Brands can now build smarter, more predictive virtual assistants that leverage on data to solve specific business challenges.
Proprietary hybrid A.I technology with built-in, industry-specific capabilities including B2C Retail, FMCG, Hospitality and Automotive Industries.
Natural Language Understanding
Define annotated data and use the entity module to equip the AI model with domain attributes needed to build quality data for complex business problems. The algorithm is also designed to provide precise intent detection.
Always learning and improving
Machine actively learns from user's responses and get smarter over time. For out-of-scope questions, the platform enables improvement of the AI model through a semi-supervised machine learning techniques to optimize the judgments and accuracy.
A.I & Human Collaboration
When a new message is received, bot suggests the most relevant answers to the agent. If the prediction falls below the confidence threshold, the agent can personalize the answer and bot learns from every interaction.
Built-in identifier that can recognize language of the message which the machine can analyze and translate them accurately to the user's native language. Proprietary South East Asia languages packages are designed to detect and understand slangs and short forms used in the local context.
AiChat platform can perform industry-specific entity extraction and powers major labeling strategy for popular B2C domains such as Retail, FMCG, Automotive, Hospitality, Pharmaceutical, Education and Financial Institutes enabling your business to speed up the A.I model training by at least 100x.
Bot can understand the context and emotion of the customers right from the beginning, helping them to select the best course of action. This provides insights about the customers and determines if the cases should be escalated to a human agent.
Our technology automatically suggests corrections that are perfectly adapted to the industry context. Bot is trained to handle various spelling errors such as word breaks, slang, typo of names and brands and homonyms. This will help to improve the accuracy of downstream tasks in the NLP pipeline.