AI sprint - one journey from data to prototype

We enable your teams to build working data products

AI sprint is a format focused on building functional prototypes with your data sources

Do you have specific business use-cases that you would like to validate? Do have data sources in mind that you would like to leverage but no time or internal know-how to start the journey?
We are here to help you! No matter if you need support on the full journey or only on parts like initial data analysis and validation.

The most common modules we offer are listed below - but feel free to reach out for individual requests

1. Datasource identification

Together we identify and review all datasources available in your business unit or company. More often than not clients are focused on single sources like SAP and forget unusal internal or external datasources on the way

2. Use case definition and Data acquisition

After clearly defining the overall goal that should be reached with the help of data we define a potential setup to gather the needed datapoints. Taking into account governance, technical feasibility, licensing cost and other factors

3. Data cleansing

While nobody wants to talk about it reality is that 80% of data science projects are about getting and cleansing the required data. We will help you with this step and make sure that you build your analytics and machine learning efforts on a solid foundation.

4. Data exploration

With the previously defined use cases in mind we explore the cleansed datasources and make sure that our final goal is in line with the available data quantity and quality. If there are any problems at this step we can still go back to acquisition and cleansing while saving a lot of money spent further down the process

5. Prototypes

For each of the desired use cases we will now build a functional prototype to proof the value add. Together with all relevant stakeholders we will iterate on the prototype until we are sure to fulfill the need while having great usability. Only then will we proceed to our final step of building a product

6. Machine learning product

After ensuring that we have sufficient high quality data and can fulfill the stakeholder product requirements we finalize all steps in a fully working data product