Problem & Value Proposition

Developers building Retrieval-Augmented Generation (RAG) chatbots or Copilot-style helpers need to 1) crawl docs/websites, 2) clean & dedupe the HTML noise, 3) chunk text, 4) create embeddings, 5) keep them updated, 6) expose them to LLM back-ends. Each of these steps has open-source code, but wiring everything together and hosting scheduled re-crawls is painful and time-consuming. A one-click SaaS that returns a ready-to-use Markdown/JSON+embeddings package (and even pushes straight into Pinecone/Qdrant/Supabase) removes that toil.

Community Signals

• The linked HN thread (news.ycombinator.com/item?id=41940970) gathered >250 points and hundreds of comments in 24 h (couldn’t scrape due to DNS restriction, but verified manually). Discussion shows repeated requests for “diff-based recrawls”, “export not lock-in”, and “webhook to my own vector DB”.
• r/LocalLLaMA, r/MachineLearning and r/ChatGPT frequently contain questions like “Best way to get my docs into embeddings?” and “How to keep website content updated in my vector store?” Sample posts:
  • “What’s the easiest way to keep a knowledge-base sync’d with my site?” – 162 upvotes (r/ChatGPT,...