From Scrapers to Agents: How AI Is Changing Web Scraping

We recently spoke with Dan Entrup from It's Pronounced Data about the evolution of web scraping in finance. The conversation touched on why an industry that runs on data still relies on 20-year-old collection methods and how AI is changing that now.

Traditional Scraping

By the early 2000s, investment firms started scraping the web for signals: retail promotions near quarter-end, hiring trends, pricing changes, IR updates. Two decades later, most teams still do web scraping the same way: bespoke, rule-based scripts per source that are fragile, slow to fix, and expensive to maintain.

The downstream stack modernized (cloud, data warehouses, LLMs, etc.), but the ingestion layer stayed manual. Data engineers end up constantly maintaining scrapers, coverage expands slowly, quality varies, and compliance audits are still manual.

From Scripts to Agents

With AI, an analyst or researcher can build datasets in minutes. You point to a source, and we extract, transform, and load the data into a spreadsheet or a data warehouse. No coding required.

AI transforms web scraping in three concrete ways.

First, anyone can now build a scraper. A hedge fund analyst who needs pricing data from Brazilian retailers no longer needs to wait for engineering support. They describe what they want in natural language and get structured data back. No code required.

Second, scrapers fix themselves. When a website moves or redesigns its price field, traditional scripts break. AI-based systems adapt. They semantically recognize the price field on the page, regenerate the scraper code, and validate it against the previous data.

Third, data validation runs continuously. Instead of spot-checking spreadsheets for errors, the system validates every extracted field against predefined rules. A suspiciously missing schema field triggers alerts immediately.

Engineers once had to maintain hundreds of fragile scripts, each breaking in its own special way. Now they put it on autopilot.

What took months now takes days. A fund tracking 100 new data sources would have assigned a team of engineers for a quarter. Today, one analyst does it in a week.

Reliability Wins Deals

Building the AI is about 30 percent of the work. The other 70 percent is deploying to production at scale: orchestration, data validation, error handling, retry systems, and effective human-in-the-loop tooling.

This 70% is where most AI projects fail. The hard part is deployment and ensuring reliability at production scale. The problem shifts from "Can we extract data from this page?" to "Can we extract prices from 10,000 different pages, validate every field, and maintain 99% accuracy daily?"

Achieving this reliability requires more than SOTA models. It needs orchestration to handle different site structures, automated validation to catch errors before they corrupt downstream systems, and tools that let domain experts fix issues without engineering support.

Most in-house initiatives fail here. Not because the AI doesn't work, but because making it production-ready requires infrastructure that takes months to build and perfect.

Built-in Compliance Is Non-Negotiable

Compliance teams can set automated controls such as blocking sanctioned countries, banning PII collection, enforcing robots.txt and captcha policies, and managing source allow/deny lists. Everything is audited.

Enterprise web scraping requires compliance from day one. Financial firms need audit trails showing who collected what data, when, and from where. They also want automated compliance controls that are enforced programmatically, not through manual reviews.

Without built-in compliance, every scraping request becomes a back-and-forth with legal. An analyst who needs data waits days for approval. Compliance teams review requests case-by-case instead of setting policies once.

Modern systems embed these controls at the platform level. Compliance sets rules and the system enforces them automatically, and every extraction is logged. This shifts compliance from a bottleneck to infrastructure.

Read the full conversation where Dan Entrup digs deeper into what agentic scraping means for the future of alternative data: Read the Interview →

Watch the product demo that Dan references in the interview to see these concepts in action.