We have experience in creating closed domain bots for long conversations where a number of questions can be asked from the Chabot to reach a final conclusion to deliver proper recommendation results. This engine makes use of blocks such as feature extraction methods such as TFIDF (Term frequency* Inverse document frequency) vectorizer, supervised classification methods like SVM (support vector machine), NLTK (Natural Language Toolkit Library) for text preprocessing (Lemmatizing, removing punctuations, stop words), entity extraction (Bag of word approach), cache for collecting the entities extracted from natural language input given from user side, session management (Unique chat box for each user).
Also using tools such as Api.ai by Google for intent identification of user query and entity extraction. Libraries such as textblob can be used for language translation module so that the bot can be used to communicate in any languages with the customer. Other libraries such as keras, Tensorflow are capable to build neural conversational model for question answering bot provide if there is huge amount of conversational data is available. Stanford’s NER can also be used for entity recognition.
Chatbots use a combination of automatic speech recognition (ASR), voice biometrics, speech synthesis, to identify users need and provided appropriate responses in a seamless manner with minimum delay.
We develop bots for various domains including,
• Banking bots
• Healthcare bots
• E-commerce chatbots
• Telecom bots
• Flight booking chatbots
• Food ordering chatbots
• Retail chatbots