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Our product

Knowledge professionals spend hours every day matching and categorizing text-based data. Our NLP-powered tool helps combine pieces of textual content based on their semantic affinity, in seconds.

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Whether you need to classify companies according to insurance risk levels or categorize and group documents in your knowledge base, our AI-based technology could be a time-saver. We employed natural language processing algorithms and machine learning to match and combine pieces of textual content based on a degree of their semantic similarity. For each document or text piece, we identify a subject, main topics, key words and phrases that form its semantic core, and then match the identical and synonymously related units. As a result, you get a fully automated content matching tool that helps you in text-centered and adjacent operations.

Who can benefit from the technology?

  • Publishers
  • E-learning providers
  • Media agencies
  • Lawyers
  • Insurers (risk categorization)
  • Patent examiners
  • University professors
  • Researchers
  • Freight forwarders
Contact our team to learn how the technology could be applied to your case via the form bellow.

About the company

Silk Data was founded by PhD titled experts with more than 55 years of combined experience, to deliver on its mission of helping companies harness the full potential of machine learning and AI. Now we are the team of 30+ world-class specialists led by 3 PhD-titled experts that work jointly on creating and supporting the most advanced AI and Machine Learning-powered technology for text processing.

Silk Data is all about <experience + youth> which is not a cliché but our winning formula. Our experienced team and in-depth expertise ensure exceptional quality, reliability, and security. Our young sprit and startup-like culture, on the other hand, promote creativity and foster innovations.

The products and solutions engineered by Silk Data are used by all types of entities, from startups to enterprises, in Germany, Switzerland, Austria, Israel, Russia, South Africa, and etc.

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