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Conversational AI combines data, machine learning (ML), and natural language processing (NLP)
Conversational AI combines data, machine learning (ML), and natural language processing (NLP) to create technologies that understands intent, analyzes different languages and contexts, and imitates human conversation. This empowers technologies like chatbots, virtual assistants, or virtual agents to learn and become more intelligent over time, and quickly and efficiently communicate in a human-like manner via text and speech.
There are two main components of Conversational AI:
Based on the idea that systems can continually learn and identify patterns through repeated use and automatically improve themselves with minimal human interaction.
Technologies that enable computers to understand language in speech or text including intent and sentiment.
Traditional chatbots follow simple pre-defined rules without a true understanding of intent and context. Although some may claim to have conversational abilities, these chatbots are typically text-based and are trained to respond to certain keywords for every foreseeable scenario. As a result, if questions are off script, they may be ineffective at answering them.
Conversational AI chatbots, conversational AI assistants or AI chatbots are built upon layers of complex systems that enable continuous self-improvement – becoming more intelligent and mature as they learn with conversation data and supervised machine learning. In addition, using natural language processing (NLP), AI chatbots emulate human conversations by analyzing and understanding sentiment and context in different languages.
NeuroSoph’s Specto AI platform builds custom-tailored conversational AI chatbots that are trained with your industry-specific data and can augment any customer interaction, improve productivity, and resolve requests faster than ever in your organization.
Some benefits from leveraging conversational AI chatbots include:
Text, voice and outbound interactions
Highest standard of security and compliance
Differentiated brand experience with personalized responses and guidance
Reducing cost of customer care by reducing volume of inquiries to select channels
Omnichannel for more customer engagement and accessible 24/7 365 days
Instant customer feedback and actionable insights to inform future developments
Optimizing workforce with decreased workload and creating more time for human agents to solve the most complex problems
Higher productivity with task and process automation and automated conversation routing
Higher customer satisfaction with seamless user experience, intuitive self-service, faster resolution and more meaningful engagements
By enabling conversational AI chatbots to learn and improve, the value of AI chatbot solutions will increase over time. As a result, many different organizations are increasingly incorporating intelligent chatbot solutions to optimize their workforce, so they can be more productive and focus on more complex tasks while resolving requests faster than ever.
Understands & responds to both text and voice
Multiple channels – blogs, websites, voice assistance, call centres, etc.
Understands context, sentiment, intent of a conversations & in different languages
Learns from interactions & improves over time with supervised machine learning
Handles wide-scope & non- linear interactions - out of scope topics & topic changes
Enterprise-ready, integrates with existing data sources & can be bootstrapped to learn new information
Can integrate with 3rd party systems (e.g. RPA, ERP, CRM, etc.) to perform tasks
Text & Voice
Omnichannel
Natural Language Understanding
Continual Learning
Dynamic Interactions
3rd party integrations
Scalable
Understands & responds in text only
Single channel - chat
Keyword driven, pre-determined & rule-based dialogue flow
Has to be reconfigured, updated & revised with new rules
Rule-based interactions - cannot handle out of scope interactions
Manual maintenance and training on every scenario make it difficult and time consuming to scale
Basic functionality may limit integration with existing infrastructure
