Deep Scraper

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Deep Scraper: AI-powered, no-code web data extraction. Get human-like insights & structured data from any website, reliably and fast.0
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What is Deep Scraper?

Deep Scraper is an intelligent, AI-powered browser extension designed to automate web data extraction and browser flows with unparalleled reliability. It solves the critical problem of broken, incomplete data extraction by browsing, understanding, and inferring insights from complex websites just like a human user. This powerful, code-free solution is essential for professionals—from sales teams and analysts to recruiters and market researchers—who require fast, accurate, and structured data without the maintenance overhead of traditional scrapers.

Key Features

Deep Scraper transforms complex web data into actionable, clean tables using natural language, ensuring speed and reliability where traditional tools fail.

  • 🧠 Human-Like Browsing & Automation: Unlike standard scrapers that rely on fixed selectors, Deep Scraper navigates dynamic websites, interacts with buttons, handles infinite scrolls, and bypasses common roadblocks (like JavaScript content) seamlessly. This ensures complete data capture even on the most modern sites.
  • 🗣️ Prompt-Based Extraction: Define exactly the data you need using plain English (e.g., "Extract speaker name, role, and LinkedIn from this page"). This eliminates the need for complex configuration, XPath, or CSS selectors, allowing anyone to run sophisticated scraping jobs instantly.
  • 💡 Contextual Insight Inference: Deep Scraper doesn't just pull raw text; it understands context. It can classify, summarize, and enrich data automatically—for example, taking raw text like "founded 2018, Series B" and structuring it into separate fields for Company Stage and Founding Year.
  • 🖼️ Image and Link Data Capture: Leverage advanced OCR and processing capabilities to extract relevant information embedded within images or linked documents, going far beyond the limitations of text-only scraping tools.
  • 🌐 Multi-Site Automation (Batch Jobs): Queue up multiple URLs and run concurrent browser extraction jobs with a single command. This capability delivers fast throughput and allows professionals to build comprehensive datasets across numerous sources efficiently.

Use Cases

Deep Scraper is built for speed and efficiency, delivering targeted data directly into your workflow across diverse professional domains.

For Sales Teams & SDRs: Targeted Prospecting

Quickly build highly targeted prospect lists and enrich existing leads. Sales professionals can use a simple prompt to extract contact details, roles, and company insights from LinkedIn, Sales Navigator, or event attendee pages, cutting prospecting time by up to 75%.

For Investors & Analysts: Market Intelligence

Gain faster market insights by automating the collection of financial metrics, growth signals, and competitor data. Analysts can scrape startup databases, track company announcements, and classify industry trends across multiple data sources in minutes, transforming what used to take days of manual effort.

For Recruiters: Automated Candidate Sourcing

Dramatically improve the efficiency and quality of candidate sourcing. Recruiters can automate the extraction of skills, experience data, and job history from batches of LinkedIn profiles or job boards, allowing them to qualify and filter candidates faster and cast a wider net for specialized talent.

Conclusion

Deep Scraper delivers the speed and accuracy modern professionals demand, transforming web data extraction from a technical burden into a simple, prompt-driven process. By leveraging human-like AI to handle complexity, it ensures you receive clean, structured data quickly and reliably, allowing you to focus on analysis rather than maintenance.

Explore how Deep Scraper can save you valuable hours weekly and provide the competitive data edge you need.


More information on Deep Scraper

Launched
Pricing Model
Paid
Starting Price
$47 /month
Global Rank
Follow
Month Visit
<5k
Tech used
Deep Scraper was manually vetted by our editorial team and was first featured on 2025-12-14.
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