Lena Zhou
Growth & Integration Specialist
Lena Zhou is a Growth & Integration Specialist at Nstdata, focused on helping businesses leverage proxy technology for scalable data collection, automation, and global operations. With experience in product adoption, platform integrations, and growth strategies, she shares practical insights on proxy use cases, market intelligence, web scraping workflows, and digital expansion.
All Articles
Feeding Web Context to Claude and GPT via Crawl
A practical Python workflow for crawling public documentation, preparing bounded Markdown context, and checking Claude and GPT answers against sources.
Building a Price-Watching Agent with Crawl API
A Python price-monitoring tutorial that separates page crawling from extraction, historical storage, change detection, and alerts.
How Public Web Data Can Support Climate Change Research
A source-first guide to combining public climate-related documents with authoritative observations while preserving provenance and avoiding unsupported claims.
Grok Setup and Web Crawler Example: A Practical Guide
A practical Python integration pattern that fetches a permitted page with Nstdata Crawl, sends bounded Markdown to Grok, and handles security and reliability.
10 Web Data Points That Support Better Retail Decisions
A practical list of ten public web signals retail teams can connect to decisions, with collection, data quality, compliance, and measurement guidance.
Zillow Scraper with Python: A Guide to Public Listing Data
A Python walkthrough for extracting listing fields from a local synthetic fixture, plus guidance on permitted Zillow data sources and real estate research workflows.
Web Scraping for AI Agents: The Complete Guide
A practical guide to agent web data: when to use search, a Crawl API, browser automation, or direct HTTP, plus tool design, security, and evaluation. It distinguishes URL discovery from page extraction and includes permission and prompt-injection safeguards.
RAG Data Quality: Why Clean Crawled Data Matters
Understand seven dimensions of RAG data quality and how to validate crawled pages, compare clean and raw inputs, preserve provenance, and diagnose retrieval failures. It separates extraction, retrieval, and generation errors so teams can debug the right stage.
Chunking Strategies for Web-Crawled RAG Data
Compare six common RAG chunking strategies for web content. Includes Python baselines, advice for code and tables, and an evaluation framework without invented benchmark results.
