Archive - December 2019

Migration From DialogFlow to RASA: the missing part

Implementing DialogFlow chatbots is cool and convenient if you have something trivial and easy to prototype: fancy UI – easily, extracting base entities like name, surname and phone number – here is a tool if don’t want to install and deploy – cloud solution is at your service.
But what if you need to go deeper:

– Do you have a Japanese tokenizer, dear DialogFlow?
– Nope
– Transparent and customizable intent classification tool?
– Sorry, guys.
– Also I want to integrate my search index, knowledge graph and custom dialogue management policy.
– What are you talking about?

In a nutshell if you want fully controlled system, if you need custom advanced AI in your app, if you need natural language processing in your chatbot pipelines, if you want to scale your chatbot behaviour – on-premise solution is the way for a chatbot developer. And here it’s Rasa framework that really shines.

Read More

ISS Art Named Global Development Partner by Clutch

As a reliable software development partner, we always strive to integrate our solutions into your marketing and strategic plans. Since 2003, our  company has been helping the world’s leading companies translate even their most complex objectives into stunning realities!

We focus on machine learning, data science, IoT solutions, and can code in a number of languages like Python, Java, C++, Swift and Kotlin. We firmly believe that your challenge is our inspiration, and we’ve successfully built solutions for the most challenging projects with that  approach in mind.

In light of our contributions to the technology community, we’ve been named a top global leader in Custom Software Development as Clutch’s online research shows. We’d like to take this time to thank our customers for participating in client reviews on our behalf, to gauge our impact on their businesses.

Read More

5 use cases of AI based recommendation systems

Artificial intelligence solutions are widely used in a variety of businesses. With opportunities they provide, it becomes possible to optimize processes and bring revenues to a new level.

E-commerce is not an exception. Lots of companies are now looking for ways to cross-sell and up-sell effectively. This is where an AI based recommender system can help. 

As McKinsey reports have shown, 75% of content that Netflix users consume and 35% of products that Amazon users buy come from recommendations. After implementing a recommender system, Amazon reported a 29% increase in sales. Alibaba group managed to drive the conversion rates by 20% when it applied ML based recommendation algorithms to provide shoppers with personalized offers during the sales festival in 2016.

Actually, most online shoppers expect companies to provide them with personalized recommendations. According to Evergage, 56% of users will come back to the sites that offer recommendations again and again.

Wondering what kind of an intelligent recommendation engine to implement for your business? Or probably you are interacting with people who need to implement such a system? If any of these is the case, you definitely need to look through the possible use cases below.

Read More