Chatbot Training Data
Chatbots have revolutionised the online interaction landscape. They are fast and efficient in providing information to customers. Unlike traditional ways where users had to hang on to a hold message before customer executives addressed their grievances, chatbots enable users to get straight to the point. While chatbots have been widely accepted and have come as a positive change, they don’t just come into existence fully-formed or ready to use. Instead, before being deployed, chatbots need to be trained to make them accurately understand what customers are saying, what are their grievances and how to respond to them. Chatbot training data services offered by SunTec.AI enable your AI-based chatbots to simulate conversations with real-life users. Our services ensure that not only your chatbots are able to understand, remember and recognize different types of user queries but are also able to provide them with satisfactory solutions and explanations.
Chatbot Training Data Services Offered by SunTec.AI
Developing a chatbot that provides human-like 24-hour customer services and also minimises the cost of customer services is not an easy task. It faces several challenges like:
- Training your chatbot model to recognise text/voice messages
- Training it to accurately respond to such messages
- Maintaining the conversational protocols throughout the interaction
To overcome these challenges, your AI-based chatbot must be trained on high-quality training data. Training data is very essential for AI/ML-based models, similarly, it is like lifeblood to conversational AI products like chatbots. Depending upon various interaction skills that chatbots need to be trained for, SunTec.AI offers various training data services.
Due to rich and diverse human languages, human interactions are often complicated. People belonging to different demographic groups might express the same sentiment/intent differently. Chatbots need to be trained to ascertain the common intent. SunTec offers large and diverse training datasets for chatbot that sufficiently train chatbots to identify the different ways people express the same intent.
Being familiar with languages, humans understand which words when said in what tone signify what. We can clearly distinguish which words or statements express grief, joy, happiness or anger. But these attributes need to be taught to a chatbot. With access to large and multilingual data contributors, SunTec.AI provides top-quality datasets which train chatbots to correctly identify the tone/ theme of the message.
Highly experienced language experts at SunTec.AI categorise comments or utterances of your customers into relevant predefined intent categories specified by you. Every chatbot is designed for a particular use case. Depending upon the use-case, our experts accurately classify your customers’ utterances in predefined intent categories for your chatbot to understand and recognise different intents which mean the same.
Chatbot Training Data Services: The SunTec.AI Advantage
For a world-class conversational AI model, it needs to be fed with high-grade and relevant training datasets. Through its journey of over two decades, SunTec has accumulated unmatched expertise, experience and knowledge in gathering, categorising and processing large volumes of data. We can provide high-quality, large data-sets to train chatbot of different types and languages to train your chatbot to perfectly solve customer queries and take appropriate actions.
SunTec.AI offers you:
- Two decades of relevant experience
- Large size of data contributors
- Multilingual training datasets
- Customised training data solutions
- Scalable output
- 99%+ accuracy
- Cost-effective solutions
- Quick turnaround time
- ISO 9001:2015 certified for Quality Management
- ISO 27001:2013 certified for Information Security Management
- 24*7 customer support
Discuss Your Project With Us
To discuss your chatbot training requirements and understand more about our chatbot training services, contact us at firstname.lastname@example.org.
We understand that the level of detail applied during data annotation directly impacts the overall accuracy and quality of the resultant AI algorithm’s predictions.
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