Mindlance offers comprehensive chatbot development services. Chatbots can be incorporated into:
1. Websites
2. Mobile App
3. Social Media

Mindlance has experience in building chatbots on the following platforms:

– Wit.Ai (Facebooks’s free chat BOT engine platform)
– API.ai (Google’s conversational user experience
   platform)
– Kik codes
– Pandorabots

Using Machine learning and Artificial Intelligence algorithms, businesses can optimize and uncover new statistical patterns which form the backbone of predictive analytics. Neural Networks based computing frameworks are playing in critical role in Predictive Analytics.

Neural networks can be used to solve some interesting business problems in an everyday life. For example, a model of neural network for a mortgage lender can be built using training data set and using inputs such as age, income, current debt, education, credit score etc. that predicts debtor risk.

Mindlance experts can work with you to build neural networks based predictive analytics models to accurately predict business or transaction outcomes.

Competition in online-selling sites has never been as fierce as it is now. Customers spend more money across all their providers, but they spend less per retailer. The average size of a single cart has decreased, partly due to the fact that competition is just one click away. Offering relevant recommendations to potential customers can play a central role in converting shoppers to buyers and growing average order size.

Online buying example above is also relevant in other industries example buying a flight ticket, booking a car or hotel.

Mindlance can help your enterprise build a product/content recommendation engine based on your business needs.

Machine learning has been recognized as a successful measure for cyber intelligence and fraud detection. Machine learning works on the basis of large, historical datasets. Based on this historical data a model can be created and trained.

The model could use various data points such as the age and value of the customer account, as well as the origin of the credit card. There can be hundreds of inputs and each contributes, to varying extents, towards the fraud probability. Note, the degree in which each input contributes to the fraud score is not determined by a fraud analyst, but is generated by the artificial intelligence of the machine which is driven by the training set.

Such features in machine learning-based systems make it possible for fraud analysts to identify the most significant contributors. Feedback from users to confirm the system’s decisions by marking customers as genuine or fraudster improves the machine’s learning ability, adding to accuracy.

Three steps to predict fraud using machine learning
– Extract features from a dataset
– Provide training set
– Build models