Build automation pipelines, Infrastructure as Code and message queues? Public and on-premise private clouds? Yes, we do it all. Kubernetes is our middle name and we get your product moving faster than ever, while reducing operational expenses.
Yield management and predictive actions. We work with bleeding edge analytics platforms, exploit machine learning and computational analytics to accelerate the heart of the solution.
Android or iOS, we help you out with your product on the device in close proximity of your customers. We can do it natively, but lately we've been promoting cross-platform work. Depends on the case.
Ecommerce, marketing sites, dashboards and all sorts application SPA's. This is our bread and butter, be it with REST or GraphQL. You need help with PHP? Then sorry, we're not for you. But have a look what we do work with, that's quite a lot!
Google Cloud Platform
Amazon Web Services
Again, what are you trying to solve? Do you want to bring down your operational expenses, improve your site reliability metrics, lower the time from development to production or launch a new service? We identify possible bottlenecks and characteristics of the service. Figuring this out is the corner stone before jumping forward.
We investigate the different technologies that would be suitable for the jobs, evaluate the pros and cons. Read on best practices when the territory is unfamiliar. Spin up a container locally and test your idea out. How does your authentication and authorization fit into the picture? Where the service is going to run, how it's going to be monitored and how to provide the metrics for operations. What we're looking for is signs of validation before building the actual thing.
Next we just build it and focus on making the initial production deployment. Aim is to release for a set of customers as soon as possible. We might be a bit hacky at this point, while still maintaining an lookout for security flaws. What we're looking for is signs of validation before full commitment on the setup of the actual service.
Even when we're total pros, the first deployment is never a shining diamond. Next we look at the data and figure out where to improve. It might be slow response times, reliability or developer productivity. After the fixes and monitored errors, we write some tests. We make sure that the code is readable after a year or two and that the documentation is sound, services are seldom throw away.
Digitalisation is driven by constant change, but not only that, the change is extremely fast. And by this, we mean disruptions - transformation of an established way of doing business. This is good news for those who are willing to adapt and have the courage to fail. Failing should be viewed as validation. This in turn implies that there must be a stop, or in other words, you have to be able to clearly define goals and embrace failing.
The technologies for processing massive data sets either real-time, or as jobs, has made significant progress during the recent year. Data revolution is an umbrella term for big data & data analytics. Organisations all over the world have huge data sets, but capability to processing large data sets is hard. Information is useful, but only when put in practice. Execution will be the hardest part, despite the organisation size, industry or core business.
Customers today are expecting you to deliver an higher service level than ever before. They expect your product to be accessible at all times globally with immediate fulfilment while simultaneously providing a personlised and memorable experience.
Firstly, get to know your customer. Actively monitor user data and key metrics to understand customer behavior. With help of gathered data new and additional services are created to ease with customer pain points and to amplify your value proposition. Constantly improve the user experience for your customers. This will result in higher service quality, more engaged users and added perceived value of your product.