Data School
We help to understand if and where the usage of data and AI makes sense. What the challenges are and how to overcome them.
Where to use it?
Data and AI have a massive impact on all industries. However, it is often not clear how to profit from these technologies. Our experience allows us to quickly identify possible use cases.
How to implement?
Software development is challenging. Technical as well as cultural obstacles need to be tackled. With more than 10 years of software development experience we are able to assist you.
Data Infrastructure
We plan and implement data architectures and MLOps platforms. This enables our customers to collect, store and process data effectively as well as to use AI in production.
Data Product
We build data and AI driven software products which reduce costs and risks on one hand and increase profit and user experience on the other.
1. Assessment
The assessment step is used to capture the status quo. Among other things we clarify which data sets, digital infrastructure and competencies already exist in the company.
Based on this assessment existing product ideas are specified and new product ideas generated.
2. Prototyping
We will take the most promising ideas from the previous assessment step and implement them in a prototype.
Prototypes allow us to validate ideas quickly and optimize them iteratively based on relevant metrics and user tests.
It is important to us that the usage of AI adds real value to the product. We will not use AI for the sake of AI.
3. Productionizing
Once satisfied with the prototype we will put it into production: secure, performant and maintainable.
We bring experience from the cutting edge of the industry. We know how leading internet companies are successfully using Data Engineering, Data Science and Software Engineering in practice.
Infrastructure topics like data architecture and MLOps are our bread and butter.
4. Maintenance
AI based products are complex and the field moves quickly.
We will help you to maintain our products and improve them based on the newest research results.
Relevant metrics and KPIs, in combination with online and offline testing, allows us to generate the best possible models.
