Using Artificial Intelligence to Make Conversations More Human

Yekaliva's intelligent chatbot uses groundbreaking AI to reimagine how a chatbot works. Rather than responding to predefined strings with generic answers, Yekaliva uses AI and machine learning to naturally respond to every question your customers have.

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Machine Learning

The Streaming Machine learning model, with classic NLP and statistical functions, classifies and segments intents. This gives a structure to score keywords and searches.

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Bidirectional Neural Model

The Bidirectional Neural model converts words into vectors and simplifies it into a matrix graph. The neural classifier matches queries with intents in the database.

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Graph Neural Model

The Graph Neural Model combines vector and enriched data relationships in a matrix. This keeps the accuracy of answers high.

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Entity Label Graph

Our Entity Label Graph is an entity that identifies distinct words in a sentence. With answer deviation, the graph maps these words and filters metaphors in questions.

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Logical graph

The Logical Graph represents NLP data flow through a series of computations in terms of relational and logical dependencies between individual hyponyms and hypernyms.

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Active Knowledge graph

The Core Knowledge Graph is built with intent databases and external data. This helps data enrichment and collecting data behind keywords. This helps give accurate answers.

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