Practical guides on web scraping, structured extraction, RAG, and competitive intelligence. Written by engineers, for engineers.
Most RAG hallucinations don't come from the model — they come from bad input data. Stale web pages, unparsed HTML noise, and broken chunk boundaries create garbage-in-garbage-out failure modes that no amount of prompt engineering can fix.
ZoomInfo costs $15,000+ annually, has data that's 18+ months stale, and locks you out the moment you stop paying. Here's how Indian startups are replacing it with a CrawlHQ-powered enrichment pipeline for a fraction of the cost.
India's Election Commission publishes affidavits for every candidate running for office. This guide shows how to extract structured criminal records, asset declarations, and financial data from ECI PDFs at scale using CrawlHQ.
A practical walkthrough: use CrawlHQ's /v1/watch and /v1/extract together to build a live competitor pricing alert system that fires to Slack the moment any competitor changes their pricing page.
AI-powered scraping sounds appealing — until your pipeline breaks on a Tuesday because the model decided to interpret a price differently. Here's how to think about when deterministic wins, and when AI is genuinely additive.
No fluff. Just practical guides on building with web data.
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