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AI Based Integration

AI Based Integration

NexGen brings you the opportunity to reinvent your business strategies by integrating AI services, a library of modules to enable accelerated AI features development for your business. Spanning across both the web and mobile applications, Artificial Intelligence will make your business smarter on every corner. Offering value for money through our high-octane AI development services, it’s time to step into the world of AI. Hire our AI consulting, development, and integration services to improve your interaction with end users thereby improving the business performance.

Additional Services

Open and AI-ready infrastructure

A comprehensive AI portfolio

SaaS applications with embedded AI

Choice of Data Management Platforms

Why Brands Choose NexGen For AI Based Integration

Experience Led & Outcome Focused

We help brands understand the role digital can play in realizing strategic opportunities and solving real world business problems, always keeping the focus on the customer's experience and the results generated.

Proven, Rapid, Agile & Trusted Delivery Methods

Using agile methodology, always keeping you in the loop. Streamlined delivery, cost effective engagements, designed to match your goals, your timeline and your budget.

Experienced Developers

Our mobile web developers create solutions that work seamlessly across all platforms and operating systems: smartphone, tablet or laptop or running iOS, Android. 

Transparent, Collaborative, Communicative

Complete project visibility and multiple open lines of communication from day one. We are available when you need us and continually updating you on your project’s status.

FAQs

Artificial Intelligence refers to the capability of a machine to imitate intelligent human behavior. Put another way, AI technologies are algorithms that attempt to mimic things that humans do. Machine Learning, on the other hand, is the science and engineering of giving computers the ability to learn without being explicitly programmed — algorithms that learn from data.

AI and Machine Learning are often discussed in conjunction with one another, but it is important to note that not all AI techniques use Machine Learning, and Machine Learning is also used for other things besides AI, such as decoding genetic sequences.

Some examples of AI technologies that are commonly used today include:

Speech Recognition: Taking audio and working out what the words spoken are.
Natural Language Understanding: Taking sequences of words and determining the intended meaning.
Computer Vision: Recognizing objects and understanding the world — to provide sensory input for control of a driverless car, for example.
Dialogue Management / Conversational AI: Ability to conduct a natural conversation with a user. Taking in the meaning conveyed by the user, thinking, and deciding what to SAY and DO next.

Front-end use of AI technologies to enable Intelligent Assistants for customer care is certainly key, but there are many other applications. One that I think is particularly interesting is the application of AI to directly support — rather than replace — contact center agents. Technologies such as natural language understanding and speech recognition can be used live during a customer service interaction with a human agent to look up relevant information and make suggestions about how to respond. AI technologies also have an important role in analytics. They can be used to provide an overview of activities within a call center, in addition to providing valuable business insights from customer activity

For one thing, it is possible to be considerably more aggressive in the application of some newer technologies specifically in the case of agent support, since there is still a human agent who can choose whether or not to use the supporting information. For example, there are new techniques applying deep learning technologies (including sequence-to-sequence learning) that can learn how to respond to a customer question from large sets of example dialogues.

The danger of applying this kind of technology to a fully automated Intelligent Assistant is that risk remains that the automated interface will provide a suboptimal or uninterpretable response that could negatively impact the individual customer experience or even the brand as a whole.

In the case of AI for agent support though, critically, you have a human in the loop alongside the AI solution. That way, the human agent can choose to approve or disapprove automated or partially automated responses. By doing so, human agents also help to continuously train and teach the Conversational AI over time.