Architecture

Architecture

Architecture of CoRover Platform is Modular, Secure, Reliable, Robust, Scalable and Extendable. Our innovation in technology is the most unique property, which makes us a differential provider in the market.

CoRover IVR Architecture
CoRover IVR Architecture
CoRover SM Architecture
CoRover SM Architecture
CoRover Disaster Recovery
CoRover Disaster Recovery
On-Premise
On-Premise
On-Premise Services
On-Premise Services
Data Flow Diagram
Data Flow Diagram
Network Topology
Network Topology
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CoRover Cognitive AI framework

CoRover Cognitive AI framework, which uses our patented AI in the most innovative way, comes with various components, such as, AI auto-suggestion, AIML, NLP, ML, deep learning and proprietary, cognitive AI framework, which makes our product very easy to use. User can initiate queries using various interfaces. Our Chatbot takes these queries and passes it through various layers of our framework as mentioned below:

AI Auto-Suggestion

This feature compares the user’s questions and maps it with the predefined questions. If the question matches the predefined questions, then the chatbot responds with the answer.

AIML

In this level, it simplifies the words in the question to interpret more efficiently. Message enrichment and correction also happens here. The AIML interpreters can provide

pre-processing functions to expand abbreviations, remove misspellings, etc
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NLP

This level applies algorithms to understand the meaning and structure of sentences. Syntactic and semantic analysis are two main techniques used with

natural language processing. Normalization, Noise removal, StopWords removal, Stemming, Lemmatization Tokenization and more, happens here.
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Unsupervised algorithm

This layer uses historical chat log data (transcripts), without the need for any human labelling.

ML and DL

In this layer the feedback data and question are collected, and the responses are updated

through machine learning abilities.
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Deep multitasking learning

This layer brings the humankind of learning to the framework, and enables focusing on

each word, sentence and previous sentences to drive deeper understanding all at the same time. With the help of the above features, the chatbot will either provide the appropriate response or project a standard reply to modify the query or let users choose from the various options or open a live chat, where the customer can directly chat with customer care employee.
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