How we work

You want to develop new business models with the help of AI? We support you in all stages of your data journey beginning with the identification of possible use cases to their successful operationalization. Build on our many years of experience - step by step and always at your side!

We at paiqo share our enthusiasm for AI and data platforms. We have a standardized process that has already proven itself in many projects and it is constantly improved and adapted based on new technologies and trends. This process consists of the following.

Machine Learning Prozess

Every journey begins with a first step. To help you plan the length of your journey, we have outlined the path for you:

Envision

A data science project starts with a use-case workshop. It is important to include as many stakeholders and domain experts as possible to obtain a holistic view of the current situation, available data sources, limitations, the project goals and success criteria.

The objective of this phase is the precise definition of the project goals based on measurable criteria, the setup of the project team, the identification of data sources and the creation of a project plan.
Thanks to our many years of experience, it is already possible at this stage to avoid the first stumbling blocks caused, for example, by an incorrectly selected goal or data source/granularity.

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Approximate time frame: 1-2 days

Connect and Explore

As a next step, extracts of the selected data sources are loaded into a laboratory environment separate from production systems. Here, the data is checked for completeness and quality and data cleansing is performed, if necessary. In addition, data from different sources is joined and transformed from its raw form into a format suitable for machine learning.

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Approximate time frame: 2-4 weeks

Find Value

After the data is available in a usable format, it is important to create a first working version of the Machine Learning Pipeline to evaluate the results against the defined success criteria.

The evaluation of the results helps to iteratively refine and verify the improvement of the machine learning model and avoids expensive go-live of insufficient results.

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Approximate time frame: 2-4 weeks

Integrate

When a successful machine learning model was created in the laboratory environment, it is time to integrate the developed solution into your operational processes.

This includes the deployment of the model to continuously generate new predictions and the creation of an automated data pipeline to load and prepare new data. This phase is completed by establishing process monitoring and performing final performance tests in live operation.

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Approximate time frame: 1-2 months

Your Benefits

Fast results 

Our iterative approach quickly shows whether the planned path leads to success or, if necessary, allows for adjustments.

Proven approach

Our standardized process has proven its worth in many projects and is continuously improved.

End-2-End

At the start of the laboratory phase, we already have the operationalization in mind to make the results quickly usable in your day-to-day business.

Our Partners

With our strong and innovative partners who set new standards in their end-to-end platforms, we jointly pursue the goal of successfully implementing AI & Analytics in all business sectors.

You want to gain more value from your data with artificial intelligence and are looking for an experienced partner?

We support you in your digital transformation from the concepts to actual implementation.