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Corporate R&D

 Parity provides a complete suite of software and services to enhance collaborative research for organizations with significant scientific and technical R&D efforts. In such organizations, research content is often spread out across geographical and functional boundaries in a form not easy to access. It is not only important to integrate this content and facilitate search, it is equally important to transform this content to support knowledge discovery, collaboration and problem solving.

Parity serves these needs by providing solutions built on top of our Intelligent Knowledge Network technology, deriving profiles and relationships for concepts, IP, people, companies and other entities, leveraging documents from internal, licensed, and public content sources.

Key Solutions

Digital Research Repository
Transform your knowledge assets into a powerful collaborative research repository. Enable researchers to quickly and thoroughly research the information they need to generate novel results and intellectual property.

Expertise Network
Build a network of subject-matter experts and expertise. Enable rapid location of subject matter experts for solving mission-critical business needs, and building of communities of practice and interest.

Value Add Solutions: R&D content processing

  • Content Preparation and Migration: Cost-effective migration to rich digital content.
  • Content Integration: Provide a more complete view of relevant content by integrating core digital library content with additional internal and external content such as patent and product data.
  • Reference Processing and Linking: Parity provides the industry's most accurate solution for bibliographic reference linking among internal documents, external research, and patents.
  • Ontology Development and Classification: Organize and categorize your content into rich domain-specific ontologies. Provide your researchers with powerful new ways of accessing and navigating content in the digital repository.

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Press Release

Elsevier Science

Elsevier Chooses Parity for Text Processing