Building intelligent Slack workflows

Extract behavior patterns from user conversations

Now, the technical challenge for dealing with these behavioral patterns is that traditionally, ML has been very difficult to use. ML is often referred to as a team sport and certainly, when it comes to very complex ML models, it takes a great effort and a team of people (e.g. data scientists, AI engineers, developers, etc.) to integrate ML into applications. However, does it have to be that hard to build ML models from conversational data and build intelligent, ML enabled bots?

Using Aito to make the interactive workflow more intelligent

“We value the broad applicability of Aito — a general purpose tool for automation tasks. Aito can organically grow with new content, and new queries and workflows can be iteratively developed,” says Sabin Ielceanu — Co-Founder at Intwixt.

A simple example

Creating an AI-enabled Slack workflow in Intwixt’s UI



-- decision automation in the cloud. #ML for #nocode and #rpa operators.

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