Intelligent
Applications 40

Introducing the IA40 — the top private companies
building with AI that will shape our future.

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Why this list?

Machine Learning and Artificial Intelligence are taking the technology world by storm. As they have matured and become more widely adopted in the developer community, the potential they have when deployed in business applications, has started to become more apparent. As investors, we see everyday the incredible effect that emerging applications using AI and ML can have to shape our lives. It is these applications that will define the future of software and the next generation of computing.

The IA 40 is a research-driven ranking of these intelligent applications, across four different categories.  We at Madrona Venture Group deeply thank co-sponsor, Goldman Sachs, and the 40 top venture capital firms that thoughtfully nominated finalists and then voted on companies they believe are poised to succeed.

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Tesorio Co-founders Carlos Vega and Fabio Fleitas
Tesorio Co-founders Carlos Vega and Fabio Fleitas
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Defining Intelligent Applications

We define application intelligence as the process of using machine learning models embedded in applications that use both historical and real-time data to build a continuous learning system. These learning systems solve a business problem in a contextually relevant way - better than before, and typically deliver rich information and insights that are either applied automatically or leveraged by end users to make superior decisions.

Intelligent applications are:

  • Extracting and cleaning data from more than one source, and aggregating it in some form of a data lake/warehouse
  • Preparing the data and then using it to train and test machine/deep learning models
  • Refining those models and deploying them in applications to generate contextually relevant predictions, recommendations, inferences or insights
  • These applications combine the above "dataware" and cloud-native software, built on a micro-services architecture, to deliver results in real time
  • Applications enabled to continually monitor and improve the underlying models and predictions
  • From an end user (or machine) perspective, leverage modern APIs and user experiences
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