Approachable AI — Accessibility (part 3)

Jeff Kimmel
Oct 16, 2020 · 3 min read
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Photo by Omar Flores on Unsplash

Elipsa’s mission is to create Approachable AI, enabling organizations to scale by empowering their business users to assume the role of the data scientist through a no-code solution. The three pillars of this mission are useability, explainability, and accessibility. This is a three-part series on those pillars and how a focus on each of these will enable broader adoption of predictive analytics and a faster journey from data to insight.

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In our first two posts, we discussed the need to make systems easy to use and easy to understand in order to have Approachable AI. The first and final pillar is Accessibility. On the surface, most would think this is the most obvious but also assume that it is the easiest and that it exists in all software today. After all, how can you use a platform if you can’t access it? However, Approachable AI is less about access to the platform and more about access to the insights.

Approachable AI is about push not pull.

Answers and Insights Where You Need Them

It sounds intuitive and common sense but many systems fall down in this area. They load their platforms with all of the bells and whistles to draw you in. To make the insights accessible, we understand that we need to provide a useable and explainable interface to actually draw the insights out.

Once models are built, the elipsa platform allows users to run predictions against new data right in the system. However, we know having to log into yet another system has the potential to bring your productivity to a halt. Through our APIs, users can connect their data to the elipsa pipeline to load in new information and directly feed the results back out to the application of their choice.

Software that adapts to your process. Not the other way around


Join the free elipsa beta program to start experiencing approachable AI for yourself.


Approachable AI for Business Users

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