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What is contentCrawler?

contentCrawler is an innovative automated tool that enables text searchability and improves storage efficiency for all documents within a repository. Operating autonomously without the need for manual intervention, it employs Optical Character Recognition (OCR) to convert image-based files, including scanned PDFs and images, into searchable PDFs, thereby enhancing productivity and ensuring adherence to compliance standards. Additionally, the tool is equipped with a compression feature that reduces file sizes, which results in lower storage and migration costs while preserving the integrity of the documents. It is compatible with multiple image formats like TIFF, BMP, GIF, EPS, JPG, and PNG, effectively transforming them into PDFs that contain an invisible text layer to improve search capabilities. Moreover, contentCrawler provides dual processing modes that allow for simultaneous handling of both new and legacy documents, ensuring comprehensive coverage across the entire document repository. Administrators can easily track the progress of OCR and compression tasks in real-time through the administration console's dashboard, which enhances oversight and efficiency in document management. This all-encompassing strategy not only ensures that organizations can fully leverage their document accessibility but also streamlines their overall management practices, ultimately leading to improved operational effectiveness.

What is HyperCrawl?

HyperCrawl represents a groundbreaking web crawler specifically designed for applications involving LLM and RAG, aimed at developing highly efficient retrieval engines. The main objective was to optimize the retrieval process by reducing the time required to crawl diverse domains. We introduced a variety of advanced methodologies to create a novel machine learning-oriented strategy for web crawling. Instead of sequentially loading web pages—comparable to waiting in line at a supermarket—the crawler requests multiple pages at once, similar to making several online purchases simultaneously. This approach effectively eliminates downtime, allowing the crawler to tackle other tasks concurrently. By maximizing concurrent operations, the crawler adeptly handles a multitude of tasks simultaneously, greatly speeding up the retrieval process in contrast to managing only a few tasks at a time. Additionally, HyperCrawl enhances connection efficiency and resource management by reusing existing connections, akin to choosing a reusable shopping bag instead of acquiring a new one with every transaction. This cutting-edge method not only refines the crawling procedure but also significantly boosts overall system performance, leading to faster and more reliable data retrieval. Furthermore, as technology continues to advance, HyperCrawl is poised to adapt and evolve, ensuring it remains at the forefront of web crawling innovation.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Docker
Google Colab
JavaScript
Jupyter Notebook
Python
React

Integrations Supported

Amazon Web Services (AWS)
Docker
Google Colab
JavaScript
Jupyter Notebook
Python
React

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Litera

Date Founded

2001

Company Location

United States

Company Website

www.litera.com/products/contentcrawler

Company Facts

Organization Name

HyperCrawl

Company Website

hypercrawl.hyperllm.org

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