May 31, 2019 • 3 min read
Intwixt helps customers design business workflows that can be deployed to messaging apps like Slack or Messenger. They’re simpler to deploy than traditional Web and mobile apps, but new challenges like usability are still being solved.
When creating a conversational UI, it is important to accurately interpret the user’s intent.
Machine Learning is the key to dealing with these behavioral patterns. An intelligent service that uses ML can learn from the conversations with Johnny and infer that there is a very high probability that if he orders food on Tuesday, that food is vegetarian and can learn from the conversations with Marry that on Saturday she is likely to order vegan food (the service won't really know that Susan is coming for dinner but that doesn't really matter 😃).
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?
To answer these questions and address the challenges, Intwixt has been searching for a lightweight ML component to add to the interactive workflows.
Earlier this spring, Intwixt requested a demo through our website to see if Aito’s predictive database could be a step in the right direction. Intwixt got access to a free Aito environment and started the experimentation independently. Quickly they noticed that this was the kind of solution they were looking for. They were able to integrate Aito to the Intwixt platform in one day.
Contextual awareness is key to solving usability challenges and this is where Aito shines. If, for example, you need to collect multiple fields of information from a user, you can use Aito to predict what the user will most likely say. Instead of prompting the user to enter successive inputs, the app can prompt them once to confirm what they’ll most likely do.
Intwixt users can now choose Aito as part of their interactive workflows. Possible use cases are predictions, smart search or recommendations. When creating a workflow, the UI allows you to create a Data Model, which can be used to create tables in Aito or other databases. This makes it more fluent to manage data, whether it comes from one database or another.
Each time the user confirms the prediction (and even when they don’t), Aito’s APIs make it easy to train the model and increase predictive accuracy. It’s a great enhancement to usability without having to develop and manage complex ML models yourself.
“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.
The flow begins when a user enters a message in Slack (e.g, "play colors game"). The Aito service is then called to predict which color the user will likely choose. No matter the path taken, the final choice is fed back into Aito for a full feedback loop to improve prediction accuracy over time. It's a simple use case, but it reveals how approachable ML concepts can be when wrapped with familiar data interaction semantics like read and write.
More detailed information on how to set up the predictive Slack workflows , including a demo video can be found from Intwixt’s tutorial.
Would you like to try out setting up an AI-enabled, interactive workflow yourself?Back to blog list