What is data extraction?

Data extraction sits between fetching a source and using its contents. The fetch produces raw HTML, a PDF, an email body; the extraction step isolates the values that matter (price, address, table rows) and emits them in a shape downstream code can consume. Historically this meant CSS selectors and regular expressions; increasingly it means an LLM call with a JSON schema.

Three flavors are common. Rule-based extraction uses hand-written selectors against a known template, fast and cheap but brittle. ML-based extraction trains on labeled examples, robust to layout drift but expensive to build. LLM-based extraction prompts a model with the page and a schema, expensive per call but flexible enough to handle unknown layouts. Real systems mix all three.

For brand intelligence, extraction is everywhere: pulling a logo URL out of <head> markup, recovering color tokens from an inline <style> block, finding a company's address on a contact page, parsing pricing tiers off a marketing page. The harder cases are the ones where the same fact is presented differently across sites, and the schema has to absorb that variance.