In this video I build a real-time AI pipeline that takes unstructured company info and turns it into structured data using an LLM—and I do it in a couple of minutes. This matters because your AI is only as good as your data pipeline: garbage in equals garbage out. Always-running pipelines are a huge deal for a lot of use cases, especially RAG pipelines, which I focus on a ton on my channel. I use IBM watsonx.data integration to put the whole thing together with no Python and no servers. IBM manages the infrastructure so the pipeline can run reliably 24/7, and I walk through the basic shape of the flow: pick a source (like Jira or a REST service), add processors to transform the data (think no-code ELT), and send it to targets downstream. I also show the LLM requests streaming in live (via the OpenAI dashboard logs) and the structured outputs you can route to a database, a RAG pipeline, or an API endpoint. The key takeaway: production pipelines aren’t a Jupyter notebook—they’re streaming data flows with parameters and guardrails that can scale to thousands of records, even per second.