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[Home ](/) 2. [Blog ](/blog) 3. How To Get Saas Mentioned Chatgpt GEO Guide # How To Get Your SaaS Product Mentioned in ChatGPT The authority-first framework we use to get clients cited by AI. No hacks. No shortcuts. Just what actually works. By Yuval Halevi  |  Growtika  |  March 2026  |  18 min read More than half your buyers ask ChatGPT for software recommendations before they hit Google. From 50+ SaaS GEO campaigns: most products that own page one of Google are completely invisible when someone asks ChatGPT the same question. Different game. Different winner. I started testing why. Days, nights. Dozens of accounts across industries. Patterns emerged and held across verticals. What I found broke assumptions I'd carried from a decade in B2B SEO. The framework: authority-first, 70/30 consensus, the LLM Sitemap. ### TL;DR - ChatGPT doesn't index the web in real time. It pulls from its training data, where authority signals and multi-source consensus determine who gets mentioned. - Traditional SEO metrics (domain authority, backlink count) correlate weakly with AI citations. Source quality and content format matter more. - The authority-first approach outperforms the content-first approach. You need Trust Hub presence before content optimization pays off. - Data tables, structured definitions, and sub-15-word bullet points get extracted by AI at significantly higher rates than narrative paragraphs. - The 70/30 consensus rule: 70% of your mentions should be factual, third-party citations across independent sources. 30% can be self-published content. - Most SaaS products see first citations within 60-90 days when the framework is applied correctly. Consistent, broad citation usually takes 4-6 months. What's In This Guide [ 1 #### Why ChatGPT Ignores You ](#why-invisible)[ 2 #### How ChatGPT Decides ](#how-chatgpt-decides)[ 3 #### Authority-First Framework ](#authority-first)[ 4 #### Content That Gets Cited ](#content-formats)[ 5 #### The LLM Sitemap ](#llm-sitemap)[ 6 #### Building Consensus ](#consensus)[ 7 #### Step-by-Step Playbook ](#step-by-step)[ 8 #### Tracking AI Visibility ](#tracking)[ 9 #### What Didn't Work ](#what-failed) ## 1\. Why ChatGPT Ignores Most SaaS Products ChatGPT pulls from a knowledge base built during training, weighted by credibility signals. Sources that were consistent and independently corroborated got baked in. Everything else got ignored. You could have 10,000 backlinks and a DA of 80 and still be invisible if your product didn't appear in the right sources before the training cutoff. When browsing is enabled, ChatGPT runs live retrieval passes. Building presence now feeds the next update cycle. The second reason companies get ignored: category ambiguity. If your positioning is vague (`the all-in-one platform for teams`), the model has no anchor for when to mention you. This is especially common in [product-led growth companies](/blog/plg-ai-blind-spot) where the product speaks for itself in demos but says nothing to an AI model. ### The Three Failure Modes From auditing 50+ companies asking "why isn't ChatGPT mentioning us," the problem falls into four buckets. The fourth gets missed most, because the company sees a citation and assumes it's working. Failure Mode What It Looks Like How Common Fixable In No authority footprint Product barely mentioned outside owned channels. No third-party reviews, no press, no community references. Very common 90-120 days Wrong content format Good coverage, but all narrative prose. No structured data, no comparison tables, no defined FAQ content. Common 30-60 days Category mismatch Product is described differently across sources. ChatGPT can't build consistent understanding of what it does. Less common 60-90 days Bad framing Product appears in ChatGPT but is described inaccurately, negatively, or in a context that hurts rather than helps. A mention like "used by teams that can't afford \[competitor\]" is a citation. It's not a win. Underestimated 90-120 days Most companies have all three. Fix them in order. Authority first, then format, then consistency. ## 2\. How ChatGPT Actually Decides What to Mention After tracking citation patterns across 50+ client campaigns and hundreds of test prompts, the signals that move citations became predictable. We mapped six. The Citation Probability Stack: What Actually Moves the Needle Trust Hub and consistent positioning matter most. Structured content affects how accurately ChatGPT describes you, not whether it mentions you. Editorial coverage only helps after the foundation exists. Warning Fake reviews on G2 or Capterra will hurt you. ChatGPT detects consensus built on thin, templated reviews. It favors fewer detailed reviews that show genuine product knowledge over many generic ones. ### The Comparison Context Signal When ChatGPT retrieves information to answer `best X for Y` or `compare A vs B vs C`, it relies heavily on pages that already structure the comparison. If your product appears in three independent comparison articles across different domains, the model has enough signal to confidently include you in its answer. If your [competitors are already getting cited](/blog/competitors-cited-ai) in these comparisons and you're not, that gap compounds fast. If you only appear in your own `us vs. them` page, the model discounts it as promotional. Third-party comparisons carry disproportionate weight. ## 3\. The Authority-First Framework The biggest insight from 50+ campaigns: the order matters more than the tactics. Content optimization before authority build-out wastes money. Authority before content lets the content work. Understanding [how ChatGPT evaluates your brand authority](/blog/how-chatgpt-rates-your-brand-authority) is the first step. Authority-First vs Content-First: The A/B Test ### What "Trust Hub" Means — and What the Data Actually Shows Trust Hub is the cluster of third-party platforms LLMs lean on when building knowledge about your product. There is no universal list. The sources that dominate for a [cybersecurity](/sectors/cybersecurity) tool differ from those for [fintech](/sectors/fintech), Trust Hub is the cluster of third-party platforms LLMs lean on when building knowledge about your product. There is no universal list. The sources that dominate for a [cybersecurity](/sectors/cybersecurity) tool differ from those for [fintech](/sectors/fintech), [developer tools](/services/developer-marketing), or logistics SaaS. It also shifts over time as LLMs update and new sources accumulate authority in your space. , or logistics SaaS. It also shifts over time as LLMs update and new sources accumulate authority in your space. Most GEO advice publishes a generic platform list and calls it done. Your Trust Hub is specific to your niche, your buyer, and the query patterns they use. Identifying it rather than assuming it is step one. How to Find Your Trust Hub Run 30+ queries across all three types: `best [category] for [buyer]`, `[product] vs [competitor A] vs [competitor B]`, `what is [category]`. Log every cited domain. Sort by frequency. Top 10 = your Trust Hub. Missing from 7+ of them = your gap list. The data below covers B2B SaaS broadly — a useful baseline, not a blueprint. Your niche will surface vertical publications, analyst sites, and community forums that won't appear here. In March 2026, we ran 150 queries across three query types through ChatGPT and logged every cited domain. ### Test 1: Best-Of Queries (50 queries) Queries like `best SIEM for startups`, `best CRM for small business`, `best DevOps monitoring tool` \*150 queries run via ChatGPT GPT-4.1, March 2026. 275 unique domains, 364 total citations.\* Rank Domain Citations Coverage % Type 1 wikipedia.org 23 6.3% Encyclopedia 2 techradar.com 14 3.8% Editorial tech media 3 peerspot.com 6 1.6% B2B peer review 3 reddit.com 6 1.6% Community 5 gartner.com 4 1.1% Analyst 6 g2.com 3 0.8% Review platform 7 medium.com 3 0.8% Publishing platform 8 capterra.com 1 0.3% Review platform ### Test 2: Comparison Queries (50 queries) Queries like `Notion vs Asana vs Monday.com`, `Datadog vs New Relic vs Dynatrace` \*284 unique domains, 359 total citations.\* Rank Domain Citations Coverage % Type 1 reddit.com 17 4.7% Community 2 peerspot.com 8 2.2% B2B peer review 3 forbes.com 6 1.7% Business media 4 medium.com 5 1.4% Publishing platform 5 wikipedia.org 5 1.4% Encyclopedia 6 techradar.com 3 0.8% Editorial tech media 7 g2.com 1 0.3% Review platform 7 capterra.com 1 0.3% Review platform ### Test 3: Definition Queries (50 queries) Queries like `what is SOAR in cybersecurity`, `what is product-led growth`, `what is zero trust security` \*207 unique domains, 306 total citations.\* Rank Domain Citations Coverage % Type 1 wikipedia.org 27 8.8% Encyclopedia 2 ibm.com 12 3.9% Vendor documentation 3 techtarget.com 10 3.3% Editorial tech media 4 atlassian.com 8 2.6% Vendor documentation 4 salesforce.com 8 2.6% Vendor documentation 6 microsoft.com 5 1.6% Vendor documentation 6 google.com 5 1.6% Vendor documentation 8 reddit.com 4 1.3% Community What This Data Actually Shows These results cover broad B2B SaaS queries. In your niche, the ranking will look different. A cybersecurity product's top cited sources won't match a fintech product's. The point isn't that G2 is useless — it's that no generic list tells you where ChatGPT pulls citations for your specific category. These tables are a starting benchmark. Your actual Trust Hub requires running queries in your own niche and logging what appears. See those tables? Your competitors might already be on them. We'll run this exact analysis for your category and show you where you stand — and what it takes to get cited. [Book a Call →](/contact) ### What the Data Actually Means for Your Strategy Source priority is query-type dependent. There is no single Trust Hub list. There are three, and they're different enough that collapsing them into one wastes resources. \*Updated: March 2026\* Query Type Dominant Sources What to Build Definition ("what is X") Wikipedia, IBM/vendor docs, TechTarget, Atlassian Wikipedia entry + authoritative definition pages on your own domain Comparison ("X vs Y vs Z") Reddit, PeerSpot, Forbes, Medium Community presence + get into third-party comparison articles Best-of ("best X for Y") Wikipedia, TechRadar, PeerSpot, Reddit Editorial coverage + PeerSpot profile + Reddit organic mentions The fragmentation finding: across all 150 queries, the average domain appeared 1.2 times. No single dominant source except Wikipedia. The goal isn't to dominate one platform — it's to appear across enough independent sources to hit whichever ones ChatGPT pulls for a given query type. And the table above won't show your niche-specific sources. A cybersecurity product's Trust Hub likely includes Dark Reading, Bleeping Computer, SecurityWeek. A developer tool's includes dev.to and Stack Overflow. Run your own queries. Find the 3-5 domain names specific to your category that no generic list will surface. Those are the ones to prioritize. \*Updated: March 2026 — corrected from prior version based on live query data\* Platform Why It Matters for AI Priority Best For Wikipedia Top cited source across two of three query types. 27 citations on definition queries alone. Critical Definition + best-of queries Reddit Leads comparison queries at 17 citations. Treated as ground truth for practitioner opinion. Critical Comparison + best-of queries PeerSpot Rank 3 in best-of, rank 2 in comparison queries. Consistently outperforms G2 and Capterra. Almost no one talks about it for GEO. Critical Best-of + comparison queries TechRadar / TechTarget Editorial tech media. TechRadar gets 14 citations on best-of queries, TechTarget gets 10 on definition queries. Critical Best-of + definition queries Hacker News Tech-heavy training data. Show HN, product discussions, and comments all contribute for developer tools. High (tech) Developer tool categories Forbes / Medium Business and community publishing. Forbes gets 6 citations on comparison queries. Medium gets 5. High Comparison queries G2 3 citations across 50 best-of queries. Still worth claiming for product legitimacy signals, but not the citation driver we assumed. Medium Product legitimacy signal Capterra / GetApp 1 citation across 100 queries. Low direct citation value. Still worth maintaining for completeness. Low Completeness only The Bottom Line There is no universal Trust Hub. These tables are a cross-niche baseline — useful for orientation, not for copying. The sources that get cited in your category are specific to your niche, your query types, and the sources that have accumulated authority there. Find yours by running your own queries. Then close the gaps you find, not the ones someone else found. ## 4\. Content Formats That Actually Get Cited We track citations across ChatGPT, Claude, and Perplexity for every client. Format patterns repeat consistently. LLMs are pattern-completion machines. Scannable content is easier to complete than dense narrative. For a broader look at what works, see our guide on [content strategies that actually get cited by AI](/blog/content-strategies-ai-age). 01 #### Data Tables Quoted almost verbatim. Numbers and comparison columns get extracted directly. The model doesn't need to paraphrase what's already scannable. 02 #### Structured Definitions "\[Product\] is a \[category\] that \[specific function\]. Unlike \[alternative\], it \[differentiator\]." That sentence structure gets lifted whole. 03 #### Short Bullet Points Under 15 words per bullet. Longer bullets get paraphrased, and paraphrasing loses your specific language every time. 04 #### FAQ Schema Every marked-up Q&A pair is a candidate for direct citation. Sales-sourced questions match real ChatGPT queries better than marketing-drafted ones. 05 #### Outcome Statistics "Reduces alert fatigue by 40%" gets cited. "Significantly reduces alert fatigue" disappears. Specificity is what the model locks onto. 06 #### Comparison Summaries "Best for teams who need X. Not ideal for Y." Decision-guidance framing gets cited constantly because it matches how buyers prompt. Narrative prose gets paraphrased. When paraphrased, your specific differentiators disappear. Structured content gets extracted directly. Rewrite key product descriptions as short, parallel, scannable statements. Low extraction rate — narrative prose "Our platform offers a comprehensive solution for security teams looking to streamline their workflows. With powerful integrations and an intuitive interface, teams can significantly improve their response times and reduce operational overhead." High extraction rate — structured definition "\[Product Name\] is a cloud-native SOAR platform for security teams under 10 analysts. Key capabilities: - Automates 85% of Tier 1 alert triage - Integrates with 200+ security tools out of the box - Mean time to respond: 4 minutes vs. 45 min industry average - No-code playbook builder, deploys in under 2 hours" Pro Tip Prioritize your hero section, "What is \[Product\]" page, and top comparison pages. One structured product definition page drives more citations than a dozen blog posts. ### The FAQ Schema Multiplier Every FAQ schema question is a candidate for a direct ChatGPT answer. Write schema for the questions your sales team hears most. Sales-sourced questions match actual ChatGPT buyer queries more closely, almost every time. ### Content Freshness Pages updated within 30 days show higher citation rates in Perplexity and ChatGPT with browsing enabled. Schedule quarterly updates to priority pages. Update the `dateModified` schema tag, refresh statistics, add examples. The model treats recency as a proxy for reliability. ### Why These Formats Work at the Model Level LLMs were trained on text where structured content preceded reliable information: Wikipedia tables, Q&A answers, technical docs. The model learned that scannable structure signals trustworthiness. Marketing prose pattern-matches to content it learned to paraphrase: press releases, ad copy, corporate blogs. ## 5\. The LLM Sitemap A standard XML sitemap tells Google where your pages are. It says nothing useful to a language model. The [LLM Sitemap](/blog/llm-sitemap) fills that gap: a structured HTML document at /llm-sitemap that gives AI systems a single source of truth about your product. The problem it solves: clients with good content still got described inaccurately because their site conflicted with itself. Homepage said one thing. Pricing page said another. The LLM Sitemap resolves that conflict. LLM Sitemap Architecture Once indexed, citation consistency improves within 30-45 days. ### The llms.txt Standard A plain text file at yourdomain.com/llms.txt listing your key pages, product context, and AI crawler permissions. Under an hour to build. Do it alongside the LLM Sitemap. For a deeper walkthrough of the full technical stack, see our [LLM visibility guide](/blog/llm-visibility). ## 6\. Building Consensus: The 70/30 Rule ChatGPT trusts consensus. One blog post doesn't move it. Consistent, independent mentions across multiple trusted sources does. The 70/30 ratio came from mapping citation patterns across our client base. Products cited consistently had roughly 70% of web mentions from sources unaffiliated with the company. Below that threshold: hedged or ignored, even with good content. Note To be clear: 70/30 is not a literal threshold coded into ChatGPT or any LLM. It's an observed benchmark from our client data that reflects a broader truth: AI models require external corroboration to treat an entity as authoritative. The exact ratio matters less than the principle. If independent sources aren't talking about you, AI won't either. Mention Type Target % Examples Why It Works Independent third-party 70% Review sites, editorial coverage, community threads, comparison articles by independent authors LLMs weight unaffiliated sources heavily. Hard to fake at scale. Signals organic adoption. Self-published 30% Your blog, case studies, press releases, product documentation, LLM Sitemap Sets your preferred framing. Provides the structured definitions you want cited. Fills gaps. Flip the ratio and LLMs treat you like a PR campaign. 90% self-published signals no one else is talking about you. ### Where Third-Party Mentions Come From - **Analyst outreach.** Tier 2-3 analysts write comparison content constantly. Their format is already structured. Briefing them on your AI-relevant differentiators pays off disproportionately. - **Community seeding.** Answer questions on [Reddit](/blog/reddit-ai-visibility) and [Quora](/blog/quora-marketing-guide). Not every answer mentions your product. Build helpfulness first. Mentions that come later carry more weight. - **Partner co-creation.** Integration partners who mention you are independent by definition and naturally relevant. - **Journalist relationships.** One article in a respected trade publication does more for AI citations than 20 blog posts. Pitch category explainers, not funding announcements. Warning Low-quality content farm placements won't help and may harm your SEO. ChatGPT weights source quality and independence, not raw mention count. Five credible mentions outperform 500 spam-site mentions. ## 7\. The Step-by-Step Playbook The exact sequence we use for new client onboarding. If Trust Hub presence already exists, compress Steps 1-3. For the full tactical version, see our [GEO playbook](/blog/geo-playbook). Interactive Playbook ### The Step-by-Step Playbook Track your progress. Check off steps as you complete them. 0% Foundation 0/3 Audit Your AI Visibility Ask ChatGPT 15-20 buyer questions. Log who appears and how. 2-4 hours Lock Category Positioning Write a 2-3 sentence Wikipedia-style product definition. Half day Claim Trust Hub Profiles PeerSpot first, then Reddit, G2, and Wikipedia. 2-3 weeks Infrastructure 0/2 Build LLM Sitemap + llms.txt Create /llm-sitemap with all six components. 3-5 days Restructure Key Content Convert narrative prose to structured bullets, tables, FAQ schema. 1-2 weeks Growth 0/2 Seed Community Presence 30 min/day in relevant subreddits and Quora topics. Ongoing (30 min/day) Execute Third-Party Placements One high-quality independent placement per week. Ongoing Expected Timeline Week 1-2 Foundation Set Week 3-6 Infrastructure Built Month 2-3 First Citations Month 4-6 Consistent Pattern Month 6-12 Model Memory From Our Work One [cybersecurity](/sectors/cybersecurity) client first appeared in ChatGPT at month 3, but only with browsing enabled. By month 9, users reported citations even in offline sessions. That's model memory. You cannot shortcut to it. Your competitors are getting mentioned. You're not. Every day ChatGPT recommends someone else in your category, you lose deals you never even knew existed. We fix that. [Book a Call →](/contact) ## 8\. How to Track Your AI Visibility AI citation tracking has no native dashboard. No rank tracker, no API, no Search Console equivalent. We use a structured prompt monitoring system run manually. For a complete diagnostic framework, see our [51-point AI visibility checklist](/blog/seo-ai-search-checklist). ### The Prompt Monitoring System Build a bank of 20-30 prompts your buyers actually use. Three types: - **Category queries:** `What is the best [category] for [use case]?` - **Problem-framed queries:** `I'm a [persona] trying to solve [problem]. What tools should I consider?` - **Comparison queries:** `Compare [your product] vs [competitor A] vs [competitor B]` Run the full bank weekly. Log: did you appear, what position, how were you described, which competitors showed up. The 90-day trend is more useful than any single week. Metric What It Tells You Track Weekly Target (90-day) Citation rate % of relevant prompts where you appear Yes 30-50% of category prompts Citation position Are you first, second, or buried? Yes Top 3 in primary category Sentiment When you appear, how are you framed? Positive, neutral, or negative? Log the exact descriptor the model uses each time. Yes Positive or neutral in >85% of mentions Competitor share of voice How often do competitors appear on the same prompts? Your 40% citation rate means nothing if the main competitor is at 75%. Bi-weekly Gap narrowing over 90 days Description accuracy Does ChatGPT describe you correctly? Bi-weekly Canonical definition used >70% Competitor co-occurrence Are you appearing alongside the right competitors? Monthly Named in relevant competitive set Cross-platform parity Appearance rate on Claude and Perplexity vs. ChatGPT Monthly Trending upward on all three ### The No-Click Reality ChatGPT citations rarely drive referral traffic. Users get the answer and move on. What they drive is pre-formed perception. A buyer who's seen your product across multiple prompts arrives at your site already convinced. They show up as direct or branded search. The "how did you hear about us" field tells you more than utm data here. ### The One Revenue Signal That Matters Add "AI assistant (ChatGPT, Claude, etc.)" to your demo form's "How did you hear about us" field. When it appears even once a week, you're generating real pipeline. Track it from day one. Pro Tip ChatGPT appends `utm_source=chatgpt.com` to links it shares with users. Check your analytics. Perplexity uses `utm_source=perplexity.ai`. Set up GA4 segments for both from day one. ## 9\. What We Tried That Didn't Work GEO has a lot of recycled advice that sounds right but doesn't hold up under testing. These are the approaches that either moved nothing or actively hurt citation rates. - **Guest posts alone.** Six months of guest posting on mid-tier blogs with no Trust Hub build-out. Zero citation improvement. Without pre-existing authority, there was nothing to amplify. - **Content-first without authority.** The A/B test in Section 3 made this undeniable. Structured, optimized content does almost nothing before the authority foundation exists. We cover this pattern in depth in our [AI visibility best practices guide](/blog/ai-visibility-b2b-saas-best-practices). - **Press releases for citation signals.** Press release syndication shows up in training data as low-credibility content. No citation lift, even when picked up by 40+ outlets. - **Mass low-DA link building.** 500 links from DA 5-15 sites moved Google rankings weakly and AI citations not at all. LLMs don't treat link volume as an authority proxy the way Google does. - **Updating content without updating `dateModified` schema.** Freshness only works if the recency signal is exposed in structured data. Rewriting a page without touching the schema tag gets you the work without the credit. The Bottom Line Citations come from being credible in the sources LLMs trust, described consistently in a format they can extract, with enough independent confirmation to feel safe recommending you. That's a 90-day program. Companies that treat it like infrastructure, not a campaign, end up owning their category. ### ChatGPT Recommends Your Competitors. Let's Change That. You just read the playbook. Now imagine having a team that's done this for 50+ SaaS companies executing it for you — while you focus on building product. [Book a Call With Our Team →](/contact) * * * ## Frequently Asked Questions #### How long until ChatGPT starts mentioning my product? From what we've seen across 50+ client campaigns: - **60-90 days:** First citations appear on niche, specific prompts - **4-6 months:** Consistent appearance on broad category queries - **6-12 months:** Model memory, cited even when browsing is off Timeline compresses if you already have Trust Hub presence. It extends in highly competitive categories with entrenched incumbents. Important nuance: these timelines apply primarily to base model memory (updated only when a new model is trained). When ChatGPT uses live browsing to answer a prompt, your optimized content can appear as soon as those pages are indexed by search engines. RAG-based answers can surface much faster than 60 days. #### Does my SEO ranking affect ChatGPT citations? Weakly and indirectly. Higher rankings mean content gets crawled more, which helps at the margin. But we've seen DA 20 products outrank DA 80 competitors in ChatGPT because of stronger Trust Hub presence. SEO metrics and AI visibility aren't proxies for each other. #### Should I optimize for ChatGPT specifically, or all AI platforms? Optimize for the framework. Trust Hub presence, structured content, and multi-source consensus drive citations across ChatGPT, Claude, and Perplexity. One strategy, three platforms. If you want the fastest feedback loop, use Perplexity. Its real-time retrieval responds to new content faster than base-model ChatGPT. #### What if ChatGPT is describing my product inaccurately? Category mismatch or consistency problem. The fix is systematic: - Lock a canonical definition and deploy it everywhere simultaneously - Update G2, Capterra, and review profiles to match exactly - Publish your LLM Sitemap to give AI systems a single authoritative source - Contact authors of major third-party articles to correct inaccuracies Expect 60-90 days before corrected framing propagates. #### Can I pay to get mentioned in ChatGPT? Not through organic citations. There's no way to pay for ChatGPT to recommend your product in its answers. Paid tactics like buying fake reviews, link schemes, or content farm mentions don't work for AI citations. However, OpenAI has begun [testing ads in ChatGPT](https://help.openai.com/en/articles/20001047-ads-in-chatgpt) (US only, as of February 2026). Key details: - Ads appear only for Free and Go plan users, not Plus, Pro, Business, Enterprise, or Edu - Ads are clearly labeled and do **not** influence ChatGPT's answers or recommendations - They're contextual display ads, not pay-to-be-cited placements So while paid visibility exists in a limited form, it's entirely separate from organic RAG citations. Ads are contextual display placements. They do not influence what ChatGPT recommends in its generated answers. The only way to get ChatGPT to _recommend_ your product is through authority, consistency, and structured content. ![Yuval Halevi](/images/yuval-halevi.webp) #### [Yuval Halevi](https://www.linkedin.com/in/yuvalhalevi/) Helping SaaS companies and developer tools get cited in AI answers since before it was called "GEO." 10+ years in B2B SEO, 50+ cybersecurity and SaaS tools clients. ### Related Articles [AI Visibility #### ChatGPT SEO: 10 Best Practices to Get Cited by ChatGPT Field-tested practices for appearing in ChatGPT answers. ](/blog/chatgpt-seo-best-practices)[AI Visibility #### LLM Visibility: The Definitive Guide to Getting Cited by AI The complete framework for appearing in ChatGPT, Claude, Perplexity, and Gemini answers. ](/blog/llm-visibility)[GEO Strategy #### The GEO Playbook: What AI Visibility Requires That Traditional SEO Doesn't The tactical playbook for getting cited in ChatGPT, Perplexity, and Claude. ](/blog/geo-playbook) #### Company - [About Us](/about) - [Blog](/blog) - [GEO HubNew ](/geo-hub) - [Contact](/contact) - [Free ToolsNew ](/tools) - [Robots.txt Checker](/tools/robots-txt-checker) - [Robots.txt Generator](/tools/robots-txt-generator) #### Solutions & Use Cases - [GEO Marketing](/services/geo-marketing) - [AI Search SEO](/services/ai-search-seo) - [ChatGPT SEO](/services/chatgpt-seo) - [Perplexity SEO](/services/perplexity-seo) - [SaaS Not on ChatGPT](/use-cases/saas-not-on-chatgpt) - [Competitors Cited](/use-cases/competitors-ai-citations) - [AI Visibility Audit](/use-cases/ai-visibility-audit) - [Entity Optimization](/use-cases/entity-optimization) #### Sectors - [Tech](/sectors/tech) - [Cybersecurity](/sectors/cybersecurity) - [Fintech](/sectors/fintech) - [AI](/sectors/ai) #### Top Content - [New Guide ChatGPT Search Optimization Guide ](/guides/chatgpt-search-optimization-guide) - [All Guides](/guides) - [Black Hat 2026 Field Guide](/black-hat-2026) - [LLM Visibility Guide](/blog/llm-visibility) - [SEO After LLMs](/blog/seo-after-llms) - [ChatGPT Recommends Competitors](/geo-hub/chatgpt-recommends-competitor) - [Breaking SEO Best Practices](/blog/breaking-seo-best-practices) #### Earn a Link the Hard Way A game in a footer? 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