Why opting for an NLP Middleware is important for your business

According to Gartner, companies working with AI should stop combining training and learning activities, one of the reasons being the slowing down of conversational agents’ learning processes. The most recommended course of action for data managers is exploring emerging middleware tools that allow them to use the same training data set for multiple AI service providers.

 

The importance of NLP Middleware

According to Gartner analyst Anthony Mullen, “middleware is becoming a necessity to deal with the breathtaking pace of AI technology”, as stated in the report Cool vendors in AI Core Technologies 2018 where Bitext is considered a pioneer in this new AI and NLP approach.

There are many benefits, both financial and practical, to adopting a middleware solution for your AI engine, the most important being vendor independence. Using middleware solutions easily integrated into any system allows companies to have a wide range of choices in selecting an AI vendor. At Bitext, we’re delivering a specific NLP middleware that enables enterprises to be technology-agnostic in the choice of an NLP vendor, as Gartner affirms.

There is no denying that Bitext's NLP middleware platform greatly enhances the performance of any AI engine and project. In Gartner’s report ‘Clarify Strategy and Tactics for Artificial Intelligence by Separating Training and Machine Learning’, Bitext is also seen as an emerging AI middleware provider for NLP chatbot systems.

Playing the ‘middleman’, Bitext technology enables companies to choose between a wide variety of vendors so that the training of their AI system improves easily. This results in better intent-matching due to the hybrid NLP approach. Bitext ends many linguistic challenges from downstream platforms, which boosts continuous improvement in the workflow when developing AI solutions.

There is no doubt, after all, that Bitext is currently at the forefront of technology since it has been mentioned in no less than 20 Gartner reports in the previous years:

  • Revolutionize Product information Management by Means of Disruptive AI.
  • Market Guide for Social Analytics Applications
  • Clarify Strategy and Tactics for AI by Separating Training and Machine Learning
  • Cool Vendors in AI Core Technologies
  • Market Guide for Text Analytics
  • Market Insights: Chatbot and Virtual Assistant Adoption Will Only Progress by Delivering Value and Trust
  • Hype Cycle for Artificial Intelligence
  • Hype Cycle for Human-Machine Interface
  • Hype Cycle for Mobile Device Technologies
  • Hype Cycle for the Digital Workplace
  • Predicts 2018: Mobile Apps and Their Development

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