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Red Ant one of two British finalists picked for IBM Watson mobile developer challenge

Mobile retail technology specialist Red Ant has been picked as a finalist for IBM’s Watson mobile developer challenge – an initiative aimed at helping it prepare for the “third, cognitive era”.

The tech giant’s CEO Ginny Rometty kicked off the challenge earlier this year, inviting developers to put forward their ideas for mobile apps for its artificial intelligence (AI) engine Watson.

Hundreds of entries have been submitted, and a shortlist of 25 companies selected, then a final five winners of which Red Ant is one.

For the competition, Red Ant has developed an app, designed to tie together the capabilities of Watson and Red Ant’s RetailOS system in a way which lets sales staff provide a ‘next generation, customised retail experience’ to customers.

The app is for the use of retail assistants, uniting customer demographics analysis, purchase histories, product information, local pricing, and customer reviews and tech specs in one place.

It has been designed to capitalise on Watson’s ability to converse with users in human speech, relaying information through text on screen, text-to-speech via an earpiece, and in the future via wearables like Augmented Reality, enabled glasses or smartwatches.

Red Ant is now set to travel to Orlando, Florida to pitch the app to judges at the IBM Innovate conference on Monday 2 June.

Dan Hartveld, technical director at Red Ant, said: “As one of two finalists from the UK, it’s great to see British technical expertise taking its place in a global arena.

“Many approaches to retail personalisation tend to focus on the consumer, however we believe the focus should also be on employees, specifically retail assistants. By enabling them to interact with omnichannel retail systems in an intuitive way, shop floor staff are empowered with the knowledge to play a direct role in boosting sales, initiating promotions and improving customer service.”

Watson is an AI computer system capable of answering and interpreting questions in human languages, and was originally used on the US quiz show Jeopardy! (pictured above) in 2011 where it won against former world champions.

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