Extracting Brand Identity Data From Public Web Sources
Developers can now extract logos, colors, and fonts from any website through a single API call.
Marcus Oyelaran
Section
Extraction Engineering
8 stories in Extraction Engineering.
Validating and Repairing LLM Extraction Output
Schema validity alone cannot catch LLM extractions that carry wrong data into production.
Teodora Vásárhelyi
LLM-Assisted Extraction vs. CSS Selector Pipelines
Selectors work best for stable sources; LLMs justify their cost across varied layouts and domains.
Jailen Rafferty
Validating and Correcting Extracted Data Before Pipeline Ingestion
Corrupted extraction silently feeds bad data to agents unless validated at three checkpoints.
Priya Subramaniam
Prompt Engineering for Consistent Structured Output
Schema-level constraints eliminate the parsing failures that plague production pipelines.
Jailen Rafferty
Incremental Extraction for High-Velocity Web Data Sources
Fingerprint extracted values instead of full pages to skip wasteful re-crawls.
Marcus Oyelaran
Schema-Driven Extraction From Unstructured Web Pages
Define the output first, then let AI find it anywhere on the page.
Teodora Vásárhelyi
Extracting Structured Data From Documentation Sites
Schema-driven JSON extraction turns messy documentation into clean, structured data for AI agents.
Jailen Rafferty