We only work on meaningful projects that have a positive impact on the world. We build strong long-term partnerships that enable our partners to build their product faster, solve the most difficult machine learning tasks, so that they can become leaders in their own domains.

How it works


Your idea & visionMaybe you have an idea and a vision of changing the world, your company want to execute new product or you just have a unique dataset, but you do not have the knowledge to get the most from your data. Our team is here to execute your vision and bring the product to life.
Fast and reliable

Fast & reliable

Speed is the foremost priorities for most companies and we are very aware of this. Our pre-prepared, generalised code allows us to move faster without compromising quality. The likelihood of becoming a leader in the industry is directly correlated to how fast a team can develop their products. We are capable of deploying solutions in as little as 4 weeks.

Pay as you go

Pay as you go

It is important to spend your resources as efficiently as possible. We cover fixed as well pay-as-you-go plans. You will always pay only for what you have used. It is important to have the ability to flexibly adjust your team size and their spent hours so that you can easily budget your resources.
Business value

Delivering business value

The end goal of each feature should be to directly or indirectly enhance your business value proposition and benefits for your users. What you invest into your product should be reflected in your revenue. Our aim is to guide you to define your product in such a way that we maximise the value proposition of your business.
Technological edge

Technological edge

Cutting-edge technologies, clean code, and optimal software architecture is standard. However, what is not common is the ability to solve the most difficult scientific challenges. Our company is the ideal intersection between the world of business, software engineering, design, and academic research.
Get Funding

Get Funding

It is time expensive to raise capital. We will connect you with our venture partners by ourselves. We are mainly interested in AI-first start-ups, but feel free to connect with us for other products that are trying to make good in the world.
Your Team


It takes a lot of time to hire the team and this proves even more true for great engineers. Your investors will ask for people who have already joined you on your mission. Our team is here as your right hand. Whether you need engineering, design, product/project management, business development or VC funding, we have you covered.
From our engineering


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Text classification with transformers in Tensorflow 2

The transformer-based language models have been showing promising progress on a number of different natural language processing (NLP) benchmarks. The combination of transfer learning methods with large-scale transformer language models is becoming a standard in modern NLP. In this article, we will make the necessary theoretical introduction to transformer architecture and text classification problem. Then we will demonstrate the fine-tuning process of the pre-trained BERT model for text classification in TensorFlow 2 and Pytorch.

Top AI and Machine Learning Books for Business Leaders in 2020

AI & machine learning is becoming part of today's products more than ever before. It is now even more important for each business to have a clear vision of how artificial intelligence intersects with their mission. This is the list what we recommend to our partners to get insights on current ecosystem.

How to build GraphQL API for text analytics in Python

GraphQL is a query language for building API and a server-side runtime, which enables you to describe your data. GraphQL isn't tied to any specific database or programming language. You can build your GraphQL server in different languages like Node.js, C#, Scala or Python. The ecosystem in Node.js and JavaScript in general is the most evolved GraphQL wise and it is usually better to build API for client facing application in Node.js. You can take a look at our blog to learn more. However, Node.js is not the best fit for data science, so we will take a look at how to build microservices that will help us serve our machine learning models, high-performance computations and text analytics. In this article, we will be solely focused on building an API that enables us to expose text analytics capabilities. Our GraphQL server can be also used to serve the output from machine learning models built by frameworks like TensorFlow, FastText or PyTorch, but we will take a look at this in depth in our future articles.

Fade-in and fade-out animation with spring and transition React Hooks

The React Spring library has a modern way of approaching animations by using a physics-based model. You can configure animations using real-life values like mass, friction, and tension, which allow you to create more believable transitions. There is also an option to set the duration in milliseconds. The library has great performance and an easy-to-use API. React Spring is cross-platform and has quite a optimal bundle size.

Top 7 Figma plugins for improving your design workflow

Figma, the platform that forced even the most conservative designers to switch from Sketch, Invision or AdobeXD, is here with new improvements. Until recently, plugins were something that we really missed in Figma in comparison with other platforms. In the last few months, Figma has launched many updates such as GIF import or Smart Selection, so we expected that plugins in Figma would finally be introduced in the near future. Now it has almost been three month since the release and there are over 150 available plugins, so I have collected the 7 most useful plugins that will enable you to make your designs much better and faster than ever before.

React hooks in Apollo client for GraphQL queries and mutations

The hype following the last React conference has diminished. The proposal for React Hooks was introduced as part of the React alpha release. Since React v16.8, React Hooks have been in all official releases as well. How does this improvement affect how we build our apps with GraphQL?

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