Industry-specific AI models embedded with domain-specific intelligence, data dictionaries & taxonomies, trained on thousands of user utterances to deliver human-like conversational experiences at scale.
Haptik’s NLP architecture is built on a combination of modules such as Language detection, ASR classification, Context Manager, that work in tandem with deep learning-based encoders to accurately understand natural language and handle user queries with higher precision.
Achieved the Highest global benchmark (EMNLP 2019 dataset) for identifying out of scope queries
In-built contextual spell corrector, adversarial training, context retention further fuel accuracy of the NLU
Users often ask queries with multiple intents, sometimes causing the IVA to break. In such scenarios, Smart Assist engages the customer with relevant recommendations closest to their query, driving them back into the conversation. It avoids routing the chat to a live agent—thereby saving costs and enhancing user experience.
Build high-quality IVAs that deliver a superior customer experience using Advanced Training Feedback.
This mechanism automatically detects and flags:
Pre-built industry AI models embedded with domain intelligence (ontology) for specific industries.
Communicate with your users in the language they’re comfortable with using Haptik’s Multilingual feature. Transcend beyond basic Google Translation and build truly powerful multilingual IVAs.
Perform Intent & query detection, answerability prediction & more, within a single task using Haptik’s open-source multi-task learning toolkit. Improve IVA accuracy with Spell-O, a contextual, deep domain knowledge specific spell corrector that understands users and helps the IVA to respond accurately despite spelling errors/typos in user queries.
Advanced NLU capabilities and deep-learning models to automatically detect user query repetition, negative feedback, broken user experiences, cuss words, and IVA repetition. Sentiment analysis helps deliver exceptional automated conversational experiences without undermining personalization in your brand voice.