What we do?

Building data science products and services can be tricky. In order to validate AI ideas quickly and to always deliver the best experience to the end users, we take the minimum viable product (MVP) approach to data science.


Machine Learning

Machine learning (ML) is a field of computer science that gives computers the ability to learn without being explicitly programmed. ML can find insights from large data sets that would be impossible with conventional tools as well as it can be used to predict outcomes and user profiles in almost all industries. We work with applied and scientific AI in order to come up with the best use cases for your business.

Distributed Computing

Distributed computing, sometimes also known as "Big Data", is the concept of being able to store and process huge amounts of data quickly. This can be useful in many analytics areas but also is one of the key technologies that has started the AI era. We can help you build your Hadoop or Spark clusters to make your data accessible.

AI PoC development

One of the core values of a MVP is develop the bare minimum and validate your use case very early on in the product development phase. One of our key competences is to come up with potential AI use-cases in your industry and develop AI PoC-s quickly to validate your ideas in the market fast.

Consulting

Not all companies are on the same level of data science readiness. We help you determine all the steps necessary to start working with your data. In addition we can help out in developing a data science strategy as well as building a data science team in your company structure.

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Our development process


We have developed a thoroughly tested AI implementation process to speed up development and create the cutting edge services customers need

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01

Data science ideation

Our process usually starts off with an AI use-case ideation phase. This usually happens when customers are just looking for ways to monetize their data and are searching for ways on what AI can be used for in their company. If the customer already has a specific area where they want to use AI, then the ideation phase is basically a MVP feature workshop where we help determine the technical setup and architecture of the project.

02

Data exploration and validation

Data exploration is the most important phase. This is where we validate the data to the use case. Here we determine if there is enough data with enough quality to build a specific AI model. Also if there is not enough data then data fusion strategies are developed.

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03

Proof of concept development

The PoC outlines a quick way to validate artificial intelligence use cases. The main concept here is to fail or win quickly, meaning that we want to create something as soon as possible so that end-customers can actually validate the idea.

04

AI solution validation and implementation

The final implementation of any AI model or solution can be drastically different from regular software development implementation. Sometimes you might have to launch a completely raw solution for the models to start learning in real-world conditions.

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