Transform web noise into AI knowledge. The flow shows how WellMarked strips away ads and cookie banners to convert raw HTML into clean data for your RAG pipeline. Most RAG tutorials start with a folder of PDFs. That’s fine for demos, but the real world runs on URLs. Your users want to ask questions about a competitor’s docs, a news article published this morning, a GitHub README, or a product page that didn’t exist when you trained your model. For all of that, you need to fetch and clean live web content before it ever touches an embedding model or an LLM. The problem is that raw HTML is terrible LLM input. A typical article page is 80% navigation, cookie banners, footers, ads, and tracking scripts. Feed that to an embedding model and you’re wasting tokens, polluting your vector store, and hallucinating answers from sidebar text.…