AI & Data Science Projects
Together we develop and test suitable use cases in order to obtain a prototype with real data at the end. We then industrialise your use cases into products with a focus on scaling and generating real added value.
Design & implement use cases
The application possibilities of data science and artificial intelligence are diverse and range from data analysis and recommendation systems to new business models. We develop the systems you need and help you integrate them into your existing infrastructure and continuously develop them further.
Do you already have ideas on how you want to use AI or do you still need support? We will be happy to accompany you on your data journey.
In order to generate sustainable and consistent added value from data, data use cases must be successfully scaled and industrialised. For this reason, the demand for data engineering expertise has risen sharply in recent years. Leading companies have already demonstrated the benefits of (big) data, analytics and AI for themselves in numerous PoCs (proof of concepts). Now they face the great challenge of implementing the dissemination, scaling and industrialisation of these data use cases.
We measure our joint success by the actual added value we achieve in the end. From the experience of over 1,000 projects with more than 100 customers, we have developed our Data Engineering Services as a holistic process and organisational model for the industrialisation of data use cases. Our experienced data engineers, data architects and AI engineers support you in the implementation.
Data projects implemented
Use cases identified
AI and Data Experts
Years of experience
WHAT DOES A PROJECT WITH [at] LOOK LIKE?
Our goal is to test use cases as quickly as possible. To do this, we first create a use case concept: we generate hypotheses for the use case and check the necessary data. In the subsequent exploration, we carry out a proof of concept and build a test environment with your data. This allows us to quickly assess whether the use case can be implemented in reality or not. After successful exploration, we programme the first prototype. This is the α-version of your analytics or big data app, so to speak.
Once this has proven itself, the use case is then industrialised into a finished product. The absolute focus is on scaling and the sustainable generation of added value - which is why the user is also the focus here. First, we create a scaling plan with prioritisation of markets, functions and brands. Then, based on the scaling concept, we move to the next expansion stage of the pilot and turn the prototype into a Minimum Viable Product (MVP) - the ß-version of your analytics or service. Big Data app. Through continuous testing in the development pipeline, we turn this ß-version into a marketable data product. DevOps is used to merge the further development and operation of the data product.
Overview of methods used
Use Case Workshop
Use Case Exploration
Use Case Workshop
You have a use case and want to find out how you can now go about AI development? After a presentation of the status quo of the current use case, we use a design thinking session to develop hypotheses in the Data Science context. For this purpose, the necessary data is then validated and checked to ensure the feasibility of the present use case. Finally, we develop an analytical concept for the use case.
Why implement Data Science projects with us
Leader for AI and Big Data
We have been recognised as a #1 Value Creator in Machine Learning by CRISP Research and as a Big Data Leader in Germany by Experton.
We have completed over 1,000 AI & Data Science projects in over 15 different industries.
We find the right technology for our customers depending on their needs and support them in the implementation.
Data Engineering & AI Projects with [at]
Together with your colleagues from the specialist department, IT and, if available, Data Science, we develop ML (Machine Learning) and AI (Artificial Intelligence) algorithms. For this purpose, the data compass provides us with reliable orientation in the data jungle. Just like a human being, a machine also learns in an iterative and agile process. We also use this principle in our own development process. With our agile approach, we can identify and eliminate problems in good time, learn from experience and improve the Algorithms continuously optimise. Our Data Science Delivery Model is the proven approach to quickly and effectively implement machine learning algorithms. Rapid Proof of Value (PoV) is often the key to success.
We see the dissemination, scaling and industrialisation of data use cases as the biggest challenge in AI development at the moment. When data prototypes are to be transferred from laboratory mode to the existing organisation, there is often disillusionment. The prototype does not fit into the current system landscape, processes and governance are not clear, the required speed of the information is not achieved and the costs for licences rise unexpectedly.
We therefore support you in the question of how you can ensure the operation of the data product cost-effectively in your company and analyse risks in advance that could cause the roll-out to fail.
Within a few weeks, we plan the step-by-step scaling and industrialisation of the prototype. In the process, we conduct many stakeholder interviews in order to plan the fastest possible way to scale up. The analysis of the technical framework conditions, as well as the adaptation of the technology to corporate standards or cloud solutions, are standards of our data engineering services. If the prototype is successful, we move on to the next stage of expansion based on the scaling concept. The robustness of the prototype application is now put to the test by further users.
Further functionalities and larger data sets will be added within the pilot phase. In this way, test results from real situations are obtained quickly. By means of extensive tests and continuous monitoring of the individual system components for troubleshooting, we develop the pilot into an operational product.
How do you proceed in a classic data science project?
First, we gain knowledge about your problem (domain and process), data and solution space (method and technology). Then an analytical concept including an evaluation strategy is created. In the second step, the concept is implemented and the results evaluated. Ideally, we create a usable prototype so that hands-on experience can be gathered and the pilot can be prepared.
Does [at] have expertise in business intelligence?
Absolutely! After [at] - Compass the first two areas of competence, "Business Processes" and "Data Intelligence", are an important part of our DNA. In addition, we use a Data Visualisation Guild, which deals with the optimal presentation of data and information.
In which sectors have you already implemented projects?
We have so far successfully implemented over 1,000 projects in a wide range of industries. These include finance, insurance, automotive, retail, energy & communication.
In which sectors does [at] bring domain knowledge?
We bring in-depth domain knowledge due to many years of project experience in the following sectors:
- Energy & Utilities
- Finance & Insurance
- Transportation & Logistics
- Consumer & Capital Goods
In addition, we have successfully implemented projects in a further 15 sectors.
How does [at] ensure the transfer of knowledge to new colleagues and employees of the client during the ongoing project?
In our projects we work according to agile procedures. This ensures a high degree of communication, transparency and thus also know-how transfer between the participants and also to new experts coming into the project.
Have you already completely implemented the data journey in a project?
Data & AI Knowledge
Creating added value from data & AI together