I'm building a lead enrichment pipeline and need bulk site crawling

Absolutely — for a lead enrichment pipeline, “bulk site crawling” usually means:

1. taking a list of domains or URLs,
2. visiting each site at scale,
3. extracting structured signals like company name, description, contacts, socials, tech stack, hiring, location, and pricing,
4. normalizing/deduping it into your CRM or warehouse.

A solid bulk crawling setup typically has these components:

1) Input handling

Start with a seed list like:

• example.com
• https://example.com/about
• example.com/contact

Normalize to canonical domains and decide whether you crawl:

• only homepage + a few key paths, or
• broader site discovery via sitemap/internal links.

2) Crawl strategy

For lead enrichment, you usually want a selective crawl, not a full web crawl.

Common approach:

• Fetch homepage
• Discover and prioritize pages like:
  • About
  • Contact
  • Team
  • Pricing
  • Blog
  • Careers
  • Security
  • Terms/Privacy
• Use sitemap.xml when available
• Limit depth and page count per domain

Good defaults:

• max 10–30 pages/domain
• same-domain only
• 1–3 retries
• respect robots.txt if needed for compliance

3) Extraction targets

Useful signals to extract:

• company name
• tagline / description
• industry keywords
• emails and phone numbers
• social links: LinkedIn, X, GitHub, YouTube
• physical address / HQ location
• funding / hiring indicators
• pricing plan URLs
• technologies used
• team names / founders
• metadata: title, meta description, canonical, OpenGraph

4) Rendering approach

You’ll likely need a mix of:

• plain HTTP fetch for fast static sites
• headless browser rendering for JS-heavy sites

A common pattern:

• try requests/httpx first
• if page is empty or script-heavy, fall back to Playwright/Puppeteer

5) Scale and orchestration

For bulk crawling, use:

• job queue: Celery, BullMQ, Sidekiq, SQS
• workers: containerized crawlers
• rate limiting per domain
• concurrency controls
• caching and deduplication

Suggested pipeline:

1. ingest domains
2. normalize and dedupe
3. enqueue crawl jobs
4. fetch/discover pages
5. extract structured data
6. enrich with third-party APIs if needed
7. store in DB / warehouse
8. score lead quality

6) Storage schema

Store both raw and normalized data.

Example entities:

• companies
• pages
• contacts
• social_profiles
• technologies
• crawl_runs

Keep:

• raw HTML/text snapshot
• extracted fields
• crawl timestamp
• status/errors
• confidence scores

7) Compliance / safety

Important for lead enrichment:

• respect robots.txt and site terms where applicable
• avoid collecting sensitive personal data without a lawful basis
• honor opt-outs and suppression lists
• rate-limit aggressively
• identify your crawler with a clear user-agent

8) Suggested tech stack

A practical stack:

• Python: httpx, BeautifulSoup, lxml, Playwright
• Queue: Celery + Redis, or SQS
• Storage: Postgres + S3
• Parsing: trafilatura, readability-lxml, extruct, tldextract
• Enrichment: Clearbit-style APIs, builtwith-like tech detection, domain email discovery

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Example crawling flow

For each domain:

1. Check robots.txt
2. Fetch homepage
3. Extract internal links
4. Prioritize key pages
5. Fetch up to N pages
6. Parse:
   • title/meta
   • schema.org JSON-LD
   • contact info
   • social links
7. Save structured result
8. Score lead

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If you want, I can help with any of these next:

1. Design a full architecture for bulk site crawling
2. Write a Python crawler prototype
3. Build a data schema for lead enrichment
4. Add JS rendering + proxy/rate-limit handling
5. Create an end-to-end pipeline for millions of domains

If you want code, tell me:

• your preferred language,
• expected scale,
• whether sites are mostly static or JS-heavy,
• and what enrichment fields you need.