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Artificial Intelligence
Exponential efficiency gains in tasks needing human intelligence

The term “Artificial Intelligence” is applied when a machine mimics “Cognitive” functions associated with human minds, such as “Learning” and “Problem solving”

Application of “Artificial Intelligence” to Enterprises can generate huge ROI for the business. Since the overall goal of intelligence is to allow machines to function in an intelligent manner, almost similar to how humans do, any enterprise can apply this to a large number of activities.

Across Retail, Travel, Hospitality, Banking and Telecom sectors, there are now a number of use cases where employee productivity is being improved with AI support, resulting in better outcomes for business. The simplest example here is that of ChatBOTs to guide Customers through the buying process or to help address grievances.

The other extreme is specialized software to recognize speech from a microphone, read license plates off a video file, identify faces on camera etc. all of which have wide-spread application across industries.

Sentiment Analysis of user sentiment from social media feeds like Twitter and Facebook, Natural language processing for self-service analytics, defect analysis using vision processing are some other areas where AI is being applied.
Internally, AI helps improve efficiency of daily tasks, guiding employees through incomplete/incorrectly performed tasks, suggesting improvements and even performing a part of the task.

If any of the above find resonance with your business needs and you wish to explore this next generation technology, you can benefit from our expertise.

AI applications include bot messaging/support, document discovery, voice analytics, text sentiment analytics and crawling techniques. Some common examples of machine learning applications include:

  • Forecasting sales based on historical numbers
  • Predict resale price of a used car
  • Guest sentiment analysis based on hotel reviews
  • Retail Consumer sentiment analysis based on twitter feeds
  • Recommendations from Amazon while browsing a company’s Facebook page
  • Product suggestions on an e-commerce website
  • Music suggestions on a music streaming app



  • Natural language processing in apps
  • Intelligent recommendations and semantic search
  • Voice and text BOTs using LUIS
  • Forecasting using Azure machine learning
  • Intelligent algorithms to see, hear, speak, understand and interpret


  • Retail Demand Forecasting, Social sentiment analysis, Purchase Assistance
  • Security Facial recognition, Video Clip/Voice Analysis
  • Hospitality Personal Assistant, Customer Experience, Geolocation based services
  • Banking Account Balance, Fraud Detection, Payment Reminders
  • Travel Search and compare, Live updates, Geolocation based services
  • Telecom Billing information, Payments


  • Get insights from large volumes of data in a fraction of the time taken to do this manually
  • Improved Customer Satisfaction due to instant response and personalized Interaction
  • Reduced Customer operations costs as BOTS available 24*7 with no additional cost
  • Faster and improved Decision making support

Azure Bot Service

QnA Maker

Text Analytics API

Azure Machine Learning


R language