Website to structured data: the missing API for AI applications

Your AI agent needs to extract pricing from a competitor's website. Or pull company info from a LinkedIn page. Or read product specs from an e-commerce listing. The traditional approach: scrape the HTML, parse the DOM, extract text, feed to an LLM, parse the response.

That's four failure points for something that should be one API call.

Why web scraping fails for AI agents

The scraping → extraction pipeline has fundamental problems:

• JavaScript rendering: Most modern sites load content dynamically. requests.get() returns empty shells. You need a headless browser (Playwright, Puppeteer) — another dependency to manage.
• Anti-bot protection: Cloudflare, reCAPTCHA, rate limiting. Your scraper breaks the moment the site updates its protection.
• HTML parsing is fragile: CSS selectors break when the site redesigns. Class names are often auto-generated (css-1a2b3c). One layout change breaks your extractor.
• No schema enforcement: You scrape text, feed it to an LLM, and hope the response matches your expected format. No confidence scores. No type enforcement.
• Maintenance cost: Every site you scrape is a maintenance liability. Sites change weekly. Your scrapers need constant updates.

The alternative: schema-based web extraction

Instead of scraping HTML and parsing it yourself, define what you need and let an API handle the rendering, extraction, and typing:

result = client.extract(
    file="https://competitor.com/pricing",
    schema={
        "plans": {
            "type": "array",
            "description": "All pricing plans with name, price, and features"
        },
        "free_tier": {
            "type": "boolean",
            "description": "Is there a free plan available?"
        },
        "enterprise_contact": {
            "type": "boolean",
            "description": "Does enterprise require contacting sales?"
        }
    }
)

The API handles:

• Full JavaScript rendering via headless Chromium
• Waiting for dynamic content to load
• Reading the visual page (not just HTML — sees the rendered layout)
• Extracting typed data matching your schema
• Returning confidence scores per field

No Playwright. No CSS selectors. No HTML parsing. No maintenance when the site redesigns.

Use cases for web extraction

Competitor monitoring

schema = {
    "pricing_plans": {"type": "array", "description": "Plan names and prices"},
    "new_features": {"type": "array", "description": "Recently announced features"},
    "positioning": {"type": "string", "description": "How they describe their product"}
}

Lead enrichment

schema = {
    "company_size": {"type": "string", "description": "Employee count or range"},
    "industry": {"type": "string", "description": "Primary industry"},
    "tech_stack": {"type": "array", "description": "Technologies mentioned on site"},
    "funding": {"type": "string", "description": "Latest funding round if mentioned"}
}

Product research

schema = {
    "specs": {"type": "object", "description": "Product specifications"},
    "price": {"type": "number", "description": "Current price"},
    "availability": {"type": "string", "description": "In stock, pre-order, etc."},
    "reviews_summary": {"type": "string", "description": "Overall review sentiment"}
}

Content aggregation

schema = {
    "article_title": {"type": "string"},
    "publish_date": {"type": "string"},
    "key_points": {"type": "array", "description": "Main takeaways"},
    "author": {"type": "string"}
}

URL to markdown: when you need the full content

Sometimes you don't need specific fields — you need the full page content as clean text. The /md/{url} endpoint converts any URL to markdown:

# Convert any URL to clean markdown
GET https://dev.thedrive.ai/md/https://example.com/blog/post

# Returns clean markdown with headers, lists, links preserved
# JavaScript rendered, ads stripped, navigation removed

Use this when you need full-text content for RAG, summarization, or feeding into an LLM context window.

Files and websites: same API, same schema

The power of a unified file intelligence API: the same schema works on PDFs, spreadsheets, and websites. Your agent doesn't need separate tools for documents vs. web pages:

# Same schema, same API, different sources
schema = {
    "revenue": {"type": "number"},
    "growth_rate": {"type": "number"},
    "employee_count": {"type": "number"}
}

# From a PDF filing
filing = client.extract(file="10k.pdf", schema=schema)

# From a website
website = client.extract(file="https://company.com/about", schema=schema)

# Cross-reference both
comparison = client.cross_analyze(
    files=["10k.pdf", "https://company.com/investors"],
    schema={"claims_match": {"type": "boolean",
        "description": "Do website claims match the SEC filing?"}}
)

One API handles both. Your agent doesn't need to know if it's dealing with a file or a URL — the interface is identical.

Pricing

Website extraction costs 5 credits per URL ($0.05) — includes JavaScript rendering, content extraction, and schema-based output. Compare that to maintaining Playwright infrastructure, proxy rotation, and anti-bot handling.

Try it now — paste any URL and define a schema. See structured data come back in seconds.