Descript, a $550M company, increased subscriptions 56% in JUST 30 days using AI search. this is the genius AEO system behind it: ​1. They started off with 1,000+ pages they had already been published -> 600+ blog posts -> 400+ tool pages instead of pumping out another 100 AI-written articles, they focused on making the content they already owned perform better. and there was a lot to fix. their old programmatic content system could produce pages fast, but quality was inconsistent and older pages were starting to decay. ​2. Before rewriting anything, they cleaned up the content library they checked for: -> pages competing against each other -> duplicate content -> outdated pages -> weak titles + descriptions this is important because publishing a “better” article means nothing if 3 other pages on your site are fighting it for the same topic. they fixed the structure of the library first. ​3 Then every page went through a keep / improve / remove process instead of telling AI: “rewrite this article” their workflow first figured out: -> what information was still useful -> what was outdated -> what was missing -> what should be removed entirely so their AI wasn’t generating a new article from scratch every time. it was making targeted changes to an existing page that already had authority. ​4. Then they changed the pages specifically for AI search this was one of the biggest parts. Descript added authoritative sources to support claims. they restructured headings so the page was easier for search engines and AI systems to understand. and they updated old statistics + research instead of leaving stale numbers sitting inside articles for years. AirOps measured Descript’s average AEO score before and after this. before: 61 30 days later: 78 a 28% improvement. ​5. The outbound sources weren’t random links either AirOps’ research found pages with multiple authoritative external citations were getting cited by AI systems far more often. so instead of making unsupported claims like: “video is becoming the most important content format” the content would point to the actual study, dataset or expert behind the claim. that gives an AI answer something it can verify before repeating it. ​6. They also made the content easier to rip answers out of long walls of text got broken into: -> clear H2/H3 sections -> short answers -> lists -> direct explanations -> question-based sections where relevant the goal was simple: if ChatGPT needed one paragraph from the page to answer a question, that paragraph should make sense without needing the other 2,000 words around it. ​7 Then they solved one of the biggest problems with doing this at scale: brand quality. Descript baked its own brand guidelines into the workflow. so hundreds of pages could be refreshed without every article suddenly sounding like generic AI content. the workflow handled the repetitive research + rewriting. Descript still controlled what quality or you can sya they controlled what “good” looked like. ​8. They put QA inside the system instead of doing it manually at the end the workflow could check things like: -> brand consistency -> outdated information -> content gaps -> metadata -> structure -> duplication before the refreshed page went live. that helped them refresh content 9X faster without adding a giant content team. ​9. And they didn’t treat a refresh as a one-time project this might be the most useful part to steal. the system could monitor performance and trigger another refresh when a page started declining. so instead of: publish -> forget about it for 2 years they built: publish -> monitor -> decline detected -> audit -> refresh -> measure again content became something they maintained continuously. ​10. Then they measured whether the refreshes actually changed anything Descript’s average AEO score went: 61 -> 78 in 30 days their organic traffic increased 35%. their content refresh process became 9X faster. and their brand visibility score reached 28% after the refreshes. but the number that matters most for every brand: subscriptions increased 56% in 30 days during the period Descript says corresponded with content engineered for AI agents. the main takeway here is NOT to publish more AI content. instead of constantly publishing new pages, Descript kept finding which of its existing 1,000+ pages were losing performance, fixing them, and then building/iterating that system. if you want us to find what’s stopping your company from ranking & want to rank #1 on AI search, reply “audit”

our startup was acquired today :) jenni ai is now part of the digital science family we fucking did it!!!!! i could go on and on about why this acquisition is so perfect, but i’ll save that for another time for now, i want to focus on the fact that we built jenni in public from day one all the way to exit there was honestly very little i didn’t share, so i hope this archive of our journey can help a few founders on their own fantastical journeys thank you to everyone who's followed along and witnessed all the pivots, near bankruptcies, and general hijinks that come with running a company despite the roller coaster, we ended by hitting our highest MRR ever shortly before deal close 🥳 it's been a crazy ride, you all helped me live out my dream, truly i owe my success to my team, my investors, and my family for all of their support throughout the years not too bad for a college dropout who had to move back in with his parents!

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Granola, a $1.5B company, increased monthly new users from AI search by 4,600% in JUST 7 months. here’s the genius AEO workflow behind it: 1. They started with buyer decisions, not keywords -> Granola mapped the questions people ask before choosing an AI meeting tool -> Then they turned those same decisions into both Google keywords and AI prompts so they could measure where the brand actually showed up 2. Every lost prompt got diagnosed before anything was created -> If Granola wasn’t showing up, they first asked why -> Was the right page missing? -> Did the page already exist but fail to answer the question properly? -> Was there a crawl/indexing problem? -> Or was AI getting the answer from third-party sources that barely mentioned Granola? 3 They separated “missing page” from “weak page” -> This sounds obvious, but it changes the entire content strategy -> If a relevant page already existed, they improved that page instead of publishing another article targeting the same question -> New pages were only created when there was actually a gap 4. They stopped treating every technical SEO issue like it mattered equally -> Crawlability, indexing and performance problems were prioritized based on whether they affected pages tied to real buyer demand -> A technical issue on a page connected to an important buying question came before cleaning up random site-wide warnings 5. They studied the exact third-party sources AI was already trusting -> For prompts Granola lost, they looked at the Reddit threads, review sites, articles, directories and other pages showing up instead -> The question wasn’t just “how do we get more backlinks?” -> It was “which sources are shaping this specific recommendation, and why are competitors represented there better than us?” 6. They used a simple framework for every important source: -> OWN: pages Granola controls directly -> CLAIM: profiles and listings they can take ownership of -> EARN: editorial mentions, reviews and community discussions they have to deserve -> CONTEST: outdated or incorrect information they need corrected -> Different source = different action 7. Reddit became part of the search strategy -> Instead of posting everywhere, they focused on communities much closer to the product and buyer -> The same buyer questions being tracked in AI search were used to decide which conversations were actually worth joining 8 They measured Google and AI separately -> A page could rank better on Google and still barely move in ChatGPT -> AI mentions could rise while organic traffic stayed flat -> So Granola tracked both systems independently instead of assuming SEO performance meant AEO performance 9. Then they reran the same questions and scaled what actually moved users -> measure the gap -> diagnose why it exists -> make one specific change -> rerun the prompts -> track whether visibility and activated users moved -> double down on what worked the result: -> monthly new active users from AI search increased 4,600% -> non-branded AI citation rate increased 558% -> non-branded mention rate increased 739% -> organic sessions increased 134% Granola didn’t win AI search by JUST publishing more blogs & content like every other company instead they built a system for figuring out exactly what they were missing, choosing the right fix, and measuring whether it actually brought in users. then after that just iterating and doubling down on what worked AI search is now one of their biggest acquisition channels. and it should be one of yours too! if you want us to run an audit for your company and make you rank #1 in AI search, reply “audit”

Webflow increased signups from AI search by 485% in a single quarter. those visitors also converted 6X better than normal Google traffic. this is the insane AEO system behind it: 1. They started measuring AI search as its own acquisition channel -> Webflow tracked share of voice, citations and signups from LLMs separately from normal SEO -> Their share of voice went from 33% to 64%, while the share of total signups coming from LLMs doubled from 4% to 8% 2. They mapped where AI was already getting its answers -> On Webflow’s own site, template pages and blog posts were showing up -> off-site, affiliate publications, Reddit, Webflow’s YouTube and influencer YouTube channels were influencing answers too -> Reddit appeared in 10% of ChatGPT citations in their research, while YouTube appeared in 32% of Perplexity responses 3 They stopped trying to win one huge query like “best CMS” -> Webflow built 10 more specific questions around the buyer, team and tool being used -> Example: “what is the best CMS for in-house teams that use [tool]?” -> Share of voice across that test went from 38% to 100% < this was made a BIG difference -> within 3 weeks, 70% of the questions they targeted had improved 4 Then they found 1,500 template pages they could optimize -> These weren’t random blog posts. Templates were already one of Webflow’s highest-converting page types -> Many had almost nothing above the fold besides the H1, so they added unique copy answering exactly who the template was for and what problem it solved 5. Every optimized template followed the same 4-part structure -> the main search/question -> a Webflow-branded term -> the exact target user -> the pain point or outcome the template solved -> A real estate template page, for example, was rewritten specifically around agents and realty firms trying to connect buyers with available listings 6. That one initiative took AI-driven signups from 168 to 983 in one quarter -> a 485% increase -> Instead of creating hundreds of new articles, they improved pages that already had product intent and could turn traffic directly into signups 7. They built a Reddit monitoring system around 100 priority queries -> First they identified which Reddit threads were already appearing in AI answers -> Then they tracked the thread, suggested a useful response, checked sentiment, reviewed the contribution and measured what happened after posting -> 98% of engagement around Webflow ended up positive or neutral 8. Their Reddit community grew alongside it -> r/Webflow views increased 135% year-over-year -> membership increased 12% -> published posts + comments increased 145% -> Reddit wasn’t treated as somewhere to dump links. It became part of the information AI could find around Webflow 9 Then they tested something ridiculously simple: adding a table of contents -> 10 Webflow pages got the change -> 10 similar pages were left untouched as a control group -> after 4 weeks, the test group saw a 60% control-adjusted increase in AI visits and 23% growth in clicks 10 The table of contents gave AI smaller sections it could pull answers from -> A long article about “landing pages vs websites” was broken into sections like “when do you need a website?” and “when do you need a landing page?” -> instead of forcing the model to understand one giant article, each follow-up question had an obvious section to retrieve 11. They ran another controlled test by turning normal headings into actual questions -> “AI design tools” became “which AI tools are most useful for web designers?” -> “advantages” became “what are the advantages of using AI-powered design tools?” -> same article, clearer structure -> AI visits increased 58% after adjusting against the control group 12. They didn’t judge these experiments from one spike in traffic -> Both tests used 10 test URLs matched against 10 similar control URLs and ran for 5 weeks -> the question-heading test had a p-value of 0.001 -> the table-of-contents test came in at 0.002 -> they were actually testing whether the changes caused movement instead of taking screenshots whenever ChatGPT mentioned them 13. Every SEO topic was eventually treated as an AEO topic too -> Webflow kept the core page useful for Google, but also made sure it answered the specific follow-up questions people ask inside AI conversations -> one page could rank traditionally while also giving AI multiple clear answers to retrieve from it 14. Then they measured the metric that actually matters: signups -> 94% growth in LLM share of voice -> 2X more signups coming from LLMs -> 485% growth in AI signups from the optimized template pages -> 24% signup conversion rate from AI traffic -> compared with just 4% from non-brand SEO Webflow didn’t win AI search by blindly publishing more content. they ran actual experiments, scaled the changes that worked and focused them on pages that already converted. AI visitors now convert 6X better than their non-brand Google traffic. why aren’t you optimizing for them? if you want us to run an audit for your comapny and make you rank #1 in AI search, reply “audit” 🫶

Runpod, a $325M company, increased monthly new paying customers 300% in just 90 days using AI search. here’s the genius AEO system behind it: 1. They went from tracking 50 prompts to 300 almost overnight -> The prompts were split by buyer persona, funnel stage and niche technical use case instead of just tracking broad category searches -> Their buyers included DevOps engineers, AI builders, researchers and platform teams comparing Runpod against AWS, CoreWeave and Google Cloud 2. ChatGPT became the channel they prioritized -> Around 98% of Runpod’s AI referral traffic was coming from ChatGPT -> So instead of treating every AI engine equally, they focused their monitoring and optimization around the channel already sending real buyers 3. Their audit found a technical problem most teams would have missed -> Several Runpod articles were only partially indexed -> AI could see the title, but not properly access the body content -> They fixed metadata, restructured the pages and corrected formatting before producing more content 4 They built prompts around extremely specific technical buying questions -> “How do serverless GPU platforms compare to traditional cloud infrastructure?” -> “What’s the best way to manage GPU provisioning and auto scaling for AI workloads?” -> The goal was to track the questions their actual ICP asks, not generic high-volume keywords 5. They used the prompts that were already working to decide what to publish next -> Runpod reviewed which questions consistently surfaced the brand -> Those prompts became new content titles, angles and topics 6. Then they exported the entire dataset -> Prompt performance was filtered by topic, buyer persona and stage of the customer journey -> Instead of a marketer guessing what article to write next, the content roadmap came directly from gaps in AI search 7 They paired the prompt data with deeper research -> Runpod used ChatGPT Deep Research to investigate trending themes, audience questions and gaps in existing content before producing the page 8. They reverse engineered the sources AI was already using -> Runpod tracked which owned pages, competitor sites and third-party domains kept getting cited -> This exposed both content opportunities and external sites where competitors had stronger visibility 9. Publishing became a feedback loop -> Find a high-value prompt -> Research why Runpod is winning or losing it -> Publish or fix the relevant page -> Monitor whether Runpod starts appearing more often -> Double down on the prompts that move 10. They brought content production in-house -> Once prompt data and source data were feeding the strategy, Runpod no longer needed to outsource the entire research/content process -> The team could produce around the exact questions and visibility gaps they were seeing in real time 11. They cut the cost of their AI search stack by more than 80% -> Their tooling cost dropped from roughly $3,000/month to $500/month while prompt coverage expanded from 50 to 300 12. Then it turned into actual customers -> ~28,000 visitors from ChatGPT -> ~2,100 conversions -> 8% conversion rate -> ~40 new customers per day from ChatGPT -> roughly a 2-week close cycle within 90 days, Runpod was bringing in 4x more new paying customers every month. they didn’t treat AEO like “publish more blogs.” they built a feedback loop around what buyers ask, what AI recommends, which sources it trusts and what actually turns into customers. a $325M company turned ChatGPT into one of its top acquisition channels. why aren’t you? if you want us to run an audit for your company & make you rank #1 in AI search, reply “audit”

1,850% more leads from AI search. This is the insane AEO system behind HubSpots $13.5 BILLION empire: 1/ HubSpot built 8 separate AI visibility tracking groups -> Each group covered a different product, with tracking broken down further into individual features -> They monitored discovery, comparisons, reviews and purchase-intent prompts to identify exactly where competitors were beating them 2/ They turned customer case studies into hundreds of industry pages -> AI repurposed real customer stories into industry-specific content, with humans reviewing each page before publication -> 92% of those pages were cited by AI search engines, lifting visibility by 49% 3/ Industry-specific comparison pages increased citations by 642% -> Instead of competing only for broad searches like "best CRM," they targeted specific buying decisions such as "best CRM for construction companies" -> Those pages also increased overall mentions by 58% 4/ They started targeting buyers BEFORE they knew what software they needed -> HubSpot built a glossary answering questions like "what is marketing automation?" and "how does lead scoring work?" -> The pages connected those early questions to relevant HubSpot products and increased citation share by 60% 5. They rebuilt product pages around actual ChatGPT questions -> Added buyer-focused headlines, FAQs, comparison tables and scannable information addressing the questions prospects were asking AI -> HubSpot reports citations to those pages increased 56% 6. They reverse engineered the sources AI already trusted -> For valuable prompts where HubSpot was missing, they inspected which publishers kept appearing in AI answers -> This revealed third-party sites that were consistently influencing recommendations 7. Hundreds of publisher partnerships produced nearly 1,000 pages -> HubSpot worked with external websites already earning AI citations, helping expand its presence across third-party content -> Instead of depending entirely on its own website, it increased coverage across sources AI was already retrieving 8. Reddit became part of their weekly growth operation -> HubSpot monitored discussions where potential buyers were researching software, comparing products and asking for recommendations -> They identified conversations and categories where competitors were getting mentioned while HubSpot was largely absent 9. They discovered huge gaps in France and Germany -> Localized campaigns reportedly took HubSpot from effectively zero mentions to 33.5% visibility in France and 17.1% in Germany within a month 10. Every visibility problem had a different solution -> Missing industry coverage? Build targeted pages -> Weak product citations? Improve existing pages -> Competitors dominating third-party sources? Work on external distribution -> Low regional visibility? Localize the strategy 11/ HubSpot increased its overall AI citations by 433% -> The improvements came from changes across product pages, industry content, comparison articles and third-party distribution 12. The result: AI-search leads grew 1,850% in one year -> From Q1 2025 to Q1 2026, HubSpot reported an 1,850% increase in qualified leads from AI search -> Those leads converted at up to 3X the rate of traditional search leads HubSpot essentially built an entire customer acquisition operation around understanding what buyers ask AI, finding where competitors win, and fixing the sources behind those recommendations. A multi BILLION company is treating AI search as such a serious acquisition channel. Why aren't you? if you want us to run an audit for your company and & make you rank #1 in AI search, reply “audit”

run an AI search visibility audit exactly like this before trying to rank in AI search for context, we got one of our customers to rank #1 in ChatGPT in only 2 weeks. here’s how (14 steps): 1. start by building a fixed set of 30–50 prompts your buyers would realistically ask before choosing a product. split them between category searches (“best [category] tools”), comparisons (“[product] vs [competitor]”), alternatives, specific use cases, pricing/cost questions and problem-based searches. don’t build the list around keywords you want to rank for. build it around actual buying decisions. 2. run the exact same prompt set through ChatGPT, Claude, Gemini and Google AI Mode. for every answer, record the model, date, companies mentioned, companies recommended and every cited URL. keep the prompts identical so you can actually compare the results. 3. separate a citation from a mention from a recommendation. if ChatGPT cites one of your blog posts while recommending three competitors, that is not the same as winning the query. track all three separately. 4. repeat your highest-value prompts more than once. AI answers are dynamic. one screenshot where you disappear means very little. you’re looking for gaps that keep showing up across multiple runs, related prompts and different engines. /5 group the prompts into topics instead of optimizing them one-by-one. “best auth API,” “SSO provider for SaaS,” “SAML authentication tools” and “SCIM providers” might all belong to the same buying topic. the goal is to understand which parts of the category you consistently own and which parts your competitors own. 6. now calculate the competitor gap for each topic. if a competitor appears in 18/25 relevant prompts and you appear in 4, start there. don’t waste time fixing a random query where both of you appeared once. 7. open every source the AI used when your competitors won. record which page was cited, which domain owns it, what type of page it is, which competitor it mentions and what claim or recommendation it appears to support. after 20–30 prompts you’ll usually start seeing the same pages and domains repeatedly. 8. before creating anything new, check whether you already have a page that should be winning that query. if the page exists, find out why it isn’t surfacing. does it actually answer the same intent? is the information outdated? is the useful information buried? does the competitor page contain proof, comparisons or data yours doesn’t? building another page can just create two weak pages instead of fixing the right one. 9. check the technical layer next. make sure the page is indexable, internally linked and returning normally to crawlers. for ChatGPT specifically, check that OAI-SearchBot isn’t blocked in robots.txt or by your CDN/WAF. for Google AI features, the page first needs to be indexed and eligible to appear in normal Google Search. 10. investigate the searches underneath the prompt. AI search doesn’t always retrieve pages using the exact sentence the user typed. Google AI Mode uses query fan-out to run related searches underneath a question. Bing Webmaster Tools now exposes the grounding queries its AI systems used to retrieve cited pages. use those related searches to understand what the engine is actually trying to find. 11. only NOW decide what needs to change. sometimes you need a new page. sometimes the right page already exists and needs better coverage. sometimes your information is outdated. sometimes the model keeps relying on third-party comparison pages where every competitor is mentioned except you. sometimes the problem is technical and writing anything would be a waste of time. 12. if you do create or update content, give the model information it cannot get from another generic article. use first-party data, real pricing, specific feature limits, screenshots, benchmarks, comparison criteria, customer results, use-case differences and actual limitations. another 2,000-word summary of information already on the internet gives the engine very little reason to retrieve you. 13. if third-party sources keep shaping the recommendation, work on those separately. find the comparison pages, industry sites, videos and discussions that repeatedly appear in the answers. figure out why competitors are represented there and you aren’t, then earn legitimate inclusion where it actually makes sense. don’t manufacture random mentions across the internet and call it GEO. 14. once the changes have been crawled, rerun the exact same audit. same prompts. same engines. same measurement. track whether your citation share increased, you started appearing in previously lost topics, competitor recommendations dropped and your brand moved from “mentioned” to actually recommended. then keep working on the clusters that are moving instead of starting another random content sprint. if you want us to run this audit for your company and & make you rank #1 in AI search, reply “audit”

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