I'm building an internal research tool to pull structured data from web pages at scale

That sounds like a great use case. I can help you design the pipeline, choose extraction strategies, handle failures, and think through scale/compliance.

A solid architecture usually looks like this:

1. URL ingestion

   • Accept a seed list, sitemap, RSS, search results, or queued tasks.
   • Normalize URLs and deduplicate.

2. Fetching layer

   • Use a crawler or HTTP client first for static pages.
   • Fall back to a headless browser for JavaScript-heavy pages.
   • Add retries, rate limiting, and per-domain politeness.

3. Content processing

   • Detect page type and extract the main content, metadata, and structured signals.
   • Parse HTML, JSON-LD, microdata, Open Graph tags, tables, forms, and embedded APIs.

4. Extraction layer

   • Define a schema for the data you want.
   • Use rule-based extraction where possible for reliability.
   • Use NLP/LLM extraction for flexible pages, but validate outputs against schema.

5. Normalization

   • Convert dates, currencies, units, names, and locations into canonical forms.
   • Resolve entities if needed.

6. Storage

   • Keep raw HTML, rendered DOM snapshots, extracted JSON, and provenance.
   • Store source URL, fetch timestamp, parser version, and confidence scores.

7. Quality control

   • Add checks for missing fields, duplicates, drift, and parse regressions.
   • Sample and manually review low-confidence outputs.

8. Operations

   • Observability: queue depth, fetch failures, extraction accuracy, latency.
   • Caching and incremental recrawls.

A few important design choices:

• Static vs rendered extraction: start with static HTML; only render when necessary.
• Rules vs ML/LLM: use deterministic parsers for known layouts, and flexible extraction for long-tail pages.
• Provenance: always retain the source snippet or selector used for each extracted field.
• Compliance: respect robots.txt, site terms, rate limits, and legal/privacy constraints.

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

• propose a reference architecture
• design a data schema
• compare crawler stacks in Python/Node/Go
• build a page-to-JSON extraction pipeline
• draft a scalable system design for millions of pages