Ty Smith
CEO & Founder
Ty Smith is the Founder and CEO of Clickstrike, the marketing agency built for AI companies. He has helped 750+ AI and tech companies grow through influencer marketing, PR, paid media, and SEO - driving over 75M views and 8,250+ media placements for clients. His insights on AI and SaaS marketing have been featured in Forbes, HubSpot, and beyond. Outside of work, Ty loves to vibe code, explore new AI tools, and build tools and processes that leverage AI. When he’s not at his desk, you’ll find him surfing or hanging out with his dog, Benny.
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Posts by Ty Smith
X Just Published the Scoring Weights Behind Its Algorithm (Here's What Actually Drives Reach)
A like is worth 0.5. Someone quietly copying your post and sending it to one other person is worth 20. Those are not estimates. They are the default values sitting in a file called param.rs in X's public repository, and until August 13, 2026, nobody outside the company had seen them. This matters more than the last round of algorithm coverage suggested, and less than the "go viral with this one trick" posts are claiming. Here is what actually shipped, what the numbers say, and what three of the most widely repeated claims about them get wrong. What Actually Shipped in August 2026 First, a correction that most coverage this week got wrong: X did not just open source its algorithm. That happened on January 20, 2026, when xAI published the x-algorithm repository, a complete rewrite of the 2023 Scala system in Rust and Python. What January gave the public was the architecture. You could see the pipeline, read the retrieval logic, and identify which user actions the ranking model predicts. What you could not see was what any of those actions were worth. The August 13 release added three things: The scoring weights. The actual numeric values used to blend predicted actions into a single score for each post. The visibility filtering system. The code that decides whether a post is allowed to appear at all, which is a separate system from ranking. A label transparency tool called Under the Hood. It surfaces aggregate statistics about the visibility-limiting labels applied to your own account and posts, piloting with accounts at least a year old. The distinction between architecture and weights is the whole story. Knowing that a system predicts the probability of a reply tells you very little. Knowing that a reply is weighted at 5.0 while a like is weighted at 0.5 tells you that a decade of engagement advice was aimed at the wrong action. How the For You Feed Assembles a Timeline Before the numbers are useful, the pipeline needs one paragraph of context. Posts arrive from two sources. In-network candidates come from thunder, which holds recent posts from accounts you follow in memory. Out-of-network candidates come from phoenix retrieval and simclusters, which surface posts from accounts you do not follow. Both sets are ranked by the same model. Phoenix reads the viewer's recent engagement history and predicts, for each candidate post, the probability that this specific viewer takes each possible action. A scorer multiplies each probability by a fixed weight and sums them. Three adjustments follow. Then the list is sorted and cut. Separately, and only after ranking is finished, a different service decides whether each post can be displayed at all. That separation between ranking and visibility is the single most important structural fact in the repository, and we will come back to it. The Scoring Weights These are the default values in the published code. X notes that live production values can differ during experiments, but these are the real starting points. Positive actions: Share via copy link: 20.0 - the highest weighted action in the entire system Reply: 5.0 - rising to 20.0 on original posts from a mutual follow Quote: 5.0 Share via DM: 5.0 Follow author: 4.0 Share: 2.0 Repost: 1.0 Favorite (like): 0.5 Post click: 0.4 Open link: 0.2 Photo expand, video open, video quality view, quoted post click: 0.05 each Post unexplored: 0.02, applied to in-network posts only by default Continuous dwell time: 0.004 Dwell and profile click: 0.0, currently switched off Negative actions: Report: -234.0 Mute author: -58.8 Not interested: -43.2 Block author: -31.2 Not dwelled: -0.02 The Caveat Almost Every Writeup Is Missing There is a comment sitting directly above these values in the source explaining that the weights encode two things at once: how much an action is valued in ranking, and how often that action typically occurs across the network. Negative feedback is rare. So a report is not weighted at -234 because one report is 468 times worse than one like. It is weighted that way because the predicted probability of a report is normally close to zero, and the coefficient has to be large for a tiny probability to influence the final score at all. The same logic applies in reverse to copy-link shares. These are scaling decisions calibrated against base rates, not a moral hierarchy of user actions. Anyone presenting the negative weights as a punishment ranking has misread the file. What the Weights Reward Forwards Beat Likes by 40x The highest value signal X can observe is one person copying a post and sending it to one specific other person. Not broad reach. Not a viral spike. One forward. Writing for likes and writing for forwards are different jobs. A post optimized for likes seeks agreement from a crowd, which favors the broadly agreeable and the emotionally simple. A post optimized for forwards has to be useful enough that a reader thinks of a specific colleague while reading it. For AI companies, this is unusually good news. A benchmark result, a well-explained architectural tradeoff, or an honest cost breakdown of running inference at scale is exactly the kind of content one engineer sends to another. It is also the kind of content that historically underperformed on like counts, which made it look like a failure under the old scoreboard. Replies Are Worth 10x Likes At 5.0 against 0.5, a post that earns a considered reply is worth an order of magnitude more than a post that earns a passive tap. This is not an argument for engagement bait. Low-effort questions invite the "not interested" signal, weighted at -43.2, from viewers who recognize the pattern. It is an argument for leaving something genuinely arguable in the post. A specific claim someone can dispute with their own experience will outperform a safe observation everyone nods at. Mutual Follows Are a Structural Multiplier Buried in the scorer is a condition requiring three things simultaneously: the post is not a reply, it is not a repost, and the author is a mutual follow of the viewer. When all three hold, the reply weight for that viewer jumps from 5.0 to 20.0. Original posts from mutual follows are the single most favored content type in the ranking system. For a founder building an audience, this reframes following back from a courtesy into a compounding mechanic. It also means the boost never applies to replies or reposts, only to original posts. Your Replies and Reposts Are Discounted Even to Your Own Followers Out-of-network posts get multiplied by 0.75, which most coverage noted. What almost nobody caught is a flag called EnableOonRescoreForInNetworkRepliesRetweets, which defaults to true and applies that same 0.75 discount to replies and reposts from accounts the viewer already follows. Your own replies and reposts are structurally discounted relative to your own original posts, even when shown to people who chose to follow you. Commenting on other accounts builds relationships and puts you in front of new audiences. It will not do what publishing something of your own does. Converting a Stranger Into a Follower Is Worth 8 Likes Follow-author sits at 4.0. A post that makes someone want the next one is worth eight times a post that gets tapped and forgotten. For AI founders, this favors serialized thinking over one-off takes. If a post reads like an excerpt from an ongoing body of work rather than an isolated observation, it earns the action the system pays the most attention to after forwards and replies. Volume Decays, It Does Not Cap Author diversity applies a multiplier calculated as (1 - floor) * decay^k + floor, with a decay of 0.5 and a floor of 0.25, where k is the rank of that post among your own posts in the same timeline slate. In practice: your first post scores at full value, the second at roughly 0.625, the third at 0.44, and everything after that trends toward a floor of 0.25. Posting more is not free. It is also not fatal. A fourth post still carries a quarter of its score, which is meaningfully different from being suppressed. A Post Has 48 Hours and Then It Is Gone AgeFilter removes anything older than 48 hours before scoring even runs. This is a hard cutoff, not a decay curve. There is no long tail on X. A post gets two days to exist and then it stops being a candidate entirely. For coordinated launches, this changes the shape of a campaign. Creator content published across a two week window produces no cumulative timeline effect. The same content concentrated into a 48 hour window competes as a single unified moment. Three Things Everyone Is Getting Wrong 1. The Link Penalty Is Not in the Code The most repeated claim about X ranking is that external links take a 30 to 50 percent reach penalty. That penalty does not exist in this codebase. The relevant parameter is OpenLinkWeight and its value is positive, at 0.2. The only URL-related suppression in the repository is a drop rule for URLs flagged as malicious, and it applies only to out-of-network distribution. Links genuinely do underperform, but the mechanism is different and the difference matters. A link click contributes 0.2 while a copy-link share contributes 20 and a reply contributes 5. Clicking out also ends engagement with the post. You are trading a high value action for a cheap one, not absorbing a punishment. Putting the link in a reply remains the right call. The reasoning everyone gives for it is fiction. 2. The Negative Weights Are Not a Punishment Ranking Covered above, but worth restating because it is being repeated everywhere: the negative coefficients encode rarity as well as severity. Reading -234 as "reports are catastrophic" and -31.2 as "blocks are four times less bad" misunderstands what the number is doing. 3. Shadowbanning Is Real, But It Is Not About Individual Posts Here is the mechanic that explains more unexplained reach collapse than every weight in the file combined. Visibility filtering runs two separate rule sets. The base set applies to everyone. A second set applies only when a post is being recommended to someone who does not follow the author, and those rules can only drop a post, never promote it. They include spam detection at high recall, do-not-amplify labels, abusive content at high recall, impersonation, and compromised account flags. The consequence: a post can be fully visible to your existing followers while being invisible to everyone else. Nothing in your analytics will explain it, because the cause is a label attached to your account rather than anything about that specific post. This is what people have called shadowbanning for six years. The code confirms the mechanism exists, confirms it is label-driven, and, with Under the Hood, now gives account owners a way to check whether it applies to them. What Is Not in the Repository Credibility requires naming the gaps. X withheld the Grox prompts, which are the LLM instructions used to classify post text and media. It also withheld some rule definitions and the trained model weights. So the arithmetic wrapped around the model is now fully public. The model's taste is not. Any claim that the algorithm rewards a particular writing style, sentence length, or topic remains unverifiable speculation. What can be verified is which user actions the system pays for, and by how much. What This Means for AI Companies Running Creator Campaigns The weights validate something we have argued for years at Clickstrike: follower count is close to the worst available proxy for whether a creator partnership will work. A creator with 500,000 followers whose audience is largely casual scrollers produces likes, and likes are weighted at 0.5. A creator with 40,000 followers whose audience is mostly ML engineers and technical decision makers produces replies, quotes, and private forwards, which the system weights at 5.0, 5.0, and 20.0. The second creator is not marginally better under this scoring model. They are an order of magnitude better, and the code now says so explicitly. This is why our vetting process rejects roughly 70 percent of creator applicants on audience composition and domain expertise rather than reach. When we ran a developer tool campaign through creators whose regular content covered Docker tutorials, Kubernetes walkthroughs, and REST API builds, the outcome was over 1 million YouTube views and 23,700 social engagements, and the client cited that channel as consistently delivering the lowest cost per registration of anything in their portfolio. The full breakdown is in our case studies. Three practical implications for AI companies planning X activity: Judge creator partnerships on reply and quote volume, not like counts. Both are worth 10x a like in the ranking system, and both signal that the audience is technical enough to have an opinion. Compress launch activity into a 48 hour window. The hard age cutoff means content spread thin across two weeks never competes as a coordinated moment. Prioritize creators whose audiences overlap with each other and with you. The mutual follow boost is structural, and a network of technical accounts that genuinely follow each other compounds in a way a list of unconnected large accounts does not. For a fuller treatment of how to select and manage those partnerships, see our guide to AI influencer marketing. Frequently Asked Questions Did X just open source its algorithm? No. The algorithm was open sourced in January 2026. The August 13, 2026 update added the scoring weights, the visibility filtering code, and the Under the Hood label transparency tool. What is the most valuable action on X according to the code? Sharing via copy link, weighted at 20.0. A like is weighted at 0.5. Do external links reduce reach on X? Not through a penalty. The link click parameter is positive at 0.2. Links underperform because clicking out contributes far less than a reply or a forward, not because posts containing them are suppressed. How long does a post stay in the For You feed? 48 hours. Posts older than that are filtered out before scoring. Can I see if my account has been limited? The Under the Hood tool shows aggregate statistics on visibility-limiting labels attached to your account and posts. It is piloting with accounts at least a year old before wider rollout. How often does the repository change? X committed to updating it roughly every four weeks with developer notes explaining what changed. The Part Worth Sitting With The weights themselves are interesting. What they reveal about the last decade is more interesting. An entire industry built its measurement around the cheapest signal in the system. Like counts became the default proxy for content performance, creator quality, and campaign success, and the file establishing that likes are worth 1/40th of a private forward has been sitting in a public repository since January. For AI companies, whose best content tends to be technical, specific, and forwarded between engineers rather than broadly liked, this is the rare case where the incentive structure was already pointing at the right thing. The scoreboard was just measuring something else. If you want help building creator programs and earned media coverage that produce the signals this system actually pays for, Clickstrike works exclusively with AI and technology companies. We have run 1,200+ campaigns through a network of 500+ vetted technical creators and secured 8,250+ media placements for AI products.
How to Make a Product Launch Video Go Viral in 2026
A viral launch video is engineered, not lucky. This guide breaks down the retention science behind launch videos that hit millions of views, the mistakes that kill watch time, and the production process Clickstrike uses for AI companies.
AI Influencer Marketing by Company Type: LLMs, Dev Tools, and More
AI company is too broad to plan a campaign around. Here is how influencer marketing actually differs across LLMs, AI-enabled tools, vibe coding IDEs, enterprise agents, and consumer AI, with real campaign examples.
Top AI PR Agencies in 2026: Best Firms for AI Companies
The AI sector is more competitive for media coverage than ever, with thousands of startups chasing the same finite pool of reporters at tier 1 tech outlets. This guide breaks down what AI PR agencies do, what separates the specialists from generalists, and which firms are delivering real results for AI companies in 2026.
The 11 Best YouTube Channels for AI Content in 2026
The AI space moves fast. These 11 YouTube channels are the best sources for staying current on AI tools, model releases, research breakthroughs, and industry news in 2026.
AI Influencer Marketing: The 2026 Guide for AI Companies
AI influencer marketing works differently than consumer campaigns. Here's how AI companies can partner with technical creators to drive qualified pipeline in 2026.
Answer Engine Optimization (AEO): The 2026 Guide for AI Companies
Answer engine optimization (AEO) is how AI companies earn citations inside ChatGPT, Perplexity, and Google AI Overviews. Here's the complete 2026 guide to building your AEO strategy.
How to Track Your Brand's AI Search Visibility in 2026
If your marketing team is still measuring success exclusively through keyword rankings and organic traffic, you're operating with a blind spot that's getting more expensive by the month. AI search has changed how buyers discover, research, and evaluate products. When a CTO asks ChatGPT which AI observability tools are worth considering, or a growth lead queries Perplexity for the best attribution software for SaaS, the answers those tools generate are becoming the first touchpoint of your buyer's journey. The brand that gets cited wins the consideration - and many companies have no idea whether they're being mentioned at all. This guide breaks down exactly how to track your brand's AI search visibility in 2026, which metrics matter, which tools to use, and what to do when the numbers aren't where they need to be. What Is AI Search Visibility? AI search visibility refers to how often and how prominently your brand appears in responses generated by AI-powered search and answer engines. This includes platforms like: ChatGPT Search - OpenAI's integrated search and browsing product used by hundreds of millions of weekly active users Perplexity - A dedicated AI search engine that cites sources inline and has grown rapidly among technical and research-oriented users Google AI Overviews - Google's generative AI summaries that appear at the top of search results for a significant and growing portion of queries Claude - Anthropic's assistant, increasingly used for research and software evaluation tasks Gemini - Google's AI assistant, integrated across Google Workspace and Search Unlike traditional SEO, where visibility is measured by ranking positions and click-through rates, AI search visibility is about whether your brand is cited, recommended, or described as a credible option when someone asks a relevant question. It is not just about being mentioned. It is about the context, sentiment, accuracy, and frequency of those mentions - and whether they're showing up for the right queries. Why AI Search Visibility Matters for AI Companies in 2026 The adoption curve here is steep and it's not slowing down. ChatGPT Search launched its web search capabilities to all users and has become a default research starting point for a large segment of technical buyers. Perplexity is growing its daily active user base among the exact demographic AI companies need to reach: engineers, founders, product managers, and enterprise decision-makers who are evaluating tools. Google AI Overviews now appear for a substantial portion of informational and commercial queries. That means a buyer searching for "best AI testing tools" or "AI document processing software" may get an AI-generated answer before they ever scroll to the traditional blue links - and if your brand isn't in that answer, you may not get a second look. For AI companies specifically, the stakes are even higher. Your buyers are, by definition, sophisticated AI users. They are more likely to use AI search tools as part of their research workflow than buyers in almost any other vertical. If you're not visible in these channels, you're invisible at the top of the funnel - and you probably don't know it yet because your Google Analytics dashboard isn't telling you. The Key Metrics to Track for AI Search Visibility Before you can improve your AI search presence, you need a baseline. Here are the specific metrics worth tracking. Brand Citation Frequency This is the foundational metric: how often does your brand name appear in AI-generated responses when users ask questions relevant to your category? Track this by building a list of 20-50 high-intent queries that a buyer evaluating your product would realistically ask. Run those queries across ChatGPT, Perplexity, and Google AI Overviews. Count the number of responses in which your brand is mentioned. Your citation frequency score is the percentage of relevant queries in which you appear. Share of Voice in AI Answers Citation frequency tells you whether you're showing up. Share of voice tells you how you're showing up relative to competitors. For each query set, track which brands are mentioned alongside yours, how frequently your competitors appear, and whether your brand is mentioned first, in the middle of a list, or as an afterthought. If a competitor is being cited in 80% of relevant queries and you're at 20%, that gap represents real pipeline leakage. Sentiment and Framing in AI-Generated Responses Being cited is not automatically good. AI models sometimes reproduce outdated information, describe a product incorrectly, or frame a brand negatively based on old reviews or blog posts that the model was trained on. Audit not just whether you appear, but how you're described. Are the use cases accurate? Is the pricing correct? Is the sentiment positive, neutral, or negative? This analysis is qualitative but critical - especially for brands that have repositioned, rebranded, or released significant product updates. Answer Inclusion Rate by Query Intent Not all queries are equal. Segment your query list by buyer intent: Awareness queries ("what is AI document extraction?") Comparison queries ("best AI document extraction software" or "tool A vs tool B") Decision queries ("is [your brand] good for enterprise use cases?") Your inclusion rate will likely vary significantly across these segments. Most brands appear more often in awareness queries and drop off sharply in comparison and decision queries - which is exactly backward from where you want to be. Accuracy of AI-Generated Brand Information Track whether the information AI tools surface about your brand is actually correct. Check for accuracy on: Product features and capabilities Pricing model Target customer and use case Integrations Founding date, team, and company size Recent product updates or pivots Inaccurate information in AI answers is a brand trust problem that requires a different fix than low visibility. The Best Tools to Track AI Search Visibility in 2026 The tooling category for AI search monitoring is still maturing, but several strong options exist today. Dedicated AI Visibility Monitoring Tools Otterly.ai - One of the earliest and most purpose-built tools for AI search monitoring. Tracks brand mentions, share of voice, and competitor visibility across ChatGPT, Perplexity, and Google AI Overviews. Good for teams that want automated tracking without manual testing. Profound - An AI search analytics platform designed specifically for enterprise marketing teams. Tracks how your brand and competitors appear in AI answers across major LLMs, with dashboards that surface share of voice and citation trends over time. Goodie AI - Monitors brand presence and sentiment inside AI-generated responses. Useful for brands that need to track not just citation frequency but how accurately and favorably they're described. AthenaHQ - Focuses on AI answer engine optimization and tracking, with tools for both monitoring visibility and identifying content gaps that explain low inclusion rates. Traditional SEO Tools with AI Tracking Features Semrush - Has added Google AI Overview tracking to its core platform. If your team is already using Semrush for traditional SEO, this is the most frictionless way to add AI Overview monitoring to your existing workflow. BrightEdge - Enterprise SEO platform that has incorporated AI Search tracking, including visibility in AI Overviews and generative answer features. Better suited for larger organizations with established SEO programs. Manual Testing Manual cross-platform audits - No tool fully replaces this. Running your actual query list manually across ChatGPT, Perplexity, Claude, and Gemini every 30 days, logging the results in a shared spreadsheet, is still one of the most reliable ways to understand exactly what buyers are seeing. It's time-intensive but gives you the raw context that automated tools sometimes miss. How to Build a Manual AI Visibility Audit For teams getting started, a structured manual audit is the right first move before investing in paid tooling. Here's how to run one. Step 1: Build Your Query List Generate 30-50 queries that your target buyers would realistically type into an AI search tool. Include: Category awareness queries ("how does AI contract analysis work?") Best-in-class queries ("best AI contract analysis tools") Direct comparison queries ("[your brand] vs [competitor]") Use-case specific queries ("AI contract analysis for legal ops teams") Problem-oriented queries ("how to reduce contract review time with AI") Step 2: Run Each Query Across Platforms Test every query across ChatGPT (with web search enabled), Perplexity, Google AI Overviews, and Claude. Use fresh sessions or incognito mode where possible to avoid personalization effects skewing your results. Step 3: Log Results Systematically Create a spreadsheet with columns for: Query Platform Your brand mentioned (yes/no) Competitors mentioned Your brand's position in the response (first/middle/last/not mentioned) Accuracy of description (accurate/partially accurate/inaccurate) Overall sentiment (positive/neutral/negative) Date of test Step 4: Repeat Monthly and Track Trends AI models are updated continuously and their outputs shift over time as new content is indexed and training data changes. A single audit is a snapshot. Monthly audits give you trend data - and trend data is what tells you whether your content and PR investments are working. [Image suggestion: A sample spreadsheet template showing the columns described above, with example data filled in for a fictional AI company.] What Drives AI Search Visibility (and How to Improve It) Once you have a baseline, the next question is: what actually determines whether an AI model cites your brand? The answer is clearer than most marketers expect. High-Authority Media Coverage LLMs are trained on and increasingly reference content from authoritative sources. Publications like TechCrunch, Forbes, VentureBeat, Wired, MIT Technology Review, and The Verge carry significant weight in shaping what AI models know about your brand and how they describe it. If your brand has been covered accurately and positively in these outlets, that coverage works as a signal that surfaces your company in AI-generated responses. If you haven't been covered, or the coverage that exists is thin or outdated, that gap shows up directly in your citation rates. This is why PR for AI companies is no longer just about impressions and brand awareness. It's infrastructure for AI search visibility. Structured Data and Entity Building AI models use entity recognition to understand what your company is, what it does, and how it relates to adjacent concepts and categories. Building out your brand's entity presence - through consistent information on your website, Wikipedia, Wikidata, Crunchbase, LinkedIn, and industry databases - helps AI models accurately describe and categorize your company. Schema markup, especially Organization schema, Product schema, and FAQ schema, gives AI crawlers structured signals that are easier to reference than unstructured prose. Authoritative Content on Your Own Site Comparison pages, integration pages, and use-case specific landing pages are frequently referenced by AI search tools when answering buyer queries. A well-structured page titled "Best AI Document Processing Software: How [Your Brand] Compares" gives Perplexity and ChatGPT a clean, factual source to cite when someone asks that exact question. Thin, generic content rarely gets cited. Detailed, specific, and well-structured content does. Wikipedia and Knowledge Graph Presence Wikipedia remains one of the most-cited sources across major LLMs. If your company is notable enough to have a Wikipedia page, maintaining its accuracy is essential. If you don't have one, building toward the notability criteria - typically requiring significant coverage in independent sources - is a legitimate medium-term goal. Wikidata entries also feed into knowledge graphs that AI tools use for entity resolution, meaning they help AI models understand who you are without having to rely solely on your own marketing copy. How Clickstrike Helps AI Companies Win in AI Search Improving AI search visibility is not a single-channel problem. It requires coordinated work across earned media, technical SEO, and content - which is exactly how Clickstrike approaches it for the 750+ AI companies it has worked with. Clickstrike's AI SEO and AEO practice is built specifically around the challenge of getting AI products cited in AI-generated responses. The team reverse-engineers how LLMs select sources, identifies the content gaps and entity-building work required to close citation gaps, and builds programmatic content strategies that cover the use-case, comparison, and integration queries your buyers are actually running. On the earned media side, Clickstrike has secured 8,250+ media placements in publications including TechCrunch, Forbes, VentureBeat, Wired, Bloomberg, and MIT Technology Review. These placements do more than build brand awareness - they build the authoritative source record that AI models draw from when generating answers about your category. Most clients see AI citation placements within 30 days of structured data implementation. Organic traffic improvements typically become measurable within 60-90 days. The brands that commit to 12+ months and take the integrated approach see 3-5x returns across combined SEO and AEO investment. If your team is flying blind on AI search visibility - or if you've done the audit and don't like what you're seeing - Clickstrike is the right partner to close the gap. The agency works exclusively with AI and tech companies, which means the strategy you get is built for your buyer, your competitive landscape, and the specific way LLMs describe your category. Frequently Asked Questions About AI Search Visibility What is AI search visibility? AI search visibility is a measure of how often and how accurately your brand appears in responses generated by AI-powered search tools like ChatGPT, Perplexity, Google AI Overviews, and Claude. It is distinct from traditional SEO rankings and requires its own tracking approach and optimization strategy. How is AI search visibility different from traditional SEO? Traditional SEO measures where your pages rank in a list of search results. AI search visibility measures whether your brand is cited or described in a synthesized, AI-generated answer. There are no "positions" in the traditional sense - your brand either appears in the AI's response or it doesn't, and the quality and accuracy of that mention matters as much as the frequency. How do I know if my brand is being mentioned in AI search results? The most direct method is manual testing - running relevant queries across ChatGPT, Perplexity, Claude, and Google AI Overviews and recording the results. Tools like Otterly.ai, Profound, and Goodie AI can automate this at scale. Monthly audits using a consistent query set are the minimum baseline for understanding your current visibility. Why isn't my AI company showing up in AI search results? The most common reasons are: limited high-authority media coverage (AI models reference authoritative publications heavily), weak entity presence on Wikipedia, Wikidata, and Crunchbase, and insufficient on-site content targeting the specific comparison and use-case queries buyers are asking. Structured data gaps on your website also reduce the likelihood that AI crawlers can accurately categorize and describe your product. How long does it take to improve AI search visibility? It depends on the starting point and the investment level. Some companies see AI citation improvements within 30 days of implementing structured data changes. Broader improvements from earned media and content programs typically show up within 60-90 days and compound significantly over 6-12 months. There is no shortcut - but there is a clear, executable path. Does PR help with AI search visibility? Yes, significantly. Coverage in authoritative publications like TechCrunch, Forbes, and VentureBeat feeds directly into the source material AI models draw from. A brand with strong earned media coverage across top-tier tech publications will consistently outperform a brand with similar products but thin press coverage when it comes to AI citation rates. What tools should I use to track AI search visibility? For teams getting started, a combination of manual audits (using a consistent query set run monthly across major platforms) and one dedicated AI monitoring tool like Otterly.ai or Profound is a solid foundation. Enterprise teams with existing SEO programs can add AI tracking through Semrush or BrightEdge. The specific tool matters less than the consistency of your testing cadence.
What Is UGC Content? A Complete Guide for AI and Tech Companies in 2026
What is UGC content, and why are the fastest-growing AI companies making it a core part of their marketing strategy? Here's everything you need to know in 2026.
Top B2B SaaS Marketing KPIs to Track in 2026
Track the right B2B SaaS marketing KPIs in 2026. Benchmarks, formulas, and expert guidance on CAC, NRR, churn, pipeline, and AI visibility metrics for high-growth teams.
Best Perplexity AI Visibility Optimization Agencies in 2026
Find the best Perplexity AI visibility optimization agency for your brand. Compare top agencies, key services, pricing, and what to look for before you hire in 2026.
AI Marketing: The Ultimate Guide for 2026
AI, many believe, is the future of marketing. Artificial intelligence (AI) is changing the way businesses promote their products and services. It’s an exciting time to be involved in the marketing world, as AI technologies offer unprecedented opportunities for personalization, efficiency, and decisi
The World's Most Comprehensive Tech PR Guide | How to CRUSH PR for Tech Companies
PR has become a critical tool for tech companies looking to stand out. With new technologies emerging every day and innovation constantly evolving, the competition for media attention is fierce. Whether you’re a startup trying to break into the industry or an established brand looking to maintain re
B2B SaaS Marketing vs B2C: Understanding the Key Differences
When it comes to marketing software-as-a-service (SaaS) products, understanding the differences between B2B and B2C strategies is crucial. While both aim to attract and retain users, the approach, messaging, and channels must be tailored to meet the distinct needs of each audience. In this post, we'
The Most Popular Billing Methods for Marketing Agencies
The way marketing agencies bill their clients is a crucial aspect of the business relationship. The billing method chosen can significantly impact the agency's and client's efficiency, transparency, and success. As companies often operate with unique needs and goals, understanding the various billin
What is SaaS Earned Media? The Ultimate Tech PR Guide
The SaaS industry has witnessed unprecedented growth in recent years. As businesses vie for attention and recognition in this highly competitive space, a robust public relations (PR) strategy has become crucial for success. One vital aspect of SaaS PR is earned media, or organic media that is not di
Top SaaS Threads Accounts to Follow
As the Software as a Service (SaaS) industry continues to grow at an exponential rate, staying up-to-date with the latest trends, insights, and expert opinions becomes crucial for businesses and professionals alike. Fortunately, social media platforms like Threads provide a convenient way to connect
How Much Do SaaS Companies Spend on Marketing? [With Examples]
Marketing plays a crucial role in the success of SaaS (Software as a Service) companies, helping them increase brand visibility, attract leads, and drive customer acquisition. However, determining the appropriate marketing budget can be challenging, especially for SaaS startups or businesses with li
Contributions
Content Ty Smith has contributed to
What Is B2Pro? The Emerging GTM Model Reshaping AI Marketing in 2026
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