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Industrialize training data production for any voice-controlled device, chatbot or IVR using artificial training data

 

 

Speed up the deployment of new domains and languages by using artificial training data.

 

 

See why automatic training is better than manual training:

  • Recognize a user´s intent in any platform.

  • Improve up to 90% accuracy.

  • Identify the sentiment of client reviews.

 

Get ahead and book a demo with us!

 

 

 

Reduce Time to Market

Data Protection

Easy integration

Multilingual

Taking the Burden out of Creating Chatbot Training Content

We help you understand your customers either if you do not have any existing training data or need to increase your accuracy or expand to other languages with consistency. Our artificial training data is generated by combining real-world sources with our unique Natural Language Generation technology. Our pre-tagged sentences include a wide range of formats to easily integrate with your favorite platform.

Book a demo with Bitext

Do you need other languages?

No problem! Our training datasets are currently available in 9 languages, so we can quickly create large, custom chatbot training datasets in different languages. Just book a demo and we will accomodate your preferences.

Make your bot fully conversational

Our data covers linguistic phenomena such as negation and coordination. We can provide the data necessary, which can range from only a few sentences per intent to thousands of them!

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Gartner Reports say...

Bitext is currently at the forefront of technology, being mentioned in 15 Gartner reports between 2018-2019 

Bitext can improve the performance of almost any conversational engine and project.

End users frustrated with the performance or complexity of their chatbot developments will be interested in how Bitext can improve intent matching confidence and reduce development time

Companies such as Bitext use semantic technologies to generate multiple variants from a seed intent. These intent variations are used to train AI engines, resulting in more user requests being correctly mapped, and reducing the burden of developers.