Proprietary AI

Conversational AI that achieves resolutions in a way that feels personal

Inbenta’s explainable AI platform uses Natural Language Processing, Neuro-Symbolic AI and a Lexicon to engage customers in thoughtful conversations.

Automate Customer Conversations with Accuracy

Trained over a decade using billions of customer interactions, Inbenta’s Conversational AI platform achieves a 95% correct answer rate that continuously improves with each interaction

Easy to Implement, Easy to Maintain

Ready to use out-of-the box with zero data training required, lowering the cost and effort of maintenance.

Highly-Configurable and Interoperable

Able to adjust to different industries, business areas and use cases, across platforms and integrations.

Engage Customers Across the World

Inbenta’s multilingual Natural Language Processing works in 35 languages and automatically adjusts to different contexts, lexicons, and even the handling of misspelled words.

Explainable AI that is accurate and safe to use

Natural Language Processing

Natural Language Processing interprets language by breaking queries into words, deriving context from the relationship between words, and structuring the data to extract the actual meaning or intent. Natural Language Processing is far superior to keyword processing, which can’t accurately understand the intent of queries.

Neuro-Symbolic AI

Neuro-Symbolic AI is the combination of machine learning and symbolic reasoning, linking the semantic relationships between words to understand the true context and meaning of queries. Unlike machine learning alone, which identifies patterns from thousands or millions of data points with no human logic (i.e., in a black box), Neuro-Symbolic AI’s logic is explainable and easy to understand.

Lexicon

Lexicon is a repository of millions of lexical (word) relationships that help our Conversational AI tools understand language. Inbenta’s Lexicon acts as a huge dictionary that contains hundreds of thousands of semantic relationships, specializing in common language knowledge (i.e., language used everyday), industry-specific language knowledge (i.e., language used in specific sectors), and customer-specific language knowledge (i.e.,language used in customer queries).