Yekaliva: One of the First Self-Learning Chatbots
This is where Chennai based company called PositiveNaick Analytics, comes to the aid of organizations with its brain child “Yekaliva - First Self-learning AI chatbot”. Yekaliva is one of the leading no-code conversational bot platforms. It helps businesses make more human conversations with their customers. Since customers these days want immediate response from the customer representatives, Yekaliva is the best chatbot that would help in achieving this by allowing customers to interact naturally with the client websites. Yekaliva leverages knowledge graph and context level graph that combine intent databases for customer queries and external data synonyms, homonyms and antonyms to accurately converse with the customers. “We follow the Lean Startup strategy in developing our company and we are the first start-up in the country to establish AARC (Active Analytics Research Centre),” says Damodharan Padmanaban, Founder, PositiveNaick Analytics.
Customize, Connect, and Integrate Anywhere
Yekaliva can be customized by organizations to reflect their website and business. In addition to this, it can be easily integrated with social media channels such as Facebook, Skype and Slack etc. Yekaliva facilitates in monitoring all chats on a single platform and can be seamlessly integrated with legacy systems with no code required. This chatbot is built with enterprise-grade security features that can be customized, and it connects with most of the popular CRM software using Zapier
Six Combinational Technology models
Yekaliva is a highly accurate AI powered chatbot that is backed by six avant-garde technology models. First is the Streaming Machine Learning Model, with the classic NLP and statistical functions to classify and segment intents in order to give a structure to score keywords and searches. Secondly, to match queries with intents in the database, Yekaliva has implemented Bidirectional Neural Model to convert words into vectors and simplify it into a matrix graph. Thirdly, the Graph Neural Model is implemented to combine vectors and enrich data relationship in a matrix.
Yekaliva’s Entity Label Graph identifies distinct words in a sentence, and with answer deviation, the graph maps these words and filters metaphors in questions. The Logical Graph is used to represent NLP data flow through a series of computations in terms of relational and logical dependencies between individual hyponyms and hypernyms. Lastly, the core Active Knowledge Graph is built with intent databases and external data, to facilitate data enrichment and collecting data behind keywords, which also helps in providing accurate answers to users.
Moving forward, the company is planning to develop an innovative platform called Express Chatbot (“Paste your WebsiteLink & the Bot is ready in minutes)”, where the developers have to just paste the website link on the platform and the bot will be ready to use. In addition to this, the Yekaliva team is also working on minimizing the bot creation time from 3 minutes to 30 seconds.
Yekaliva is a highly accurate AI powered chatbot that is backed by six avant-garde technology models. First is the Streaming Machine Learning Model, with the classic NLP and statistical functions to classify and segment intents in order to give a structure to score keywords and searches. Secondly, to match queries with intents in the database, Yekaliva has implemented Bidirectional Neural Model to convert words into vectors and simplify it into a matrix graph. Thirdly, the Graph Neural Model is implemented to combine vectors and enrich data relationship in a matrix.
We follow the Lean Startup strategy in developing our company and we are the first start-up in the country to establish AARC (Active Analytics Research Centre)
Yekaliva’s Entity Label Graph identifies distinct words in a sentence, and with answer deviation, the graph maps these words and filters metaphors in questions. The Logical Graph is used to represent NLP data flow through a series of computations in terms of relational and logical dependencies between individual hyponyms and hypernyms. Lastly, the core Active Knowledge Graph is built with intent databases and external data, to facilitate data enrichment and collecting data behind keywords, which also helps in providing accurate answers to users.
Moving forward, the company is planning to develop an innovative platform called Express Chatbot (“Paste your WebsiteLink & the Bot is ready in minutes)”, where the developers have to just paste the website link on the platform and the bot will be ready to use. In addition to this, the Yekaliva team is also working on minimizing the bot creation time from 3 minutes to 30 seconds.
