Sana Ibrahim

Visual sitemaps reveal orphaned pages and structural problems that spreadsheets hide from auditors.

Extracted data needs semantic structure, not just text, to work with AI systems.

LLM extraction survives redesigns where selectors fail, but needs careful pipeline design to work.

Catch crawler failures that look healthy by measuring what standard monitoring misses.

Building infrastructure that survives the real internet at scale.

AI systems need fresher data than analytics pipelines, or they confidently deliver wrong answers.

Identify pagination patterns before building scrapers to avoid silent data gaps.

AI crawlers now treat sitemaps as essential discovery tools, not just search engine optimization.

Crawl-Delay politely requests limits that most major AI crawlers ignore outright.

Choosing URLs wrong breaks everything downstream in your pipeline.

Managing crawl budget by prioritizing high-value pages over waste saves indexation at scale.

Benchmark scores miss the multi-step failures that tank agents in production.

Separate source credibility from overall quality scores to catch citation failures before they ship.

AI agents researching the web need to verify what they find before trusting it.

Four design patterns that prevent crawlers from falling apart at scale.

Agents need scraping APIs designed for their failure modes, not developer-friendly DIY tools.