Artificial Intelligence and Data Science
About 90% of all data science and AI projects get stuck in the proof-of-concept or analysis phase. Find out how our approach is different, which steps we’re taking to change that, and how your company can benefit from AI applications, regardless of the industry your business is in.
In a nutshell
Data science project phases
To offer a more structured approach and make the project successful,
we have defined four phases of each AI / ML / data science project we implement
In an initial meeting with our team, we discuss your ideas, business cases and problems that could be potentially solved using state-of-the-art data analysis models or AI algorithms. This meeting is free of charge and serves to get to know our team and understand our approach better. Based on this initial consultation, there are two different ways to proceed.
We can either start with implementing one of the standard use-cases from our service portfolio or we can organize a so-called data thinking workshop in case no specific AI use cases were initially identified. Usually, 1-2 of our data science and software experts and several of your business experts participate in this workshop. As a result, we create a concrete road map with all the use cases identified together and prioritize them according to business impact and effort. This roadmap lays the foundation for establishing data science and AI in your company for the long term.
Some of the (standard) use cases in our service portfolio
— Demand and price forecasting
— Customer lifetime value prediction
— Next best offer/action
— Customer segmentation
— Cross- and upselling optimization
— Dynamic Pricing
Text recognition (NLP/NLU)
— Automatic e-mail classification
— Sentiment analysis
Fraud and anomaly detection
After examining your data landscape and identifying use cases during the explore phase, we now take a PoC (proof of concept) approach.
The primary goal is to implement a use case with high impact and low time effort. In this way, we ensure that we sustainably increase the acceptance of Data Science and AI in your company. This phase also includes the precise data analyses carried out by our experts and the prototype development.
Following the previous phase, we continue directly with the build phase. The business risk for you is manageable at any point throughout the project, as we end each stage with a review and only move on to the next phase once all requirements have been met and you have expressed a clear desire to continue working with us.
In the build phase, the focus is on the targeted integration of our prototype into your IT systems, apps or websites. To achieve this, we rely on state-of-the-art software development methods and closely examine testing, continuous integration and delivery. At the end of this phase, the pilot is ready to be used in the operational business.
After a 1-3 month pilot phase, we will discuss the next steps with you and offer you different service packages to ensure smooth operation in the long term.
Our service packages will then include regular tests of the models and a re-deployment, if necessary.