R-Style Lab FAQs

    • What types of businesses are R-Style Lab’s services best suited for?

      Startups, small to medium-sized companies.

    • How can R-Style Lab benefit clients?

      Founded in San Francisco, USA in 2006, R-Style Lab has gained impressive expertise in delivering top-notch solutions, which can seamlessly integrate into a client’s IT infrastructure or support his project. The company focuses specially on AR, VR and connected things; however, its vast portfolio includes mobile apps, website development and cloud infrastructure.

    • What geographical regions does R-Style Lab serve?

      After its launch in the USA, its primary focus has expanded from the American market to more global projects, and its clients’ headquarters are spread around the globe: London, Berlin, Moscow.

    • What are the most popular or commonly known projects R-Style Lab has worked on?

      We have developed a console portal for Wargaming, creator of the legendary World of Tanks game; machine learning based Glamozis app; a radiation detection connected solution for U.S. law enforcement officers; Breakfast Delivery app for college students - and much more!

    • How/why is R-Style Lab different than those competitors or most others in their market?

      Our team is known for seeking non-trivial ways to tackle each client’s problems, keeping in mind the main goal - needs of the clients and the context they are placed in. We don’t apply harsh schemes to each problem, but try to look out of the box and find the unique, secure and well maintainable solution.

    • Who are R-Style Lab’s biggest competitors?

      After ranking #1 among IoT development companies in top listings, we see other tech vendors from there as our primary competitors, which is both challenging and inspiring.

    • What pricing tiers or structure does R-Style Lab offer?

      R-Style Lab offers diverse pricing models to its clients, either assigning a dedicated team or teaming up with in-house developers: Fixed Price; Time & Material; Mixed Model.

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