Artificial intelligence

Our approach for an intelligent future

Analyze the situation

In order to start your intelligent future, it's important to analyze the current situation. Create a futuristic intelligence dream for your organisation. Locate time-consuming inefficient jobs or routine task that could be made smart using AI so employees can focus on what really matters and increase added value for your business.

Analyze the situation

  • What are routine tasks?
  • What are time-consuming inefficient tasks?
  • Describe AI-dream situation
Analyze AI-dream with expert

In the next fase discuss the futuristic intelligence dream for your organisation with one of our AI-expert. Aside from advising you on how to achieve your AI dream, he can inform you more on the AI-learning process. On top of that, he might come up with new ideas that will raise your business efficiency even further.

Analyze AI-dream with expert

  • Can AI provide added value?
  • Determine added value of AI
Collect data

Data is one of the most important necessities for AI. It is used for the AI to learn about the problem and it's environment by continuesly looking for patterns and learning from those patterns.

Collect data

  • What is relevent data?
  • How can relevant data be obtained or generated?
  • Is the data in balance?
Development of the AI-model

The collected data needs some pre-processing in order for the AI to use this data and search for patterns. This pre-processing is an important step to speed up the learning process, create relevant features and to extract patterns from the data. After pre-processing the AI is trained on obtained data and tested against unseen data to get an accurate view on the performance and accuracy the AI gives.

Development of the AI-model

  • Pre-processing of data
  • Train AI-model on obtained data
  • Test AI-model on unseen data
Model implementation

In the final phase the trained AI needs to be implemented using the business infrastructure. It needs to be able to collect more data of incorrect classified cases .This data can be used for further training to increase the performance and accuracy of the AI and learn from previous mistakes.

Model implementation

  • Implement model at customer
  • Obtain new data for further training