TURNING DATA INTO 
CONCLUSIVE INFORMATION

applications of a.i. and machine learning to the needs of your business

We are experts in machine learning, data science, AI and software development and take care of your project from start to finish, from data preparation to scalable production deployment, updates and follow-ups. We analyse each case and create highly customised solutions aligned with your strategies and goals. 

Our main goal is to help companies leverage their data to improve processes and results. Therefore, our services include the analysis of structured and unstructured data sets and their transformation into actionable information. If the need arises, we collect and integrate data from various existing or pre-defined sources for you:

  • your DATA-Driven company
  • Gain real insights from your data
  • Predict future scenarios and reduce costs
  • Make better decisions, faster
  • Make the most of your time and resources
  • Anticipate your customers' needs
  • Improve the management of your business as a whole

The twelve principles of the agile manifesto

"We follow these principles:


  1. Our highest priority is to satisfy the customer by delivering valuable software early and continuously.
  2. Welcome requirement changes even late in development. Agile processes use change to the customer's competitive advantage.
  3. Deliver working software on a regular basis within a few weeks or months, and prefer the shorter time span.
  4. Subject matter experts and developers need to collaborate daily during the project.
  5. Build projects around motivated individuals. Give them the environment and support they need and trust them to get the job done.
  6. The most efficient and effective way to communicate information to and within a development team is face-to-face.
  7. Functioning software is the most important measure of progress.
  8. Agile processes promote sustainable development. Clients, developers and users should be able to maintain a steady pace indefinitely.
  9. Constant attention to technical excellence and good design promotes agility.
  10. Simplicity - the art of maximising the amount of work not done - is essential.
  11. The best architectures, requirements and designs come from self-organised teams.
  12. Periodically, the team reflects on how to become more effective and adjusts its behaviour accordingly."

development process step-by-step

01/ Assessment

We gather requirements and assess whether we can help you optimise your processes. We evaluate the data you store, what external information is required for the project and with this we determine the specific approach to your problem.

02/ data preparation

Our team of data scientists transforms and prepares the data for exploration and modelling. If necessary, external data will be added. Then, all this data will be aggregated, an additional set of variables will be created and the data will be coded to provide as much information as possible to the model.

03/ Model development

We create and test different AI and machine learning algorithms and models and choose the ones that provide the best accuracy and least errors, to be used in the final solution. The developed algorithms will be trained and will organise the data and look for patterns in order to understand what happened in the past and anticipate what is likely to happen in the future.

04/ POC

Our projects usually involve the development of a prototype solution demonstrating the power of AI. This stage involves evaluating the results and surveying the adjustments needed for final delivery.

05/ Integration and deployment

After the prototype evaluation phase, we are ready to work together. We will work through deliverables - together with your team - to refine the effectiveness of the system in solving your task. We create complete applications and services where you can see the results through a user-friendly visualisation or we perform back-end integrations through APIs.

06/ Evaluation and Optimization

We are always looking for the maximum effectiveness of our projects, so even after the delivery of the model, we continue monitoring the results. Performing safety, performance and accuracy tests and, if necessary, implementing improvements in order to improve the model's accuracy.