The boost.ai platform
Get a quick overview of what virtual agents are, the parts that they are made of and how they work within the boost.ai platform.
Model training
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How boost.ai leverages generative AI
Get an overview of how boost.ai build and implement high performing virtual assistants by leveraging Generative AI within our custom platform.
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Working with context topic
In this course we'll teach you all about context topic: the virtual agent (VA)'s capability to remember what the user was talking about. This ensures a much smoother conversational experience, and improves customer satisfaction (CSAT).
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Get started with the downloadable clean-up report
In this course, you'll learn how to start using the downloadable clean-up reports to improve your model. It contains tips and tricks on what to work on first, and when you should prioritize which report.
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Building at scale with objects
In this path, you'll learn how to use objects. Objects are reusable templates for intents, training- and test data, and even responses. They allow us to create hundreds of intents in just a few hours, and can make building, managing...
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Building virtual agents with boost.ai
Building virtual agents might sound like a big task, but it's not that bad so long as we approach it systematically. This course is all about giving you an overview of how it's done.
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What is a virtual agent?
Not quite sure what a virtual agent is, or what it can do for you? In this course we explain the basics.
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How does a virtual agent work?
In this course we will focus on how the virtual agent (VA) itself works and how we build it
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What parts make up a virtual agent?
Are you new to boost.ai's platform, or curious what it is? In this course we explain how our platform is set up, in broad lines, to help you understand how we help you deliver great virtual agents.
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Supercharging Productivity and Build Time
How are generative AI and large language models redefining conversational AI? That's the question we are going to explore in this course.
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Giving the best response: Generative or predefined?
Let's explore the building blocks of conversation design: generative and predefined responses.
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Covering a wide range of topics & tasks in days
We can combine a variety of different methods to create a virtual agent ready to answer questions on thousands of topics. In this course we will se what they are and their strengths.
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Teaching our VA to understand & respond to users
In its simplest form, a virtual agent needs to understand what users are saying and respond appropriately. In this activity, we are going to dive into how we build understanding and create responses in the boost.ai platform.
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Implementing a virtual agent with boost.ai
Curious to find out what a project together with boost.ai looks like? In this course, we outline the typical steps we take together to create a great virtual agent project.
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Building an intent hierarchy
The intent hierarchy is the core of the boost platform. It is here that the scope, what your virtual agent is supposed to know, is organized. This course covers how to build and navigate your own intent hierarchy.
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Train a custom NLU model
For the virtual agent to understand end users it needs some kind of model, and the boost platform allows you to train your own custom NLU model. This course takes you through that process, including training data and test data.
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Introduction to language processing
The language processing algorithm is a central part of how our custom NLU model understands languages. In this course, we will see how big of an impact this algorithm can have on model performance and how it works.
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Working with clean-up reports
The clean-up reports are built-in reports that tell you where to improve your trained NLU model. In this course, we will go through some of the more important reports and give you an idea of where to start.
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Improving your model with test results
Test results are the ultimate tool for benchmarking and improving a custom-trained NLU model. This course introduces the various kinds of issues we can find here and how to resolve them, improving prediction accuracy.
