Value Proposition

• 100% custom-made
• Chatbot platform independence: Lex, Luis, Dialogflow…
• Multilingual, 14 languages available. 
• Set up, scalable & adjustable 100% guarantee
• QA & improvement service included: global LiveCycle of the Bot
• Guaranteed accuracy 90%
• Bootstrapping: Reduce time to market
• Reduce customer service costs from day 1 
• Training data without privacy problems or manual errors 
• Create a custom bot from scratch or improve your current bot 

   

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MAIN TYPES OF BITEXT CUSTOM BOTS

• Customer Service BOTS
• Employees service BOTS (HR)
• Sales or pre-sales BOTS
• B2B BOTS

For different languages

Data Selection for Bot Training

From the hundreds of utterances Bitext can generate for a chatbot, a careful selection has to be made, because the amounts of utterances that common chatbot platforms can hold is small. That selection follows the following criteria:

• The quantitative limitations of the NLU engine of chatbot platforms
• The fact that some utterances can make one intent overlap with another one
• The balance needed between the number of total utterances and the number of intents, also depending on the platform
• A careful qualitative profiling of the language expected to be used by the users of the chatbot. This includes: language register (more colloquial or formal language, offensive language expected or not, spelling errors and mistakes…) and the expected users’ region (UK/US English, Spain/Mexico Spanish…)

In summary, Bitext selects the most appropriate utterances (and intents) to best adapt a generic NLU engine (like a chatbot) to a specific language, a vertical and a user profile.

Data Customization for any NLU Engine

Bitext has created a new paradigm for taking a general-purpose NLU engine and adapt it to a specific vertical or industry. 

This paradigm relies on a knowledge-transfer methodology that models the linguistic knowledge that configures a vertical and transfers it to a general NLU engine. This transfer is performed in several areas:  the language of the vertical (via dictionaries, grammars and ontologies), its contents (taken from public and private data sources, as FAQs of its main companies or logs obtained in previous experiences), and the linguistic profile of the expected users of the NLU engine (their region, their register, and the peculiarities of their language). Then we model that information into the specific NLU platform that is going to be used (Amazon Lex, MS LUIS, Dialogflow…)

All the knowledge used to perform this transfer is contained in our training and evaluation datasets.