Natural Language Processing
A natural language understanding engine
boosted for customer relations and pre-trained by sectors for unrivalled performance
NLU dedicated to Customer Relations
The result of a fruitful collaboration between the passionate linguists and expert engineers of the AI & Data Factory, our natural language understanding engine stands out for its very high level of performance.
With over 150,000 verbatims cleaned and categorized by our team of linguists, this tool is "fine-tuned" to the world of customer relations, and surpasses competing tools on the market in terms of accuracy.
Pre-trained NLU by business sector
Organized by industry sectors such as healthcare, assistance, banking or retail, our NLU models are trained on huge industry-specific text datasets pre-processed by our team of linguists to capture the nuances and language specific to each domain.
Complex applications and social context
In addition to responding to basic requests, our NLU engine is also capable of understanding complex queries and grasping the social context in which they occur. This ability is particularly valuable in sectors such as customer relations, where customers often express frustration or dissatisfaction.
By integrating this in-depth understanding of language and context, our callbots are able to provide more accurate and relevant responses, while taking into account the subtleties and social implications of linguistic interactions.
Zaion Clustering
As your needs evolve, so does our AI!
User requests to a customer service department are dependent on a global business and social context. That's why we've developed an algorithm to detect new trends: new words, unusual expressions and so on.
Technically, this algorithm is trained on raw data, without any human supervision. In the case of a bot, we use verbatims not understood by the bot to identify the words and phrases used. This can guide the bot's designer towards new intentions or entities to be modeled. In the case of semantic analysis of human-human conversations, this algorithm can be used to build classes grouping together the phrases and expressions most frequently used by your customers, giving you an in-depth analysis of their needs.
