Domain Specific Goodness With Artificial Intelligence

Today, AI is used to propose what you should purchase next on the internet, to interpret what you say to virtual assistants like Amazon’s Alexa and Apple’s Siri, to recognize who and what is in a photo, to identify spam, and to detect credit card fraud. This form of machine intelligence may be seen in Apple’s Siri virtual assistant’s voice and language recognition, in self-driving car vision-recognition systems, and in recommendation engines that suggest things you might enjoy based on previous purchases. These systems, unlike humans, can only learn or be taught how to perform specific jobs, which is why they are referred to as narrow AI. Here are some areas of AI that you can leverage on:

Natural language processing (NLP)

Natural language processing (NLP) is an artificial intelligence area that aids computers in comprehending, interpreting, and manipulating human language. In order to bridge the gap between human communication and machine comprehension, NLP depends on a variety of fields, including computer science and computational linguistics. Here are some example use cases: chatbots, speech recognition, sentiment analysis, email filtering etc.

Computer Vision

Computer vision is an area in artificial intelligence that teaches computers to analyze and comprehend images. Machines can properly recognize and categorize things using digital pictures from cameras and movies, as well as deep learning models, and then react to what they “see.” Here are some example use cases: biometric access management (face recognition), industrial robots and self driving cars, digital diagnostics (X-rays, MRI’s, CT Scan etc), anomaly detection on images and videos.

Machine Learning (ML)

Machine learning (ML) is a field of inquiry devoted to understanding and building methods that ‘learn’, that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms are used in a wide variety of applications, such as in personalization, email filtering, speech recognition, and recommendation systems etc, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Reinforcement Learning

Reinforcement learning is a machine learning training strategy that rewards positive actions while penalizing undesirable behaviors. A reinforcement learning agent can sense and comprehend its surroundings, act, and learn via trial and error in general. Reinforcement learning is built on the principle of continuously learning from data, human experts, and the external environment. Reinforcement learning enables lifelong learning, with adaptation at its core. It enables AI systems to become intelligent over time to adapt to the task at hand.

How can your business leverage Artificial Intelligence ?


Discovery and roadmap (1-2 months)

We will map out areas in your business that can leverage AI (customized to your business and data). As a result of this exercise, we will discover AI use cases, process (architecture), and have a clear roadmap for development.


AI use cases pilot (5-8 months)

We will develop solutions (open source or on cloud or on-premises) to enable AI in your organization. Pilot 1-2 use cases to validate the business value. As a result you will see the rate of automation and optimization go up in your organization.


Enterprise grade ML operations (10-12 months)

We will help you scale up your AI solutions and support majority of your business operations related to AI solutions and use cases. You will exponentially improve your rate of automation and optimization of your business.

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