Runpod 用 AI 搜索优化内容策略

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”