity (through marketing campaigns, user base growth, and general mindshare) will indirectly boost its presence in AI outputs. Growth Memo also reports that popular brands that “invest heavily in their online presence with content, SEO, reviews, social media and digital advertising” tend to dominate chatbot mentions. For example, in categories like project management or CRM, well-known SaaS names (think Salesforce, HubSpot, Jira, etc.) are far more likely to be mentioned by an AI than lesser-known competitors – largely because their footprint in the training data is so much larger.

SEO Strategies to Boost LLM Indexing and Mentions

Optimizing for AI chatbots goes hand-in-hand with optimizing for traditional search – with a few new twists. Recent studies indicate a strong link between organic search performance and LLM visibility. In one study of 10,000 queries, “brands ranking on page 1 of Google showed a strong correlation (~0.65) with LLM mentions”¹⁰. High Google rankings (and to a slightly lesser extent, high Bing rankings) were the best predictors that a brand would be named by ChatGPT or similar. By contrast, classic SEO factors like raw backlink counts or even content length were less influential. The message is clear: if you can rank well for your product’s key topics, you greatly increase the odds an AI will mention you when answering questions on those topics.

Why would Google rank matter to ChatGPT? Likely because the factors that get you ranking – relevance, authority, quality – also make your content more likely to be included in training data or fetched by AI. Additionally, when chatbots use live web lookup (like ChatGPT Search), they heavily pull from top results. Over 87% of ChatGPT’s cited sources in live mode come from Bing’s top results for that query¹¹. Similarly, Google’s SGE (AI snapshots in search) cites content predominantly from the first page of results. So earning top organic positions on both Google and Bing ensures your product content is front-and-center when AI tools scan for answers.

High-ranking pages received 3× more LLM citations on average, and content directly addressing user solutions (“solution-focused”) outperformed generic informational content. Niche industry websites also saw stronger correlations with LLM mentions, meaning authoritative specialization helps.¹²

Here are specific SEO and technical steps to improve LLM visibility:

Target the questions AI is likely to get: Just as you optimize for certain keywords, consider the natural language questions users might ask chatbots. Queries containing the word “best” are a strong trigger for brand mentions in answers¹³. For example, users asking “What is the best [category] software?” will prompt an LLM to list a few product names. Identify these high-intent questions (e.g. “best IT service desk software”, “top data analytics platforms”) and ensure you have content ranking for them, such as “Top 10” comparison posts or authoritative guides on your site or industry blogs. If the AI’s training data includes those listicles or comparisons and your product is featured, you’re likely to be mentioned. Conversely, if you’re absent from all the “best [category]” lists on the web, don’t expect the AI to magically include you. This is essentially AI-era SERP optimization – getting on the webpages that AIs draw from for recommendations¹⁴.
Ensure crawlability and indexation: LLMs ingest content by crawling web pages (via custom crawlers like OpenAI’s GPTBot or by leveraging common search indexes). So, technical SEO fundamentals are critical – if your site isn’t easily crawlable, or if you accidentally block these bots, your content won’t be seen by the AI. Check robots.txt and meta tags to make sure you aren’t disallowing important sections to bots like GPTBot, Bingbot, Googlebot, etc. Also ensure key pages aren’t buried behind login walls or heavy JavaScript. Unlike Google, AI training crawlers don’t run complex JavaScript or parse dynamic content well – they rely mostly on raw HTML text. So provide static, textual versions of content (for example, server-side rendered pages or plain HTML fallbacks) for any critical information about your product.
Diversify across search engines: Because different AI systems lean on different data sources, you should optimize for multiple search indexes. ChatGPT (when augmented with real-time web search) leans on Bing; Perplexity and likely Google’s Gemini lean on Google Search. Aim to rank on both Google and Bing for your target queries. This may mean verifying your site on Bing Webmaster Tools, following Bing SEO guidelines, and even optimizing content for slightly different keyword variants that Bing favors. The effort can pay off by covering all bases. If, say, your product’s docs rank #1 on Bing for a query about “how to implement X”, that could make ChatGPT pick up your solution first, while a top Google rank would influence Gemini to cite your brand.
Focus on authoritative on-page content over link-building gimmicks: Interestingly, researchers found that backlinks and traditional domain authority metrics, while not useless, were not the primary drivers of AI mentions¹⁵. An obscure page with fewer backlinks but extremely relevant, comprehensive content might get cited by an AI if it directly answers the question well. This ties back to creating high-quality, answer-rich content (discussed in the next section). That said, getting mentions from high-DA publishers via PR can still help indirectly – not because the AI is counting links, but because those mentions typically occur in the kind of authoritative content LLMs train on¹⁶. In short: continue earning quality backlinks for human SEO benefits, but for AI, concentrate on content depth and relevance over sheer link volume.
Keep content fresh and updated: LLM training data has cut-off dates. If your product launched new features or improved offerings after the last training cutoff, the AI might not know unless it’s retrieving live info. By publishing timely content about new developments, you increase the chance that retrieval-based bots will surface your latest info. Also, as models get updated with new training rounds, having a strong web presence during that interval means your brand’s new info will be included next time. The goal is to fill any “knowledge gap” about your product with authoritative content so that you become the go-to source for the AI on that subject.

Content Strategies to Get Your Product Indexed (and Cited) by LLMs

What types of content are most likely to be ingested, understood, and regurgitated by LLMs? This is a crucial question for marketers planning content marketing in the age of AI. While traditional SEO content tactics still apply, you should prioritize formats that LLMs find “citation-worthy” and that build your topical authority. Research suggests a few winners when it comes to content that LLMs latch onto:

Original research and data-rich content: LLMs have a built-in bias toward content that provides concrete facts, statistics, and findings. In a analysis of thousands of AI search queries, content with original survey data, industry benchmarks, or unique research was 30–40% more likely to be referenced by AI¹⁷. Numbers and hard facts give the AI something solid to quote. For a B2B SaaS marketer, this means producing data studies, whitepapers, or reports that others might cite. For example, if your cloud software publishes an annual report on server downtime or a survey of IT leaders with compelling stats, an AI might pick up those stats when asked about industry trends. Being the source of a useful statistic (e.g., “85% of CFOs plan to increase SaaS spending next year, according to X company’s 2024 report”) can make your brand a go-to reference in AI-generated answers. Consider incorporating relevant data points into your blog posts and resources – it increases the chance an AI will use your content to support an answer.
Deep, comprehensive guides and documentation: Long-form content that thoroughly answers complex questions often gets picked up by AI. One study noted that an extremely in-depth page (10,000+ words, very high sentence count) on a topic had dozens of citations by ChatGPT, whereas a shorter, less detailed page on the same topic had almost none¹⁸. LLMs aren’t simply looking for length, but depth increases the odds that your content contains the exact info needed to answer a user query. So don’t shy away from long-form content like ultimate guides, technical explainers, or multi-part tutorials, as long as they remain well-structured and readable. Break them into clear sections (with descriptive headings) so the AI can identify relevant parts. For example, a 5,000-word guide on “Enterprise Data Security Best Practices” that covers definitions, strategies, tool recommendations (featuring your product), case studies, and statistics could be an AI goldmine – any section of it might answer a related question, prompting the model to mention your brand as part of the explanation.
Structured product documentation and FAQs: Ensure your product documentation is not only public but also structured for clarity. LLMs excel at digesting technical docs that have logical hierarchy and can pinpoint specific instructions or definitions. For instance, if someone asks a chatbot “How do I integrate [YourProduct] with Salesforce?”, the ideal scenario is that the LLM has seen your integration guide in training. If that guide had clear steps under a heading like “Salesforce Integration”, the model can confidently mention and even summarize it. Well-structured docs (with sections, bullet steps, code snippets, etc.) help the AI extract exactly what it needs. Additionally, public FAQs or knowledge base articles that address common questions can feed into AI responses. If “Does [YourProduct] have a free trial?” is answered on your site, an AI might relay that info if asked. Review your docs and help center content for AI readiness – is all key information accessible without login? Is it written in plain language? These factors will influence whether an LLM can learn from it.
Expert commentary and thought leadership: Including expert quotes or insights in your content can boost its credibility in the eyes of AI. An LLM is more likely to trust and repeat information that is attributed to a subject-matter expert or industry leader¹⁹, like SourceForge. This doesn’t mean the AI knows who the expert is, but the presence of a professional tone and detailed analysis signals high-quality content. For marketers, this means leveraging your internal experts (CTO, product architects) to write blog posts or contribute quotes, and even citing external experts or linking to research. For example, a blog post titled “The Future of Cybersecurity in Finance – 5 Predictions” that features quotes from known security researchers alongside mention of your solution can become a reference the AI pulls when asked about cybersecurity trends.
Use cases and success stories with specifics: Vague marketing fluff won’t stick in an LLM’s memory, but detailed case studies might. User discussion threads that share specific experiences (metrics achieved, problems solved) are often cited by AI. On your own site, publishing case studies that include concrete results (e.g., “XYZ Corp reduced cloud costs by 30% using [YourProduct]”) could make those facts quotable. Moreover, if those stories are picked up by third-party sites or communities, they reinforce your product’s association with certain outcomes. SourceForge offers the ability for software vendors to publish case studies. AI models have also been observed citing forum threads or blog discussions that delve into implementation details. So encouraging users to share success stories publicly (on forums, Reddit, etc.) or contributing your own “lessons learned” posts can create the kind of multi-perspective content AI likes to reference.
Content that aligns with user intent “clusters”: LLMs organize information by semantic relationships, not just keywords. Ensure your content signals the right topical associations. For example, if you market a DevOps tool, it’s wise to publish content that situates your product in the broader DevOps conversation – articles on CI/CD best practices, containerization, case studies in release automation, etc., where your product naturally comes up. This creates topical clusters linking your brand to the key themes. The LLM then sees your product frequently in context with those themes and will be more likely to mention it when the topic arises. Essentially, own your niche online: cover not just your product’s features, but the pains, trends, comparisons, and innovations in your domain. This topical authority signals to the AI that your brand is a core part of the conversation.

Lastly, pay attention to prompt-based nuances. While you can’t control user prompts, research shows certain phrasing in a question makes brand mentions more likely. For instance, prompts containing “trusted source” or “recommend” often lead AI to cite organizations or brands (e.g., “According to the FDA…” or “X is a recommended solution”)²⁰. An AI might or might not name-drop on a generic question, but will almost certainly do so on a “best tools for…” query. Knowing this, your content strategy can ensure that when those prime opportunities occur (e.g. a user asks for the “best” solution), your brand has been seeded in the AI’s training via numerous relevant mentions.

Leveraging Product Documentation and Official Knowledge Sources

In the realm of B2B software, product documentation, knowledge bases, and official references are treasure troves of information that AI models can and will absorb – if you let them. Many technical professionals ask AI assistants for “how do I do X in [Software]?” or troubleshooting advice. You want the AI to have seen your official documentation so it answers accurately and reinforces your product as the solution.

Here’s how to maximize documentation impact:

Open up your docs: If your product docs are gated behind logins or not indexed, consider making at least the core guides public. Developer-focused companies often maintain open docs (e.g., APIs, SDK guides) which end up in places like readthedocs or publicly crawlable sites. This pays dividends when an LLM has digested those pages and can effectively act as a quasi-support agent for your product. Some chatbots, when asked a how-to about a well-documented product, will practically quote the manual. That’s free, scalable support and marketing.
Structure and clarity: Organize docs with clear headers, step-by-step instructions, and contextual examples. As noted earlier, structured technical documentation gets preferential treatment in LLMs. For example, break down a feature release note into sections (“Overview”, “Use Cases”, “How to Enable”, “Example Code”). If a user asks about that feature, the AI can easily pick the relevant section to mention. In contrast, if your release notes are a jumbled paragraph, the model might miss the key point.
Glossaries and definitions: Many LLM queries are definition-type (“What is [YourProduct]?”). Ensure you have a concise, crawler-accessible definition of your product and core features on your site (e.g., an About [YourProduct] page or FAQ). That exact phrasing often gets learned by the model. If your site doesn’t provide a clear definition, the AI might pull one from a less controlled source (like a random blog), which could be inaccurate or outdated.
Consistency across official profiles: LLMs also ingest structured info like Wikipedia (more on that shortly) and knowledge graph data. Make sure your branding and descriptions are consistent across your website, SourceForge listing, LinkedIn, Crunchbase, app marketplaces, etc. An AI will cross-reference facts; if everywhere it finds the same company description and product tagline, it solidifies the association. Consistency in name and context also avoids confusion (you don’t want the AI mixing up your company with something else due to naming similarity).
Knowledge base articles for common questions: Identify the top 20 questions prospects or users ask about your product (e.g., pricing, integration availability, compliance, etc.) and have public articles or pages addressing each. When those questions inevitably get posed to ChatGPT or Claude, the model will be more confident mentioning your product with the correct info. For instance, if “Is [YourProduct] HIPAA compliant?” is answered on your site and that info was seen during training, the AI can answer “Yes, [YourProduct] is HIPAA compliant” and possibly cite your brand as a trusted source.

Building a Wikipedia and Knowledge Graph Presence

One official source worth singling out is Wikipedia. Having a Wikipedia page for your company or product can significantly influence AI responses. Many LLMs were trained on Wikipedia data (which is high-quality and structured). If your product has a well-documented Wikipedia page, an AI is more likely to recall key facts about it (founding date, what it does, notable clients, etc.), and mention it in relevant contexts. For example, if asked about “popular e-commerce platforms”, an AI might explicitly include a product if it remembers a Wikipedia list that includes it.

However, getting a Wikipedia page is non-trivial – it requires notability and strict neutrality. To achieve Wikipedia inclusion, focus on: significant third-party coverage (e.g. press articles, analyst reports), academic citations of your technology (if applicable), and mentions in market reports or industry rankings. SourceForge provides authoritative third-party coverage about software companies and software products. These provide the evidence Wikipedia editors need to justify a page. Work on your digital PR to accumulate a handful of solid citations (news articles on launches or funding, expert mentions in journals, etc.). Only then consider drafting a Wikipedia entry, written in an objective tone and backed by those references. Once a page is up, monitor it for accuracy and neutrality (it’s important it doesn’t read like an ad, or it’ll get removed). The effort is worthwhile: a maintained Wikipedia page acts as a single-source authoritative summary of your product that LLMs will reliably ingest.

Similarly, ensure your company is part of the Google Knowledge Graph. This might happen automatically as you gain mentions and a Wikipedia page, but you can also use schema markup on your site (organization schema), claim your Google business profile (if relevant), and be present on sites like SourceForge, Wikidata, Crunchbase, etc. Knowledge Graph entries often feed into AI answers for factual questions (for instance, “When was [Company] founded?”). While LLMs don’t query the Knowledge Graph per se, the information often overlaps with what’s in training data like Wikipedia. Plus, any tool that uses a hybrid of LLM and search (like Bing Chat) will show knowledge panel info if available.

Community Engagement and Developer Evangelism

Beyond your own content, third-party community content has a huge impact on whether an AI chatbot will mention your product. Remember, LLMs learn from what others say about you, not just what you say. That’s why cultivating an active, positive presence in relevant online communities is key.

Consider the following channels and tactics:

Q&A forums and developer communities: If your target users are developers or IT professionals, having your experts engage on SourceForge, Stack Overflow, Stack Exchange, Reddit, or specialized forums can greatly expand your footprint. When someone from your team (or an advocate) answers a question like “How do I solve X problem?” and mentions how your product can help (in a genuinely helpful, not overtly promotional way), that content might be picked up in training. LLMs often cite Stack Overflow threads or similar when the question is technical. A well-upvoted answer that includes your product as a solution example is the kind of user-generated content that LLMs treat as high-value knowledge. Over time, these community contributions build a web of associations: e.g., many Q&A answers about CI/CD mention your tool, so the AI learns your tool is commonly used for CI/CD.
Developer evangelism and content: Beyond Q&A, invest in engineering blog posts, how-to articles, and conference talks that are shared widely. For instance, a SourceForge article, a Medium article, or Dev.to post by one of your engineers on “10 Tips for Scaling with [YourProduct]” might get lots of reads and links. Not only can this rank in Google (SEO benefit), but if it’s widely shared, it could be deemed important enough to appear in an AI’s training mix. Some companies even create tutorial content on popular community sites (like writing guides on freeCodeCamp, HackerNoon, etc.). These carry your message to new audiences and also become part of the collective knowledge that AI mines.
Webinars, videos, and podcasts (transcripts): Don’t overlook multimedia content – if it’s transcribed or summarized online, it counts. Many companies host webinars or appear on industry podcasts (such as the SourceForge Podcast), then publish the transcript or a recap on their blog. Those transcripts include mentions of your product and key messages, which become text the AI can read. You can also submit talks to YouTube, which now often auto-transcribes content (some models might consume those transcripts). While we don’t fully know how much audio/video content is in LLM training, any textual derivative (like a conference talk write-up) can be picked up. So, participating in virtual panels, talks, and then publishing the learnings is another way to saturate the web with authoritative content about your product.

In all community engagement, the golden rule is authenticity and value. As the BuzzStream team put it, focus on “being human” and providing real help, not just gaming the AI. Attempts to spam or overly manipulate will be evident to users (and possibly filtered out by AI companies who don’t want obviously biased data). Instead, play the long game of thought leadership and helpful participation. The more genuinely useful content about your product exists, the more likely an AI will reflect that positive presence.

Case Studies: B2B Products That AIs Frequently Mention

It helps to look at examples of brands that consistently get named by AI chatbots, as they illustrate what works. Often these are category-leading SaaS products that have invested in broad online presence and become synonymous with their domain.

Project Management Software – e.g., Atlassian Jira: If you ask ChatGPT or Claude about project tracking tools, Jira almost invariably comes up. This is no accident – Jira has extensive documentation, a robust SourceForge listing with user reviews, countless forum mentions (Stack Overflow Q&As), and a Wikipedia page, and is referenced in many “best project management tool” articles. It’s also deeply integrated into developer workflows (lots of GitHub issues mention Jira IDs). All this makes it nearly impossible for an AI not to know about Jira. Meanwhile, a smaller competitor with little web footprint might go unmentioned. Jira’s case shows the power of ubiquity: it’s present in training data via official docs, community content, and general tech discourse.
CRM Platforms – Salesforce & HubSpot: In the CRM domain, Salesforce is a legacy giant frequently cited by AI, and HubSpot, despite being newer, also shows up often. Salesforce benefits from decades of analyst reports, news coverage, and a strong Wikipedia page – high-authority signals that LLMs have ingested. HubSpot’s strength is its content marketing: it runs one of the most trafficked marketing blogs and resource libraries, ensuring that any AI has seen the name “HubSpot” in context of marketing, sales, and CRM topics hundreds of times. Additionally, both companies have thriving communities – Salesforce’s Trailblazer community and HubSpot’s community forums – which generate Q&A content about implementations. They also rank on Google for countless relevant queries (e.g., “how to improve sales pipeline” often surfaces HubSpot content). As a result, ask an AI about improving sales processes or marketing automation, and these brands are top of mind. HubSpot and Salesforce both have comprehensive and content-heavy SourceForge listings, and rank at the top of their categories.
Communication and Collaboration – Slack: Slack is commonly referenced by AI assistants for questions about team collaboration, not just because it’s popular, but due to conscious efforts by Slack’s team to be part of the tech ecosystem. They have an official Slack API documentation site (which is public and widely used by developers), their product is integrated in many other services (yielding mentions in integration guides, e.g., “how to connect X to Slack”), and they’ve gotten lots of media attention. Slack also smartly launched community initiatives (like Slack user groups, and content around remote work) – fodder which shows up in many online articles. All this means an AI answering a question about “tools for remote team chat” will almost certainly say “Slack” among the options, reinforcing Slack’s status as a default solution.
Cloud Platforms – AWS: In the cloud computing space, AWS is so dominant that any AI when asked about cloud providers will list AWS first. The interesting point here is that Amazon itself doesn’t produce a ton of public thought-leadership content compared to others – but the ecosystem does it for them. AWS is mentioned in innumerable blogs, SourceForge listings, tutorials, Stack Overflow answers, open-source projects, etc. Their official docs and FAQs are public and extensive. AWS also appears in academic papers and industry research (e.g., “built on AWS cloud” is in case studies everywhere). So even without Amazon’s direct content marketing, AWS’s massive mindshare and community adoption secures it a constant presence in AI outputs. Competing cloud platforms like Microsoft Azure and Google Cloud also get mentioned due to their own strong presence (and likely because they too are widely written about in the context of comparisons with AWS).

Actionable Steps to Improve Your Product’s AI Mention Rate

Achieving organic mentions in AI chatbot answers won’t happen overnight. It requires a sustained, multi-faceted approach. Here is a step-by-step action plan for product marketers to boost their product’s mention rate over time:

Audit Your Current AI Visibility: First, establish a baseline. Try queries on ChatGPT, Microsoft Copilot, Perplexity, Claude, Grok, etc., that are relevant to your product (e.g. problems your product solves, or “best [category] software”). See if and how your product is mentioned. Use tools like HubSpot’s AI Search Grader (which checks brand visibility across ChatGPT and Perplexity) to quantify your share of voice. Identify which competitors are being mentioned where you are not. This audit will highlight gaps – whether it’s certain use-case queries where you don’t appear, or inaccuracies in how the AI describes your product.
Allow AI Crawlers and Index Your Content: Ensure your website is fully accessible to search engines and AI crawlers. Remove any robots.txt blocks that might prevent GPTBot or common crawlers from indexing key pages. Use Google Search Console and Bing Webmaster Tools to make sure all important pages (product pages, docs, blogs) are indexed. Fix any coverage issues (crawl errors, noindex tags) that could be hindering discoverability. The easier it is for these systems to read your site, the better foundation you have. Tip: Check that your pages load fast and degrade gracefully without JS – some AI scrapers may not wait for long load times or execute scripts. Also, if you have content in PDFs or unusual formats, consider providing an HTML version so it’s definitely read.
Develop High-Value Content (Data, Guides, Thought Leadership): Plan out a content calendar focusing on the types of content that drive AI mentions. This includes at least one original research piece or industry report you can publish (and pitch to press) – something you can update annually for recurring value. It also includes “best practices” guides or tutorials in your niche that are more comprehensive than what’s currently out there. Aim to create the definitive piece of content on each of the top 5 problems your product solves. Back these with data or expert insights. Not only will this help your SEO (perhaps earning featured snippets or top rankings), but these pieces are likely to be referenced by others (increasing the chance they end up in the AI training corpus with mentions of your product). For example, a SaaS project management tool could publish a “State of Project Delivery 2025” report with survey data – which journalists and bloggers might cite (thus feeding the AI new info that [YourProduct] conducted a notable study on project delivery). SourceForge provides third-party publishing for software case studies, B2B software listings and reviews, and an authoritative B2B software podcast that comes with a transcript.
Optimize for “Best” and Comparison Queries: Make sure your product is featured on pages that list top tools in your category. This might mean guest posting an article like “Top 10 Solutions for X” on an industry blog, or working with a review site/analyst to get included in their comparison. If those aren’t feasible, create comparison content on your own site (e.g., “[YourProduct] vs. [Competitor]: An Honest Comparison”) which can rank and be picked up by AI. Many chatbots, when asked to compare products, pull bullet points from comparison pages. Ensure those pages exist and present your product favorably (but factually). Also, explicitly use wording like “best [category] tools” in some of your content where appropriate – as noted, LLMs pick up on that phrasing. Essentially, claim a spot in the “best of” lists through content and partnerships (such as SourceForge), so the AI has no shortage of material associating you with the top of your field.
Pursue Digital PR on High-Authority Sites: A proactive digital PR campaign can amplify your brand on the kind of sites LLMs love. Target a handful of Tier-1 or Tier-2 publications for guest articles, op-eds, or features. This could be a niche industry journal, a tech news site, or a business magazine. The BuzzStream study suggests focusing on high-DA publishers (like SourceForge, with an industry leading DA of 93) increases the odds of being in AI training data²¹. While getting a piece in The New York Times might be far-fetched, aim for respected outlets in your domain. For example, if you’re a cybersecurity B2B, a contributed article on SourceForge or Dark Reading would be valuable; if martech, maybe MarTech Series or AdAge. Focus PR efforts on topics that let you showcase data or unique perspectives (e.g., reporting a trend you’ve observed across your customers – anonymized data insights that journalists can use). Successful PR hits will not only drive human awareness, but also sprinkle your brand name in high-authority texts that the AIs ingest.
Strengthen Wikipedia and Knowledge Panel Entries: If you meet notability guidelines, work towards creating a Wikipedia page for your company/product. Aggregate all the press coverage and references you have – you might engage a neutral Wikipedia editor or consultant to help with this process for objectivity. On your website, add an “In the News” page listing major publications that have mentioned you – this both demonstrates notability and provides easy reference links for Wiki editors. Simultaneously, use structured data (schema.org) on your site to mark up organization info, product info, and sameAs links (to LinkedIn, Wikipedia once available, etc.) to feed search engines. While this step is a longer-term project, it can be pivotal. Once live, monitor your Wikipedia page; as you have new significant coverage, add it (with citations). This becomes a virtuous cycle: the more notable you are, the more Wikipedia and Google recognize you, and the more any new AI model update will include you by default as a known entity.
Engage in Community Q&A Regularly: Set a goal for your team (developers, support engineers, even product managers) to answer a certain number of community questions each week. This could be on Stack Overflow (tagged with topics related to your product’s space), Reddit threads asking for tool recommendations, replying to SourceForge customer reviews of your software, or Quora/Stack Exchange sites. The key is to be genuinely helpful and not just push your product. Perhaps initially don’t mention your product at all – build credibility by solving problems. When appropriate, you can say “You could use [YourProduct] for this part – disclosure, I work there – and here’s how it addresses the issue…” For developer-heavy products, participating in open-source communities or Slack groups is also valuable. This steady trickle of community content will accumulate. Even if any single answer’s impact is small, collectively you are increasing the surfaces where your product is mentioned in problem-solving contexts. These are exactly the contexts an AI might recall when it gets a similar question. Plus, these efforts often generate backlinks or referrals over time, feeding the bigger SEO/Awareness engine.
Encourage User-Generated Content & Case Studies: Your happy customers can be great amplifiers. Encourage them to share reviews and stories on public platforms. This could be as simple as asking for reviews on SourceForge, Slashdot, or Trustpilot – many of those review texts are public and discoverable (and likely used in training data that includes “reference sites” content). Or invite customers to guest blog on your site about their use case (which you both promote). Host community contests or prompt users to share tips on social media. Also, publish more case studies yourself, focusing on specifics. The more third-party mentions of your product in a positive light, the more credible signals exist for AI to pick up. If, for instance, multiple bloggers have written “Why I chose [YourProduct] over the competition,” an AI answering a question about that decision might list your product with reasoning drawn from those posts.
Monitor Mentions and Refine: Use tools to track new mentions of your brand across the web using tools like Google Alerts. This will help you see the impact of your efforts – e.g., after a PR campaign, did you notice more AI-driven traffic or mentions? Manually test relevant prompts every few months to see if the answers evolve to include you more often. When you do see your brand mentioned by an AI, note what source or context it came from (sometimes chatbots cite sources or you can infer from wording). If an AI cites a blog that mentioned you, that’s a clue which content is working. Also note any inaccuracies the AI has about your product, and proactively correct them on your site or content. For example, if ChatGPT says “[YourProduct] pricing starts at $100/month” and that’s wrong, ensure your pricing page is crystal clear (and possibly put out content like “cost of [YourProduct]” to get the correct info out there). Over time, these adjustments will help align the AI’s knowledge with reality.
Stay Updated on LLM Developments: The AI search landscape is fast-changing. New models (like the ones mentioned earlier) will come with new behaviors and sources. Keep an eye on announcements – for instance, if Google’s Gemini is known to use a certain new dataset, see if you can get your content into that. Watch for partnerships (e.g., OpenAI partnership with certain publishers – if your industry publishers cut deals, make sure you’re featured on those sites). Also, adapt to new AI-driven search features – e.g., if Bing or Google allow site owners to submit content to an AI index or opt in via some protocol, be ready to participate. Being an early adopter for these can give you an edge. In short, maintain an AI SEO mindset just as you do for traditional SEO algorithm updates.

By following these steps and treating AI visibility as an extension of your overall marketing strategy, you’ll gradually build an ecosystem of content and references that make your B2B product impossible for AI chatbots to ignore. It requires investing in quality content, genuine engagement, and technical accessibility – the same fundamentals that also drive human trust. The reward is that whenever a prospective customer asks an AI assistant in 2025, “What’s a good solution for this problem?”, your product stands a much better chance of being in the answer. And as AI adoption grows, that could be the difference between catching a lead at consideration stage or being absent from the conversation entirely.

Tags: Artificial Intelligence, LLM