The next big shift:
Artificial Intelligence

The Effect Network is an AI framework that empowers AI development for enterprise. Artificial Intelligence is considered a key component of the Fourth Industrial Revolution. Over the next decade the AI industry will grow exponentially and cause the biggest technological shift we have ever seen.

AI to drive GDP gains of $15.7 trillion with productivity and personalisation improvements.

Turning artificial
intelligence into
business value

For businesses, AI will enhance efficiency and throughput, while creating new opportunities for cost savings and revenue generation.

The powerful possibilities of AI technology are already solving business challenges and will continue to drive innovation: from healthcare to cyber security, from manufacturing to the automotive industry.

How it works

Tomorrow’s technology
accessible to everyone now

The Effect Network allows everyone to access AI solutions and start the innovation of business processes. From improving AI accuracy to collecting new data-driven insights, the Effect Network provides easy and versatile solutions for rapid experimentation in the field of AI development.

Get started with
AI today

AI implementation is a long journey. By starting today you prepare for the next major technology shift in your industry. Repetitive tasks within organizations often form an ideal starting point for AI automation. By starting small and scaling up through encouraging rapid experimentation, you can build a solid foundation to bring value to your organization and your end-users.

Structure your data with Effect Force and start your AI initiative

Explore Effect Force

Effect Force Case
Sentiment Analysis

Effect Force enables organizations to deliver fast and accurate sentiment scores with the help of an on-demand WorkForce. The sentiment scores open up business insights, and the accumulation of these accurate sentiment scores form the foundation for high quality training data.

We offer two ways human knowledge can be incorporated in ML models:

  • Workers help label the initial training dataset that will be fed into the ML model (Supervised Learning)
  • Workers help correct inaccurate predictions once the system goes live (Active Learning)
Read more about Sentiment Analysis