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I built CrawlSpider by repurposing pieces of two things I had already built.

I built CrawlSpider by repurposing pieces of two things I had already built.... One was InfoCaptor, where I already had data visualization, dashboard and data processing pipeline. The other was an internal link builder I had built under CrawlSpider, which already knew how to crawl sites, extract pages and work with URLs/content. I started combining those pieces into something different: an AI visibility tracker. The basic pipeline is now: brand + prompts → multiple LLMs → collect resp

五维评分

付费意愿
45
需求真实度
55
个人可落地
88
竞争空间
35
新鲜度
72

AI 分析

痛点:品牌方不知道自己在 ChatGPT/Claude 等大模型回答中被提及、被引用的频率和趋势,缺少可长期追踪的 AI 可见度数据。

目标用户:做 GEO/AI SEO 的品牌市场人员、SEO 代理商、内容营销团队和独立顾问。

证据:The basic pipeline is now: brand + prompts → multiple LLMs → collect responses → detect brand mentions/citations → store results → track visibility over time. / PS I also published a blog post recently how to build this from scratch and some of the pit falls.

变现:SaaS 订阅制:按监控的品牌数、prompt 数量和 LLM 平台数量分级收费(约 $29–$199/月),代理商可加价提供白标报告;也可做一次性 AI 可见度审计报告作为引流产品。

MVP:2 周 MVP:输入品牌名 + 10~20 个 prompt,调用 2~3 家 LLM API 定期抓取回答,用规则 + LLM 判定品牌提及与引用来源,存入 Supabase,前端出一个提及率趋势图 + 竞品对比表,接 Stripe 和每周邮件报告。

竞品:Profound、Peec AI、Otterly.ai、Scrunch AI、Athena

摘要:作者把之前做的 InfoCaptor(数据可视化/管线)和一个内部链接爬虫拼在一起,做出了 AI 可见度追踪工具 CrawlSpider:品牌+prompt → 多个 LLM → 采集回答 → 检测品牌提及/引用 → 存库 → 长期追踪趋势,目前已上线运行,并写了从零构建的博客。帖子本身没有披露任何付费用户、收入或他人需求附和的证据,但 AI 可见度(GEO/AEO)是当前有真实付费市场的方向,只是已有 Profound、Peec、Otterly 等多家竞品,独立开发者需靠垂直场景或低价切入。