How do AI companies build web crawlers?

AI companies that train search, recommendation, or language models need a steady stream of fresh pages, feeds, and files from the public internet. That work is done by crawlers: distributed systems that fetch content, discover new URLs, and revisit known pages to detect updates. Building a crawler that runs continuously is less about a single “bot” and more about orchestration, scheduling, data hygiene, and resilience.

Starting points: seeds and discovery goals

Crawling begins with seed URLs: curated lists of trusted sites, public directories, XML sitemaps, known RSS/Atom feeds, and URLs retained from previous crawls. Teams define discovery goals early, such as:

• Broad coverage across many domains
• Deep coverage within a smaller set of sites
• Freshness for frequently updated sources
• Focus on specific formats like HTML, PDF, documentation, forums, or code

These goals determine how URLs are prioritized, how often pages are revisited, and which content types are retained.

The fetch pipeline: from URL to stored document

Production crawlers are typically split into independent stages that scale horizontally:

1. URL normalization Canonicalize schemes, trailing slashes, default ports, and known tracking parameters to reduce duplicates.

2. Robots and policies Check robots.txt rules, crawl-delay hints, and internal allow/deny lists before any request is issued.

3. Fetching Perform HTTP requests with timeouts, retries, compression, conditional headers, and strict content-size limits.

4. Rendering (optional) Some pages require JavaScript execution. Headless browsers are used selectively due to cost and complexity.

5. Parsing and extraction Extract clean text, metadata, structured data (such as JSON-LD), and outgoing links.

6. Storage Persist raw snapshots and processed representations to object storage and indexing systems.

Each stage is usually implemented as a worker pool or microservice to allow independent scaling and failure isolation.

Constant discovery: how new URLs appear

Crawlers continuously expand their URL set through:

• Link extraction from fetched pages
• XML sitemaps and sitemap indexes with last-modified hints
• RSS and Atom feeds for rapid discovery of new content
• Redirects and canonical tags to identify preferred URLs
• Public datasets and curated URL lists that are validated before crawling

Discovery never stops because every fetch potentially introduces new crawl targets.

The URL frontier: scheduling, prioritization, and politeness

The URL frontier is the prioritized queue of URLs waiting to be fetched. It balances:

• Priority signals such as link importance, feed recency, or historical value
• Freshness models that predict how often a page changes
• Host-level throttling to avoid overloading individual sites
• Fairness constraints so large sites do not consume all capacity

A common design uses per-host queues coordinated by a global scheduler that enforces politeness and rate limits.

Detecting change and reducing duplicate work

To avoid wasting bandwidth and storage, crawlers apply several techniques:

• Conditional requests using ETag and If-Modified-Since
• Content hashing to detect exact duplicates
• Near-duplicate detection using shingling or embeddings
• Aggressive canonicalization to collapse infinite URL spaces

These measures improve dataset quality and keep crawl costs manageable.

Beyond first-party crawlers: multiple data sources

Large training pipelines rarely depend on a single crawler. Data often comes from a combination of:

• Public crawl corpora
• Licensed datasets from publishers or data providers
• Direct partnerships and content agreements
• Open repositories with clear licenses
• User-provided or customer-owned data under explicit consent

Crawlers usually complement these sources by improving freshness or discovering new public material.

Avoiding private, gated, and sensitive content

Training-oriented crawlers are designed to avoid content behind logins, paywalls, or private user accounts. Authenticated pages, dashboards, private forums, and personal messaging systems are typically excluded entirely.

Many teams also block whole categories of sites based on risk, quality, or policy considerations.

Extraction, normalization, and quality filtering

Before content is considered usable, it undergoes extensive filtering:

• Boilerplate and navigation removal
• Language and encoding detection
• File-type–specific parsing for PDFs and documents
• Malware and exploit scanning
• Spam and SEO-farm detection
• Size, format, and content-type enforcement

This ensures downstream systems receive clean, consistent representations rather than raw HTML artifacts.

PII handling and content safety

Personally identifiable information is managed through layered protections, including rule-based filters, classifiers, and similarity detection. Depending on policy, content may be redacted, transformed, or excluded entirely.

Some organizations adopt conservative defaults, excluding entire classes of user-generated content to reduce risk.

From crawl to controlled datasets

The crawler’s job is not to collect everything, but to feed a controlled, reviewable, and repeatable pipeline. Training datasets are versioned, filters are auditable, and changes in policy or licensing can trigger retroactive removals.

At scale, crawling is as much a governance and quality challenge as it is a distributed systems problem.