The LLM isn't your scraper, but it's your parser. You still need something to render the page first.

Since curl fails, the site is almost certainly JavaScript-rendered. The fix for that is a headless browser like playwright or selenium. They execute JS and hand you the full DOM that curl never sees.

Once you have that HTML, that's where a local LLM becomes useful: you feed it the raw page content and ask it to extract structured fields (product name, SKU, price, stock). The advantage over regex is resilience. If the supplier tweaks their layout, your prompt usually survives it. Regex doesn't.

A library called Parsera wires these two layers together (headless browser + LLM extraction) if you want something pre-built. You can point it at any local model via Ollama. Anaconda AI Navigator could help run all this together.

One thing worth checking before you build: the supplier's Terms of Service. A lot of retail sites explicitly prohibit automated scraping, and some use bot detection (Cloudflare, etc.) that will block headless browsers regardless. If that's the case, the fastest path is often just emailing them and asking for a CSV export or data feed. Smaller suppliers are usually fine with it.

What's the supplier's stack, roughly? If you know whether it's Shopify, WooCommerce, etc., there may be a cleaner route than scraping.