Reports

The websites you need to monitor are getting harder to scrape, not easier. This report examines the breaking point where dynamic rendering, structural drift, and anti-bot defenses make in-house collection more of a burden than advantage; and outsourcing the smarter call.

Building a scraper is affordable. Keeping it running is not. This report breaks down the real three-year cost of in-house web scraping; the engineering hours, proxy spend, downtime, and missed opportunities that rarely show up in the original plan.

The market-moving signal now appears on the web weeks before it reaches an earnings report. This report examines how financial institutions acquire and operationalize alternative web data; pricing, hiring, sentiment, and demand; to see those signals first.

Automated traffic now rivals human traffic; and the bots behind it look more human every year. This report examines how detection and mitigation systems are evolving to tell the difference, blocking sophisticated automation without turning away real users.

Feeding web data to an AI system is a different discipline than feeding it to a dashboard. AI is less forgiving of quality gaps, silent failures, and missing provenance. This report examines what separates AI-grade web data pipelines from those built for traditional analytics.

Model access is no longer the bottleneck. In 2026, the organizations pulling ahead are the ones that can feed their AI systems reliable, real-time web data at scale. This report maps the shift from model-centric strategies to data infrastructure as the core competitive advantage.