Gain competitive advantage
by developing new business solutions
Use data more effectively
and make better-informed decisions
Use our step-by-step approach
to carefully manage your investment
Build AI solutions
without an in-house data science team
Start using our proven methodology
“I don’t know how to start with AI. It’s a black box – the results aren’t guaranteed, it takes a huge budget and probably a lot of time.”
AI doesn’t have to be a black box – it’s more about acquiring the knowledge you need to make the right decisions. Take a look at our AI solution development process and try to identify the stage you’d like to start from. It boils down to having a “go” / “no‑go” decision after each phase of your AI project, allowing you to optimize costs and mitigate risk.
Use case discovery
Use case discovery
To get the right start, you’ll need to know two things: the possible machine learning business applications used in various industries, especially your own, and how machine learning can add value to your organization. That’s the knowledge foundation you’ll build your AI strategy on.
From there we can offer a workshop covering use cases from your industry and related ones, consultancy on how to address your business challenges with data you have already gathered, and evaluation of what is possible for your specific business case.
At this stage, you’d like to determine if and how machine learning can help solve your specific business challenge. Select the use case you’d like to analyze, describe the business problem in detail and define the desired results.
We will explore how your data can be used to solve your business case, assess opportunities and risks associated with the project, estimate potential results the project may deliver, develop baseline models and prepare an implementation timeline.
You have already chosen a machine learning use case you’d like to explore and you know it’s feasible. Now it’s time to build a prototype of the model and deploy it in a test environment, the final verification before it goes live.
We then deliver the technical specification summarizing our approach and machine learning methods used in the solution. We will also recommend an approach to productionization.
Your machine learning solution is verified and ready to move to the production phase.
That’s right, it’s time to go live.
Our data science and software engineering teams develop a production-quality version of the solution and, if required, integrate the model with your existing systems or platforms. We educate you on how the model was developed and how it works, providing you with the technical documentation and a series of workshops for your users.
Of course, that’s not the end. Your deployed solution needs to be adjusted to a changing environment. We provide ongoing support for the models we help deploy and cooperate with you to gather the data and enlarge the dataset on which the model works.
We also continuously verify whether the model responds appropriately to the changing environment and improve its accuracy wherever possible. We also monitor the performance of the infrastructure and recommend changes to optimize resource usage.
Let’s discuss your business case
If you need help choosing an Artificial Intelligence service, let’s talk: that’s the easiest way to analyze your business challenges.
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