AI Development & Data Engineering
Together we develop and test suitable Use Cases with the goal of creating a prototype with real Data. We industrialize your Use Cases into products with a focus on scaling and generating real added value.
AI Development
Companies like Google, Amazon and Uber are banking on an “AI First” strategy, meaning that these digital titans are consistently putting
The possible uses of Data Science and Artificial Intelligence are wildly diverse, ranging from Data Analysis and recommendation systems to completely new business models. We will develop the necessary systems for you as well as assist in integrating these new systems into your preexisting infrastructures while continuously refining them. Do you already have an idea of what you wish to accomplish with the help of AI or do you need support? Whatever the case may be, we will gladly accompany you on your Data Journey.
Data Engineering
In order to consistently generate sustainable added value from Data, Data Use Cases must be successfully scaled and industrialized. As a result, the demand for Data Engineering Expertise has skyrocketed in the last few years. Leading companies have already proven the usefulness of (Big) Data, Analytics and AI in numerous Proof of Concepts (PoCs). Now they are faced with the daunting challenge of actually implementing the distribution, scaling and industrialization of these Data Use Cases.
We measure our successes by the amount of added value generated. Using our experience from over 1.000 projects, we have refined our Data Engineering Services into a holistic process and organizational model for the purpose of the industrialization of Data Use Cases. Our experienced Data Engineers, Data Architects and AI Engineers are there to support you during implementation.
Data projects implemented
Use cases identified
AI and Data Experts
years of experience
STARTING POINT
You want to apply Use Cases within your company. If you already have ideas of Use Cases, we will use our experience assist you in prioritizing and evaluating the feasibility and possible benefits of your selected Use Cases. Alternatively, we can work together to generate Use Case ideas in our own Roadmap Workshop in order to create the foundations for your Use Case library. If you already have a specific Use Case in mind, we can start right away.
PROCEDURE
Our goal is to test Use Cases as quickly as possible. To do this, we first create a Use Case concept by generating hypotheses for the Use Case and checking the necessary Data. In the subsequent exploration, we carry out a Proof of Concept and build a test environment using the Data you provide us with. In this way, we can quickly determine whether the Use Case in question can realistically be implemented. Following a successful exploration, we get to work on programming a first prototype, which can be seen as the Alpha-version of your Analytics or Big Data App.
If this manages to prove itself, the Use Case is then industrialized into a final product, with an absolute focus on scaling and the sustainable creation of added value. This means that here to the end user is the central focus. First we create a scaling plan, prioritizing markets, functions and brands, after which we move on to the next phase of the pilot, turning the prototype into a Minimum Viable Product (MVP) – the Beta-version of your Analytics or
Best Practice Methods
Use Case Workshop
Use Case Exploration
Prototyping
Hackathon
Scaling Conzept
MVP (Minimum Viable Product)
Results
In depth analysis and solutions for established processes
Analytical Concept and User Stories are made ready for Implementation in agile sprints
Technical feasibility is validated with a Proof of Concept
Established value generation thanks to productively deployable MVP’s and Prototypes
Detailed concepts for Data Science and AI-Problems
Data Science Use Cases
Use Case Workshop
Do you have a Use Case and want to find out how to move ahead with AI development? After presenting the status quo of the Use Case in question we use a Design Thinking Session to generate
References
of [at]
AI Development and Data Engineering
that defines us
Leader in AI and Big Data
We have been recognized as the # 1 Value Creator in machine learning by CRISP Research as well as a Big Data Leader in Germany by numerous experts.
Industry-specific know-how
We have implemented over 1.000 AI & Data Science Projects in over 15 different business fields.
Technology-independent consulting
We strive to find the right technology for our customers according to their needs and support them in implementing it.
FAQ
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How do you proceed in a classical data science project?
First of all we gain knowledge about your problem (domain and process), data and solution space (method and technology). An analytical concept including an evaluation strategy is then drawn up. 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] – Kompass the first two areas of competence “Business Processes” and “Data Intelligence” are an important part of our DNA. Additionally, we use a Data Visualization Guild, which deals with the optimal presentation of data and information.
In which industries have you already implemented projects?
To this day, we have implemented more than 1,000 successfully completed projects in a wide range of industries. These include Finance, Insurance, Automotive, Retail, Energy & Communication and much more.
In which industries does [at] bring domain knowledge?
Due to our many years of project experience in the following industries, we have in-depth domain knowledge:
- Automotive
- Energy & Utilities
- Finance & Insurance
- Transportation & Logistics
- Retail
- Consumer & Capital Goods
In a further 15 industries, we have also successfully completed projects.
How does [at] ensure the transfer of knowledge during the ongoing project to new colleagues and employees of the customer?
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 parties involved and also to new experts joining the project.
Have you ever completely implemented the Data Journey in a project?
Yes, together with our customer Vorwerk. You can find out more in our Vorwerk Case Study.
Data Engineering & AI Projects with [at]
Together with your colleagues from business, IT and if available Data Science we develop ML (Machine Learning) and AI (Artificial Intelligence) algorithms. For this purpose, the data compass offers us safe orientation in the data jungle. Just like a human, a machine 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 time, learn from experience and continuously optimize the algorithms. Our Data Science Delivery Model is the proven approach to quickly and effectively implement machine learning algorithms. The fast proof of value (PoV) is often the key to success.
We see the distribution, scaling and industrialization of data use cases as the biggest challenge in AI development today. When data prototypes are to be transferred from the laboratory mode into the existing organization, disillusionment often occurs. The prototype does not fit into the current system landscape, processes and governance are not clear, the required speed of information is not achieved and the costs for licenses increase
to unexpected heights. We support you in the question of how you can ensure the operation of the Data Product in your company at a reasonable price and analyze in beforehand the risks that could cause the roll-out to fail.
Within a few weeks, we plan to gradually scale and industrialize the prototype. We conduct many stakeholder interviews in order to plan the quickest possible way to reach a broad audience. The analysis of the technical framework and 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 expansion stage based on the scaling concept. The robustness of the prototype application is now being put to the test by other users. Further functionalities and larger data sets will be added during the pilot phase. This allows us to quickly obtain test results from real situations. By means of extensive tests and constant monitoring of the individual system components for troubleshooting, we develop the pilot into an operational product.
Data & AI Knowledge
Creating joint value from Data & AI