Indrek Vainu

Newspaper reports: AlphaBlues makes the first popular customer service chatbots in Estonia

“The TrainBot and the BusBot are the first Estonian chatbots that have started to play a role in the everyday customer service” writes the leading Estonian newspaper in their article about chabots that we at CartSkill launched a few months ago. “It is possible to communicate with those bots. Each request is responded quickly and a quick praise “Thank you” is followed by the reaction “You’re welcome” … aside from this the bots can offer relevant information and are able to show you train and bus departure times, schedules and prices”. It has been great to see a quick uptake and

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Chatbots in 2017

Let’s take a quick look what we can expect from chatbots in 2017. 2016 was the year that chatbots made their first mark. With Facebook opening their Messenger platform for developers in April 2016, the bot-rush started. Other large players jumped on the bot wagon as well. Namely Google with its purchase of api.ai and release of Assistant and Home, Apple’s iMessage apps and Amazon with its Alexa skills store for Echo devices. Here is what we can expect from chatbots in 2017: Chatbots will seek to provide more value for users. In 2016 we saw a myriad of bots that were not

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Our new distributor in Australia and New Zealand

Happy to announce we have formed a partnership with ArneTech who becomes our representative in Australia and New Zealand. This allows companies in the region with interest in high-end custom chatbots to work with us. Based in Melbourne, ArneTech, is headed by long-time software entrepreneur Kristian Jaakkola. With a degree in computer science and experience in web service and quality assurance, Kristian brings a wealth of knowledge from the software sector allowing him to quickly grasp the real needs of companies and possible technical solutions as they embark on their chatbot strategies. According to Kristian “ArneTech is boosting the business of its

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Slush ’16 recap

Last week we took part in Slush, the gathering of 15,000 tech-heads as the slogan goes in the far North. Helsinki, Finland to be more exact. As always for this time of the year, the weather was lovely with sufficient amounts of snow, rain, wind and occasional sunshine to sweeten the deal. Having attended the first Slush many years back (a cozy gathering of 300 people), the event has now grown to 17,000 visitors making it one of the largest tech conferences in Europe. What happened and what did we learn at Slush this year? Here’s the rundown: CartSkill reached

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Customer case: find a car through Messenger – autotie.fi

We’re happy to announce that we launched a bot for Autotie.fi – the leading car sales portal in Finland that is part of the Sanoma Media Group (HEL:SAA1V). I sat down with Jarkko Forsberg, who is managing Autotie to learn more about the reasons he decided to bring the business to Facebook Messenger. INDREK: What is Autotie.fi? JARKKO: Autotie.fi is the leading new and used car classified in Finland. Our approach is extremely consumer centric. Thus we have been the ones to bring new service features to the market – chat and dealer reviews among other things. INDREK: Bots are

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Using neural networks for enhancing video call quality

One of the things our tech team is always fascinated by is the way in which neural networks can be deployed to solve a variety of use cases. And even though we work with conversational text and chatbots to improve customer service for our customers, we are inherently curious and tackle various challenges through our experimental work. So it is no wonder that a few months ago, during one of our casual Friday afternoon brainstorming sessions, our talk drifted to the poor quality of video calls with an iPhone. It is well known that the popularity in various entertainment and

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What We Learned At #dmexco – the Marketing/Advertising Expo

Customers are at the center of every business. Keeping customers happy is what makes the business grow. While simple in its essence, customer service is often an overlooked aspect. For many it is a thing that would be best kept at a comfortable distance – to outsource it to call centers abroad and maintain a variety of email/web forms through which customers can interact. Quite often one might wonder whether the crafted email will reach a service representative or end up in a black hole. We just came back from Cologne where we attended dmexco, Europe largest marketing and advertising

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3 Elements That Make Bots Work In Facebook Messenger

The brave new world of bots is marching onward with each passing day to the steady drumbeat of growth in messaging apps worldwide. People love to text and messaging has emerged as the preferred communication mode for many. With the growth in Facebook Messenger and its platform, talking to companies and brands through messaging is slowly starting to gain ground. As with any breakout thing, there are opinions on both sides of the spectrum of whether it is hype or whether it is something tangible that will stick around. Whatever the case may be (I’ll leave my crystal ball post

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Fashion brand’s Messenger store launch covered in US media

CartSkill builds one of Europe’s first online stores in Facebook Messenger for fashion brand Monton. Now users can shop inside Messenger by chatting with the Monton brand. Instructions here. “We think of this as part of an omnichannel approach,” said Külli Koort, head of ecommerce at Monton, which operates an ecommerce site and just shy of 100 brick-and-mortar locations across eastern Europe. The idea is to create an online store within Facebook Messenger, piggybacking off Facebook’s existing app with mass adoption, while at the same time reducing the number of clicks it takes to get people ensconced in Monton’s ecommerce

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Deep learning networks benchmarked.

As we have made a good number of improvements in our visual recommendation technology we decided to benchmark it against other popular deep learning networks. The aim was to compare our recommendation against AlexNet (Caffe implementation) and ResNet (Torch implementation) and to outline errors in product similarity identification. The setup for the comparison was such that with each network we fed in 3 different product categories and identified visually similar products based on a single thumbnail image. The errors are outlined in the images below in red circles. For the product categories we used 3260 different bags, 7028 different shoes

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