I'm building an internal research tool and need structured extraction from web pages

Sure — I can help with structured extraction from web pages.

A good pattern is:

1. Fetch the page content
2. Extract the relevant fields
3. Normalize into a schema
4. Return JSON

If you want, I can help with any of these in a few ways:

Options I can provide

• A scraping/extraction plan for a specific site
• Python code using requests, BeautifulSoup, lxml, or playwright
• JavaScript/Node code using fetch, cheerio, or playwright
• A JSON schema for the fields you need
• An LLM-based extraction prompt that converts page text/HTML into structured output
• A hybrid approach: deterministic parsing first, LLM cleanup second

Typical extraction workflow

For each page, you might collect:

• url
• title
• published_date
• author
• summary
• main_content
• entities / tags
• tables / list items
• source_metadata (site name, crawl time, language)

Example output format

{
  "url": "https://example.com/article",
  "title": "Example Article",
  "published_date": "2026-10-04",
  "author": "Jane Doe",
  "summary": "Short summary here.",
  "main_content": "Extracted body text...",
  "tags": ["research", "news"],
  "source_metadata": {
    "site": "example.com",
    "extracted_at": "2026-10-04T12:00:00Z"
  }
}

If you want an LLM extraction prompt

A common prompt pattern is:

• Provide the page HTML or cleaned text
• Specify the schema explicitly
• Require valid JSON only
• Instruct the model to use null when a field is missing

Example:

Extract the following fields from the page content and return valid JSON only:
- title
- author
- published_date
- summary
- key_points

Rules:
- If a field is missing, use null.
- Do not invent data.
- Output only JSON.

If you share:

1. the website/page type,
2. the fields you need, and
3. your preferred stack (Python/JS/etc.),

I can draft a concrete extractor for you.