In this first of two-part posts where I’m going to detail instead all the steps to build a complete Food Delivery chatbot. Thanks to Xenioo’s built-in AI, Database, and advanced Conversation features you’ll see how it is possible to realize such a powerful and smart chatbot without writing a single line of code.

The How To Build posts series aims to show how a complete chatbot can be built using all of the available Xenioo features with the highest possible detail. By the end of each post, you will gain insight into basic and advanced Xenioo functionalities and be ready to try more complex approaches. All of the additional material used for these posts and the complete chatbot are made available here for download or inside the Xenioo templates section.

The case for a Food Delivery Chatbot

During the last two years, we’ve witnessed tremendous growth in e-commerce chatbots. By either working as “on the side” consultants or being the first line of sales, these chatbots are for all intent and purpose driving thousands of product sales. The Food industry, and in particular the home delivery services part of it, has definitely embraced this trend by acquiring all the good aspects of this kind of revolution:

• Your chatbot can work with multiple customers simultaneously. You’re not missing any more phone orders because customers find a busy line and stop waiting;
  
• If your restaurant is also serving seated customers, no one needs to answer any call and get distracted from regular service;
  
• Orders are written clearly and checked multiple times by your customers. No more wrong orders because of over-the-line misunderstandings or badly written notes;
  
• Your customers can update orders anytime, even after placing them, instead of calling your restaurant multiple times;
  
• Doing some kind of promotion? Reaching out to your previous customers would be a lot easier than putting out paper fliers no one is reading.

If you feel like your business could benefit from one or more of these improvements keep reading as we’re going to build the definitive food delivery chatbot!

Building our chatbot

The first step for building a successful chatbot is to have a list of the main features we’ll need to implement so that we know how to set up our flow. A food delivery chatbot needs to be capable, at the very least, to:

1. Take orders from customers using a free dialogue approach;
2. Allow customers to review, edit and confirm their orders;
3. Take direct payment from our customers;
4. Get customer location to set up a delivery route;
5. Engage with our delivery personnel, dispatching orders and routes;
6. Let the customer check the order state;
7. Collect service feedback after the order has been delivered;
8. Signup our customers for a promotional campaign so that we can engage them with new offers;

Not a bad list of features right? Now let’s see how we can tackle all of these points with Xenioo: let’s create a brand new chatbot in our account and let’s start building!

Creating our Food Menu

In order to allow our chatbot to get orders we need to first create some kind of menu. Our menu will contain all of the information about each and every food served by our imaginary restaurant.

To create our menu we will use all the power of the Xenioo Database feature: we’ll store in our menu collection all of the information we need to display, search and categorize the food we sell.

Let’s head to the Database section of our brand new chatbot and create a new collection named “menu”. We will be adding to menu items data such as name, description, price (obviously!), image, and, since we’re at it, also possible intolerances that our customers may be worried about.

Using the group property of our collection fields we’ll make sure to split our data into different sections so that Xenioo will build a nice data-entry form for us, without writing a single line of code. Once our collection is created we can head to the add new button and select “Add new record with form” to start our data entry.

Xenioo has automatically built for us a complete data entry form with the right input control for each of our fields. Boolean fields are shown as checkboxes, numeric fields have already set decimals or whole numbers and file type fields can manage your attachments and automatically store them in your account storage. All we have to do now is add all of our menu items.

As we have seen also in our previous Let’s Build Post, records can be exported and imported directly using simple CSV files. This makes it incredibly easy for you to manage your data: if you happen to have already a menu in an excel format or, later on, if you wish to maintain your whole food offering using excel. Better yet, if you do not wish to create collection fields manually, you can just import your CSV file directly and Xenioo will import all columns as fields and do all the heavy lifting for you.

Using Xenioo granular users permissions activated by the Pro Team package, it is also possible to create a user that can access only the Database section of the chatbot. Just imagine building a chatbot for your restaurant customer and then giving them a full back office to let them maintain their own data. How much development time would software like this take usually?

Training our NLP Model

Once we’re happy with our menu data we can move on to training our NLP model. What we’re going to do next is basically teach Xenioo how to handle the different requests that may come from our customers. To do this, the first thing to do is to move to the AI section of our chatbot and create a brand new intent.

Within a chatbot, intent refers to the goal the customer has in mind when typing in a question. For our restaurant, the very first intent will be “Food Ordering”.

Let’s go ahead and create the intent by clicking on the add intent button. All we need to do now is just give it a name and click on Save. We’ll be back later to add more details.

Once our intent is created let’s double-click on it to move to Expressions editing. In an intent, expressions are the utterances we’re expecting our customers to use to express their goals. We can set up some typical examples now and fine-tune our training later, even while our chatbot is live.

For our restaurant, a Food Ordering intent may have expressions such as “I want a pizza” or “I would like two margherita and a blonde beer” and so on. Let’s add some typical expressions to our expressions list.

You don’t really need to think of all of the possible ways your customers may order food. Just write some of the most typical and the Xenioo engine will be capable of understanding also expressions that diverge from what you’ve entered.

Of course having our chatbot understand our customer’s expressions is just half of the job: what we really need is to understand what exactly they are asking for. Is it a pizza? If so, what pizza? Do they want a beverage? What exactly do they want to drink?

The information that can be found inside an expression is called Entity. An entity represents a value that can be extracted from the expression and later used by your chatbot. For example in the expression “I want a pizza margherita” what we really need to know is that the required pizza is Margherita and that our customer wants just one.

Usually, we would just go ahead and click on the parts of the sentence that may represent our entities but since we already built our menu we could make things even faster and use our database as an entity list. Let’s move to the Entities section and create a new entity called “food” where we say that the data is coming from our collection.

All that we need to do here is select our collection and pick which fields we would like to represent the possible entity name and the possible synonyms of our entity. Once we’re done with the creation we can just hit save to confirm our entity.

Now, all we need to do to make Xenioo apply our entities to our expression is head to the Train & Test section. After a very short while, Xenioo will finish modeling our expressions. If we go back to our Food Ordering intent and double-click on it we’ll see how our expressions list has changed.

As you can see now some of our words are highlighted. That is Xenioo showing that some of the words are now recognized as entities. It means that the values that will be found will be made available to our chatbot during the flow.

Let’s try our NLP training by clicking on the Train & Test section again. On the input box, to test our training, we type “I’m eating a margherita and a cold blonde”. Press enter or click on Evaluate Expression to see the results.

The sentence is way different from any other we’ve been training but still, Xenioo manages to get a 61% score best of all, as you can see, we get our food recognized automatically. If we were in a real conversational situation, $food and $food2, our entities, would be available to our chatbot as standard variables, ready to be used in any part of the flow.

Speaking of flow, it is time to move to the build section and start creating our chatbot!

The post HOW TO build a complete food delivery chatbot (Part 1) appeared first on Xenioo.

In fact, WhatsApp has over 2 billion users around the globe.

But did you also know that you can automatically chat with your customers via WhatsApp? That you can have one-on-one conversations with your customers, at scale?

If you didn’t, it is possible.

And in this article, I will explain what WhatsApp chatbots are, why you need to use them, and how you can create one yourself using Xenioo.

What is a WhatsApp Chatbot?

A WhatsApp chatbot is an automated conversation partner on WhatsApp.

Usually, when you chat on WhatsApp, you are chatting with another person. But with chatbots, this is different. Because you are not talking with another person, but with a computer.

The benefits of a WhatsApp Chatbot

Because you can automatically talk with your (potential) customers, you can have one-on-one conversations, at scale.

This means that your chatbot can have personalized conversations with each one of your users and it could be talking to thousands of people at the same time.

So, what are the benefits of such a WhatsApp chatbot?

First of all, a WhatsApp chatbot can have a significant impact on your customer service. For example, you can drastically reduce your customer service costs. Your customer service probably handles a lot of standard questions every day and it is proven that 80% of these questions can be easily answered by a chatbot. Besides, your chatbot can reply in seconds to customers’ questions instead of hours (or even days). Moreover, your chatbot is always available. Instead of using live chat where a customer needs to wait on an available live chat agent, you can use a chatbot that is always ready to respond to questions.

Secondly, you can boost your sales by using a WhatsApp chatbot. With a chatbot, you can directly sell your products to your customers and let them pay via the chatbot. Think of it as like an eCommerce store inside your chatbot. Another way would be to promote your products and services via the chatbot.

The third benefit of chatbots is that they can automatically qualify your leads, which is especially useful for B2B companies. In most B2B companies, there are some qualification rules to determine whether a lead could become an actual client. They ask questions like:

• What industry are you in?
• How many employees does your company have?
• What is your role inside the company?

But often these questions are asked by humans. But why would you not automate this? The chatbot can ask these qualification questions and if the lead is qualified, he/she will be forwarded to a sales agent (that can close the sale).

So in short, a chatbot:

1. Can have personalized conversations, at scale
2. Drastically reduce your customer service costs
3. Can reply to your customers in seconds
4. Is always available for your customers
5. Increase your sales
6. Automatically qualify your leads

Why WhatsApp is the best platform to engage users

So now we know why we need to use a chatbot, why should we put it on WhatsApp?

As said before, WhatsApp has over 2 billion users worldwide.

From these 2 billion users, 1.6 billion access the app on a monthly basis. This results in 65 billion WhatsApp messages that are sent on a daily basis.

With so many people on one channel, it is safe to assume that your customers are also using it.

And you need to be on the channels where your customers are.

Different types of chatbots

In general, there are two types of chatbots:

1. Standard-guided chatbots
2. Pure conversational chatbots

Or you can have a mix of both (probably the best way).

Standard-guided chatbots

Standard-guided chatbots are chatbots that are mainly based on rules. These chatbots often use buttons to guide the user to a specific part of a conversation. Also, the user often cannot talk freely to the chatbot, because the chatbot is not smart enough to handle it.

Benefits of standard-guided chatbots

• Good for qualification of leads
• Control of the conversation flow

Drawbacks of standard-guided chatbots

• Falls short when users want to ask questions freely
• Can lead to frustration

Pure conversational chatbots

Pure conversational chatbots are more advanced types of chatbots. They can understand the intent of the user and the context of the conversation, by using Artificial Intelligence techniques. With these types of chatbots, the user can ask anything they want, and the chatbot probably often provides a good answer or redirects the customer to a live chat agent.

Benefits of pure conversational chatbots

• More natural conversations
• Chatbot can understand the intent of the user
• Chatbot can understand the context of the conversation
• Users can ask more advanced questions

Drawbacks of pure conversational chatbots

• Higher costs, because of Artificial Intelligence techniques
• Development of the chatbot takes longer

Create your First WhatsApp Chatbot

So, now we know why we need to use a WhatsApp chatbot, let’s create one ourselves using Xenioo.

And as an example, we will create a chatbot for a Pizza restaurant.

First, go to the Xenioo console and log in with your account credentials.

(If you don’t have an account yet, you can create one here).

When you are logged in, you can click on Create New Bot:

This will automatically create a standard chatbot flow for you, which should look like this:

So, let’s start by editing the name of this flow to “Welcome Message” by clicking on the gear icon on the top-right corner. Then, you can just type in “Welcome Message” and hit Save: 

Before we are proceeding, I recommend that you already create three chatbot behaviors in Xenioo. You can do this by clicking on the Add new behavior icon in the top-center:

What are these chatbot behaviors? These are for:

• Ordering a pizza
• Making a reservation
• Showing the menu

Now, head back to the Welcome Message behavior and go to the Start Interaction. You can just change the text by clicking on the element and type in a new message in the right panel and clicking on Save:

Then, we need to make three buttons for the Order pizza flow, Make a reservation flow and Showing the menu flow.

We can do this by clicking on the gear icon of the start interaction and then clicking the Add new action: 

Here, you can select the Quick Reply Button:

Next, you can set the text of the button with Button Title and set the “Go To Operation”, which will move the conversation to another interaction:

NOTE: WhatsApp doesn’t support showing buttons. Instead, the buttons are shown as plain text as “If you want to go to the menu, type in 1”. 

Now you can do the same for the Making Reservation button and Menu button, so it looks like this:

Before we move on, I want to tell you about two awesome features of Xenioo regarding buttons.

The first one is called the “Alternate text”, which displays an alternate text for text-based channels (such as WhatsApp). 

So one a more visual channel (like Facebook Messenger), Xenioo will show the Button Title, but if you have set the “Alternate Text”, this will be shown on WhatsApp:

With this example, the button on Facebook Messenger will show ‘Menu’, while on WhatsApp it will show “See our menu”.

The second feature is called “Trigger Text expression”. Let’s say the user chooses not to use the buttons, but instead just types in “menu”. Then, your chatbot will break (because he is not familiar with that). This feature fixes that, so if the user types in “menu”, it will go to the appropriate flow:

Now, let’s first create the ‘easy’ flows: Make a reservation and showing the menu.

The Make a reservation flow is also pretty straightforward for this tutorial, just tell the user that they can make a reservation via a phone call or your website:

With the menu, we need to change the text and add the File Download action:

If you select this, you can set the URL of the your Restaurant menu:

So the Menu flow looks like this:

Next, we are going to do the more advanced part: Ordering a pizza.

Before we are going to deep dive into this flow, start by creating five interactions and name them like this:

• Check on toppings
• Check on size
• Pizza with topping and size
• Pizza confirmation
• Start over

You can do this by clicking on the Add new interaction icon in the top-center:

And naming them accordingly by just clicking on them and changing the Name:

Now, head back to the Start interaction of the Order pizza flow and start by asking a question:

NOTE: We will handle the response to this question later on in the NLP part, so don’t worry about that now

Next, we are going to set the actions for the “Check on toppings” interaction. This interaction will check whether the user has typed in a topping. If the user has filled in one, we will move on to the next interaction and otherwise, we will go back.

We can do this by selecting a Variable Switch as action (Under the ‘Flow’ section):

And check whether the topping has any value:

By the way, ‘topping’ is a variable name I created. You can just type in a variable name and click on “Add new variable” and Xenioo will automatically create it.

Accordingly, we need to determine what happens if the topping has a value. And we can do this by clicking on Add operation and select the Go To Bot interaction:

The Go To Bot interaction will redirect the conversation flow to the next interaction (Check on size):

But what happens when the user didn’t type in a valid topping? Then, they will go back to the previous question (using another Go To Block action):

With the next interaction (Check on size), we are doing a similar thing, but then we check on the size of the pizza, instead of the topping.

So, we will add a Variable Conditional Switch to this block that checks on the size:

If the size already has a value, we continue with the “Pizza with topping and size” interaction, and otherwise we are going to ask a question.

NOTE: Now, you might be thinking “Huh, but we didn’t ask for the size, yet?” That is completely true! We will cover this in the next section where we will handle the NLP part of our chatbot

Now, let’s create the question that asks for the size of the pizza. You can just add the Text action and type in “What size pizza do you want?”:

Then, we need to add three buttons for “Small”, “Medium” and “Large”, which all go to the next interaction. For each of the buttons, click on Add Operation and select Set User Variable (under Profile):

We can use the Set User Variable to change the variable size to small:

NOTE: Don’t forget to also add the Go To action, which makes sure the flow goes to the next interaction (“Pizza with topping and size”)

Now, you can do the same for the Medium and Large buttons, so it looks like this:

Lastly, we need to ask whether the user is satisfied with the chosen toppings and size of the pizza. You can do this by adding a Text action and two buttons for Yes and No.

Afterwards, you can connect the buttons to the right interactions. If the user is not satisfied with the pizza, the chatbot can start over again by connecting the “Start over” interaction with the first interaction.

Because we already covered all the things you need to know to do this, I assume you can handle this yourself

Here is the final flow of the Order pizza behavior inside Xenioo:

Use NLP to make your chatbot smarter

What we’ve created now, is what you would call a standard-guided chatbot.

But, as you might remember, we still need to handle the input for the toppings and size of the pizza, so that people can just type in what pizza they want.

We can do this by using the NLP feature of Xenioo. 

Thus, let’s start by creating an intent. An intent is an action your user wants to do, such as ordering a pizza. So let’s create an intent for exactly this.

In Xenioo, you need to go to the AI section and then click on Add intent:

Here, you can type in the name of the intent and click on Save:

Next, we need to give the chatbot certain training phrases, so it knows when this intent is triggered. You can do this by just typing in phrases and hitting Enter:

Then, we need to make sure that the chatbot understands the difference between a veggie pizza and a mozzarella pizza. We can do this by using entities. 

Entities are useful facts the user is saying, such as the size of the pizza or the kind of topping they want. In Xenioo, you can just click on one of the words and tell Xenioo it is a size:

And you can do the same for toppings:

You can extend these entities, by going to the Entities section, click on the three dots and choose for “Edit”:

Here, you can extend the entity values. For example, for topping you add more toppings, such as:

• Beef
• Sausage
• Bacon

And we can also do that for the size entity:

But, how are we sure that this is actually working? By using the Train & Test feature of Xenioo. You can just type in a sentence and Xenioo will give you the matched intent and entities:

There is just one last thing, how do we use this inside our chatbot flow?

Go back to your Order Pizza behavior and choose for the Start interaction. There, insert a new action called NLP Input (under Input):

Then, you just need to make sure that if it matches the Order Pizza intent, that it automatically goes to the “Check on size” interaction:

Chatbot Fallback: What to do if the chatbot doesn’t understand the user

But what happens when the chatbot doesn’t understand the user?

Then, you need to make sure that there is a human that can ensure a good user experience.

And I have written a full guide on this, that you can read here.

Deploy your chatbot on WhatsApp

Now we’ve created our complete WhatsApp chatbot inside Xenioo, we can deploy it on WhatsApp.

And Xenioo does provide a lot of direct integrations with WhatsApp service providers.

One of them is Kaleyra Cloud, which is the one we are going to use in this tutorial, and one of those providing WhatsApp Business API integration.

So, if you don’t have an account on Kaleya Cloud yet, I recommend you to create one. 

First, we need to go to our chatbot in Xenioo and click on Publish:

Here, you can choose for WhatsApp (Click on Settings under WhatsApp):

Then, we can select Kaleyra Cloud as our provider:

As you can see, we need some information of Kaleyra to get this to work, so let’s get it.

If you log in at Kaleyra, go to Developers and click on Create API Key:

Then, a pop-up opens and you can give it a title and click on Save:

If everything went good, you must get the following confirmation:

NOTE: I recommend to click on “Download” to make sure that you have stored your API key securely (we need this in the next steps).

Next, we need to go to Channels → WhatsApp → Click on Manage:

Then, choose for Configuration and click on Add number:

Here, you need to specify the number you are going to use on WhatsApp and the incoming URL (which is the “Hook URL” inside Xenioo):

Then, if we go back to Xenioo, we need to fill in our:

• Sender phone number
• API Key
• Account SID 

If you have done this, you can click on Save:

And that’s it! Now, your chatbot is working on WhatsApp!

How it looks on WhatsApp

And here is how it looks on WhatsApp:

That’s it

That’s it! That’s how you create a basic WhatsApp chatbot with Xenioo.

Next article we’ll continue with that basic chatbot enhancing it with new options and make it ready for production.

Have any comments or would like to discuss Xenioo and chatbots?

Join the Facebook group and let us know!

The post How to create a basic WhatsApp Chatbot appeared first on Xenioo.

Still, sometimes, a user likes to talk to a human being, or your NLP does not yet manage a specific problem.

In a situation where a human being is required to engage the user directly, most tools fall short. You usually end up with something that can create an exceptional chatbot or something that can manage a user-to-operator conversation.

With Xenioo, you get the best of both worlds, and in this post, we’re going to see how.

Creating our Customer Service Chatbot Team. Enters the Ninja

To get the most out of Xenioo conversation and operator management, we are going to activate the “Pro Team” package.

This additional package will not only upgrade some actions but also enable team fine-tuning and operators inside conversations.

This package is also available for free if you activate a Professional or Custom plan.

Adding our “Pro Team” package, we can immediately head to our chatbot settings and see how it has dramatically changed.

In our example, we’re going to add three new team members to our support bot. These users will have different jobs and different purposes so that we can explore how Xenioo lets you fine-tune permissions and conversations.

The first one that we’re calling Andrew is part of the Technical Support group. He will only work on customer support on the conversations section.

We will add him to our customer service chatbot team enabling only access to the conversation section. Specifically only to conversations that are associated with his group.

He will not be able to check and engage in any conversation unless specifically assigned to him or his group.

Anna, our second team member, is part of the marketing group and will be engaged by marketing questions.

She also needs to check reports now and then so we’re going to give her access to the bot dashboard and bot statistical data.

Mary, our last member, is a Supervisor and she will see all conversations and statistics. Also, she hasn’t developed the chatbot, but by overseeing conversation, she can choose to better train the AI.

For this reason, we’re also giving her access to a read-only view of the chatbot and full editing capabilities of the NLP section.

You can see, Pro Team opens up all of the permissions that allow you to choose what team members can do. It gives them access to your customer service chatbot being sure that they are not going to access information that is not meant for them.

Once we’re done creating our full team and permissions, let’s continue by adding operator management to our chatbot.

Do you want to talk to a human?

Once your chatbot has done all it could as 1st level support automation, it may be time to move the conversation to a real human.

With Xenioo, you can manage this situation with a very specific action called Set Chat Operator State.

With the Pro Team package, this action becomes even more powerful.

As you can see in the flow above, we manage some chatbot struggles by giving the option to talk to an operator. If the user chooses to do so, we redirect to an explanation message and activate takeover.

This action will:

• stop the chatbot from replying. Whatever the user writes will not activate any chatbot action until the conversation is handed back over
  
• assign the conversation to the Technical Support group (the Andrew group we’ve just created!)
  
• automatically pick a member of the above group based on three possible rules:
  • Random: a random member of the group is assigned to the conversation
  • Less Busy: Xenioo will automatically pick the team member currently engaged in fewer conversations
  • Specific: you can choose a specific member of the above group

So in a single action, we’ve just managed to create a rule we can use anywhere in our chatbot. Using these options, you can easily create multiple paths that allow your users to talk to Andrew or Anne depending on the flow.

For example, your chatbot could be in the marketing inquiries section and redirect the conversation to the marketing team.

Anne, in our example the marketing operator, will see the conversation popup in her Xenioo dashboard and can then engage in a one-to-one conversation with the user on any chatbot channel.

Simultaneously, Andrew could be handling multiple conversations with users having tech problems. Once he solves all of the issues, he can then give back control to the chatbot and even redirect the conversation to a specific Behavior or Interaction.

The possibilities are actually endless.

Conversation with users

All of your team is going to access different views of the same chatbot.

Andrew, in our example, is going to see something like in the picture below.

As you see, only the conversation section of your support bot is available and a user, requesting help, is waiting for a takeover.

Andrew sees this conversation because we just configured our chatbot flow to redirect the to the technical group the help request. Andrew can take over the conversation and chat with our users in real-time.

The channel is not important: Xenioo will handle all of the multi-protocol communication intricacies for you, and everything will just work.

Once the issue has been solved, Andrew can give back control to Xenioo and, if required, redirect the user to a specific Behavior or Interaction.

For example, he could redirect the user to a section where the chatbot collects some feedback on how the support session went.

Changing Conversation Data

Andrew, as an operator also has a very interesting capability: he can alter the customer service chatbot data by updating run-time variables and tags.

By just clicking on the right panel where all the current conversation data is displayed he can pick specific values and change them as well as add new tags.

This is very useful whenever helping the user requires not just guiding but also updating previous data that the chatbot may have collected.

Did the user specify the wrong product? Or a wrong id number? Our operator can intervene and change it in real time. Every change made is immediately reflected by our chatbot live.

Having a Support Supervisor

Sometimes, larger customer care teams may have a supervisor taking care of actually assigning customers requesting help to specific operators.

Much like you would do with real help-desk tickets. With Xenioo, you can also support this kind of scenario.

Mary, our supervisor example, can access any conversation of our chatbot and has a specific power. She can assign and transfer a conversation to any operator of any group.

By supervising conversations and managing alerts, she can decide to redirect a troubled user to any other operator.

In our example below, she’s redirecting the conversation to Andrew again.

Takeover can happen anytime in the chatbot flow and operators can step in at any point. This is useful when a user is stuck on a question and you want to help.

As the user may not be expecting the switch to human interaction, Mary also can display a specific alert message.

A complete Customer Service chatbot for problem-solving

The Pro Team package is a life changer for everybody working with the Customer Service chatbot.

Real-time multi-channel conversation means that you can be always available on Facebook, Web, WhatsApp, Telegram, and Slack and have one single entry point for managing everything.

What do you think? Do you have any specific requirements for your team? Let us know by joining our Facebook group or by contacting us directly: we’re looking forward to your feedback!

The post Managing a Customer Service Chatbot Team to support customers appeared first on Xenioo.

Too awesome? Keep reading.

The natural evolution of pure conversational chatbots is probably being transformed into fully functional voice assistants.

Alexa, Google Assistant, Duplex and other emerging technologies will seamlessly integrate into our devices and offer conversational interactions that (hopefully) mimic real human interactions.

Many have been asking us to make available this kind of integration inside Xenioo. So our team have been working around the clock to make it happen.

Now you can design and build an Alexa skill by building a Xenioo chatbot like you do with other channels.

Creating Your Alexa Developer Account

Alexa skills need to be created inside an Alexa Development Account.

Creating a development account is free and (if you want) it can be the same account you already use for buying on Amazon.

Any Skill that is under testing status is automatically distributed on all the devices associated with your development account. So it may be a good idea to use the account you’re using on all devices.

Please note that if you want to update the development state of your account on any device, you will need to unregister it and register it again like detailed in this procedure.

After you’ve set up your development account you are ready to create your first Skill. There are multiple wizards and templates that let you create multiple Skills with little to no effort but with also little to no real control.

What we are going to choose for our example is a fully custom skill.

After that, we will be telling the Alexa developer console that we are going to “Start from scratch” and proceed to the Builder section.

Since we will be doing an Audio Streaming Skill, we can click on the “Interfaces” button and enable the Audio Player interface.

We don’t need anything more from the console right now: we can move on to the streaming setup.

Streaming Your Own Music

In our example, we don’t want to use generic and global streaming services. We want, instead, to stream our own music collection we’ve been curating and building through the years and that sits in one or more disks of our home network.

To accomplish that, we’re going to integrate Xenioo with another terrific service: Epicentric.

Epicentric is an incredible service that lets you upload your own music and stream it wherever you are. and allows the creation of multiple, complex playlists that are automatically managed by an AI that will make sure you are always listening to something fresh.

Just like we did with Alexa account, go ahead and signup to Epicentric. It is free and after the quick and useful walk-through, you are ready to continue with our streaming skill creation.

Conversation With Alexa

Alexa is built around the free conversational interaction concept. The conversation between the user and Alexa (and so to your chatbot) cannot have real predefined flows.

A user can say just about anything in any order and with different levels of information.

Let’s consider the exchange below:

While being extremely simple, the above example embodies the perfect exchange: the user expresses exactly what is required for the Skill to continue.

But you cannot expect everyone to interact with you like that. Mostly, in fact, are more likely to interact like this:

As you can see the user is not giving away all the info at once and our Skill is trying to fill all the information by repeating or rephrasing the question.

Your skill required information are called slots. They are pretty much like Xenioo NLP Entities and Xenioo Variables in a standard conversational chatbot.

Creating Our Voice Chatbot

With the above information in mind, we can now move on to create our Xenioo chatbot for Alexa.

It won’t be much different from creating a standard conversational chatbot. As we said, there won’t be a real step-by-step flow. We can head directly to the AI section and set up the NLP that will drive our Skill.

Inside the Xenioo AI section, we will define all the intents and expressions that will be used to create our Skill model and to define how the flow of the conversation will execute.

Depending on the type of Skill you are creating, you may be required to implement specific intents in your chatbot.

A streaming Skill like the one we’re building now is required to implement at least a Pause and Resume intent. Outside of these two, you can go on and create as many intents as you like.

Everything that is said to Alexa will be processed by the defined intents and passed to Xenioo. It will dispatch the intent redirection to your chatbot interactions. The schema below illustrates what happens behind the curtains:

Xenioo does full use of Alexa SSML in every text that is sent back to the user. Feel free to use any lang, break, whisper or other supported tags to obtain specific voice effects.

Once you’ve created your intents, remember to connect your Activation to your chatbot interactions.

Since everything the user says will trigger an existing intent, only intents that are connected to an existing chatbot interaction will be deployed to your skill model. Also, if you want to implement or override existing Alexa skills (like the resume or pause we are required to do) remember to specify the Alexa skill key in your intent skill key.

First Deploy Of Alexa Chatbot

We’re looking forward to deploying our very first Skill for a test drive. Before fully completing our chatbot we want to push our first work!

We can do it anytime by just clicking on the Publish button inside our Build section. In the channel selection screen let’s select Alexa and a default Amazon login window will open. This window will connect your Amazon profile and allow Xenioo to push Alexa skills for you.

After confirming your account and confirming all required permissions, we can continue configuring our skill deployment.

In the above window, you are required to select the skill you’re publishing, the locale and the activation text you like.

The activation text will be used by Alexa to activate your skill so be sure to pick something which is both easy to say and not clashing with default Alexa phrases.

The most important setting here is the Hook Url. Be sure to copy this URL and paste it to your Alexa skill configured endpoints.

Note that Xenioo will only update and build your Invocation Model as that is the key to Alexa Skill inner workings.

Xenioo does not wish to overwrite any setting you have specified in your Alexa skill and that you may change later. So let you decide whatever is your desired configuration from the native Alexa console.

Xenioo is the perfect backend middleware and AI-driven flow platform for your Skill and it does exactly that awesomely.

Once you’re done configuring everything press publish and your Skill is ready to be tested live!

In our next post, we will see how Streaming, Account Linking, and Device State management works!

The post How to Build an Alexa Skill With Xenioo (Part 1) appeared first on Xenioo.

Amazon Alexa, Google Assistant, Duplex ed altre tecnologie emergenti, si integreranno perfettamente all’interno dei nostri dispositivi, consentendo esperienze di conversazione sempre più simili a quelle di solito affrontate con persone reali.

Il “business della voce” è un trend in crescita e molte grosse realtà, a partire da Bezos di Amazon, stanno investendo molto nel mercato degli applicativi vocali.

Inoltre molti nostri clienti ci hanno chiesto di rendere disponibile questo tipo di integrazione all’interno di Xenioo.

Non potevamo non accettare questa sfida.

Con Xenioo è ora possibile progettare, costruire e pubblicare una Skill Alexa semplicemente creando un chatbot Xenioo come faresti per qualsiasi dei canali già disponibili.

In questo articolo andremo a vedere nel dettaglio esattamente come costruire una Skill Alexa capace di riprodurre la tua musica personale indipendentemente da dove si trova il tuo dispositivo.

Fantastico, vero? Continua a leggere.

Se ti interessa scoprire come costruire una Action Google Assistant, Xenioo supporta anche questo. Dettagli nel nostro articolo dedicato.

Creaiamo il Developer Account di Alexa

Ogni Skill di Alexa deve essere creata all’interno di un account di sviluppatore Alexa (Alexa Development Account).

La creazione di questo account è gratuita e puoi utilizzare, se preferisci, lo stesso account che già utilizzi per gli acquisti su Amazon.

Ogni Skill in stato di test viene automaticamente distribuita su tutti i dispositivi associati al tuo account di sviluppo. E’ quindi una buona idea utilizzare lo stesso account che stai utilizzando sui tuoi dispositivi.

Tieni presente che se desideri aggiornare lo stato del tuo account su ogni dispositivo, sarà necessario procedere prima alla de-registrazione e successivamente alla registrazione, come spiegato nella documentazione tecnica di Alexa.

Dopo che l’account di sviluppo è stato impostato, sei pronto per creare la tua prima Skill Alexa.

Esistono una moltitudine di templates che ti permettono di creare Skills con uno sforzo minimo e molto rapidamente, ma con il limite di lasciare poco controllo su quelle che la Skill potrà fare.

Per il nostro esempio andremo a selezionare una Skill completamente personalizzata (“Custom”).

Successivamente, andremo a selezionare che intendiamo cominciare da zero (“Start from scratch”), proseguendo nella sezione Builder.

Dal momento che vogliamo costruire una Skill che faccia streaming audio, andremo a cliccare sul bottone “Interfaces” per poi abilitare l’interfaccia “Audio Player”.

Perfetto! Per ora non ci serve più niente dal pannello sviluppatore di Alexa. Possiamo quindi procedere nella configurazione del servizio di streaming.

Configuriamo il servizio di Streaming

Nel nostro esempio non vogliamo usare un servizio di streaming generico, come tanti disponibili online.

Ci interessa invece ascoltare musica dalla nostra collezione musicale che abbiamo pazientemente creato e curato nel corso degli anni e che si trova depositata su uno o più dischi del nosto network casalingo.

Per fare tutto questo, dobbiamo integrare Xenioo con un altro eccezionale servizio: Epicentric.

Epicentric è un servizio online che ti consente di caricare la tua musica personale per ascoltarla ovunque ti trovi.

Inoltre ti permette di creare playlist multiple ed avanzata, basate su mappe mentali e che verranno automaticamente gestite dalla intelligenza artificiale della piattaforma.

Esattamente come abbiamo fatto per l’account Alexa, dovremo procedere a creare un account anche su Epicentric. E’ gratuito e dopo una veloce introduzione per capire come utilizzarlo, saremo pronti per procedere con la creazione della nostra Skill.

La conversazione con Alexa

Alexa è costruito attorno al concetto di interazione conversazionale libera: la conversazione tra l’utente ed Alexa (e quindi il tuo chatbot) non può avere veri flussi predefiniti.

Infatti un utente potrebbe dire qualsiasi cosa, in qualsiasi momento e con diversi livelli di informazioni presenti.

Vediamo un esempio qui sotto:

Seppure estramente semplice, l’esempio di conversazione sopra riportato rappresenta il perfetto scambio utente – Alexa: l’utente esprime esattamente la sua richiesta e la Skill risponde di conseguenza.

Purtroppo non possiamo aspettarci che tutte le conversazioni siano cosi lineari e precise. Anzi, molto spesso il tipo di conversazione che avvine è più simile all’esempio sottostante:

Come puoi vedere, l’utente non stà dando tutte le informazioni alla prima interazione, e questo porta la Skill a dover riformulare alcune domande per poter arrivare ad ottenere tutte le informazioni necessarie a completare la richiesta.

Le informazioni richieste dalla Skill sono chiamate Slots e possono essere paragonate a quelle che in Xenioo sono le Entità NLP e Variabili durante un conversazione standard con un chatbot.

Creiamo il chatbot vocale

Con le informazioni che abbiamo appreso nel paragrafo precedente, possiamo ora proseguire finalmente nella creazione del nostro chatbot per Alexa.

Non sarà molto diverso dalla creazione di un chatbot conversazionale standard: dal momento che, come abbiamo detto precedentemente, non ci sarà un vero e proprio flusso guidato, possiamo andare direttamente nella sezione AI per configurare tutta la NLP necessario per guidare la nostra Skill.

All’interno della sezione AI di Xenioo, andremo a definire tutti gli intenti e le espressioni che saranno usate per creare il modello conversazionale della Skill, definendo allo stesso tempo come verrà eseguito il flusso.

In base al tipo di Skill che si sta creando, potrebbe essere necessario implementare degli specifici intenti.

Per la nostra Skill di streaming, per esempio, è necessario implementare almeno due tipi di intenti specifici: Pause e Resume.

Oltre questi due, obbligatori, potrai poi creare quanti più intenti ti servono per far gestire la conversazione ad Alexa nel modo desiderato.

Qualsiasi cosa venga detto ad Alexa verrà processato dagli intenti definiti e passato a Xenioo, che a sua volta redirigerà il flusso alle interazioni configurate.

Lo schema sottostante illustra cosa effettivamente accade dietro le quinte:

Xenioo utilizza l’ Alexa SSML in ogni testo che è spedito indietro all’utente. Sei quindi libero di poter usare ogni interruzione, sospiro o qualsiasi altro tag supportato per ottenere specifici effetti vocali.

Una volta che hai creato i tuoi intenti, ricordati di collegarli alle interazioni del chatbot. Infatti qualsiasi cosa detta dall’utente attiverà uno degli intenti esistenti e solo gli intenti che sono collegati ad una interazione esistente saranno pubblicati nella Skill.

Inoltre, se vuoi implementare o sovrascrivere uno degli intenti predefiniti in Alexa (come per esempio il Resume oppure Pause), ricorda di specificare la chiave Alexa nel campo Key dell’intento, come mostrato nell’immagine sottostante.

Pubblichiamo il chabot in una Skill Alexa

Siamo arrivati al punto in cui finalmente possiamo pubblicare il nostro chatbot nella nostra prima Skill Alexa e cominciare a fare dei veri test sui dispositivi.

Possiamo farlo come sempre cliccando sul bottone Publish dalla sezione Build del chatbot.

Nella sezione dei canali, selezioniamo Alexa e clicchiamo sul bottone di login con logo Amazon. Seguite la procedura che vi porterà a connettere il vostro profilo Amazon con Xenioo: è in questo modo che Xenioo potrà pubblicare la vostra Skill per voi.

Dopo aver confermato il collegamento tra gli account ed i permessi richiesti, possiamo continuare con la configurazione della pubblicazione della Skill.

Nella sezione Deploy dovrete selezionare la Skill da pubblicare, la localizzazione ed il testo per l’attivazione.

Il testo di attivazione sarà usato da Alexa per attivare la tua skill, quindi cerca di utilizzare una parola o una frase che isa semplice da ripetere e che non possa andare in conflitto che le frasi di default di Alexa.

La configurazione più importante in questa sezione è l’ Hook Url: copiate questo URL ed incollatelo nella configurazione dell’endpoint nel pannello di Alexa per skill che si sta configurando.

Tieni presente che Xenioo aggiornerà unicamente l’ Invocation Model della Skill, dal momenteo che è ciò che permette la gestione della conversazione. Xenioo non andrà mai a sovrascrivere nessun settaggio configurato dal pannello nativo di configurazione della Skill Alexa.

Xenioo è il “middleware back-end” perfetto e la piattaforma basata su intelligenza artificiale ideale per progettare anche le skill più compesse. E lo fa decisamente bene.

Una volta che tutto è stato configurato correttamente, cliccate Publish e la Skill sarà pronta per essere testata dal dispositivo.

Nel nostro prossimo articolo, vedremo come funzionano nel dettaglio lo Streaming, Account Linking (collegamento degli account) e Device State Management (gestione dello stato del dispositivo).

The post Come creare una Skill Alexa con Xenioo (Parte 1) appeared first on Xenioo.

Today we’re going to see how we can quickly and seamlessly integrate it using specifically designed Xenioo Actions.

Choosing Xenioo Actions

The first thing to do when processing an input is choosing the right action. Xenioo input actions are split into two categories: Inputs and Detections.

Inputs are actions that will hold the conversation until the user correctly answers while Detections are general triggers that may be fired anywhere during an interaction, a behavior or globally, at bot level.

It is a fundamental differentiation as it changes dramatically the way your bot behaves. Inputs are usually made with direct questions that need to be answered to proceed.

Something like “What is your phone number?” or “How many people will be attending?” and so on. Those are fundamental variables of your process.

On the other hand, Detections may be triggered by general questions that can be answered anywhere in your chatbot. They may include general chit-chat questions (what is your name? are you a chatbot?) as well as user details information that needs to be available anywhere like “I need to change my reservation”.

In our example, we’re picking an IBM Watson Assistant Input Action.

Integration Parameters

To link our Xenioo chatbot to IBM Watson Assistant we need a couple of parameters that can be quickly copied and pasted. The first two are username and password: you can find both in the Assistant section of your Watson Services page.

Just click on the Show Credentials button and copy/paste them to Xenioo integration interface.

After this, we only need the Workspace Id. Inside your IBM Watson Assistant interface, you have one or more Workspaces: intents detection is based on a specific Workspace with a specific Unique ID.

To get your Workspace Unique ID, click on the three small dots that you see near your Workspace name and then click on “View Details”. The Workspace card should flip and display, among other things, the ID you need. A handy icon can be used to quickly copy the value: once done, paste it to the corresponding field in Xenioo.

Using Intents

With our integration parameters saved, we are now ready to proceed: all there is to do is specify the intent we want to catch.

You can either type it directly or copy/paste it from IBM Watson Assistant Interface. Do not worry about upper and lower case or about the # symbol used by Watson: Xenioo will just compare everything correctly.

After doing this last step, we’re ready to test our chatbot: let’s click on the preview button to run it and see how our integration works out.

The answer you get immediately after intent detection is retrieved from IBM Watson. This is optional and completely up to you: you can either define replies in Xenioo interactions or have Xenioo retrieve them from IBM Watson for you.

Each Entity you define inside IBM Watson Workspace which is detected in an intent is automatically transformed into a Xenioo variable that you can now use in hundreds of different ways.
This is a huge feature as it allows you to further manage your chatbot flow based on the user detected information.

As with all Xenioo Actions, everything can be checked and debugged through our Execution Diagram Interface. From there you’ll see how intents are detected and with what score and see how different variables are affecting your flow.

“The Right Tool For The Job”

I originally intended this post to end with something like use Xenioo when this, or  DialogFlow when that or IBM Watson with it.

The truth is that, with Xenioo, you don’t really have to pick a side: the smooth integration that is smartly managed by the Xenioo engine make it so that you can create intents with each platform and pick whats the best tool depending on the job.

I wouldn’t be surprised to see chatbots using all of the engines simultaneously depending on the language or the question!

The post Integrating IBM Watson Assistant NLP with Xenioo appeared first on Xenioo.

Chatbots are quickly evolving and moving from the typical buttons-clicking experiences to a real, meaningful conversational experience.

Multiple real world applications demand a deeper user relation and a better general usage experience. While probably not yet passing a touring test, your chatbot can already be of great help in multiple applications like first level support, customer inquiry and first contact management (to name a few).

In this post we are going to explore these new features and see real life examples and applications.

Conversation Control by ChatBot AI and NLP

Before this, you could control how your chatbot reacted to triggered NLP intents by adding NLP detection actions global level. This approach is great and gives you an incredible level of flexibility directly at design time.

The only issue of this approach is that it works wonders until you want to switch to a chatbot that heavily relies (or is completely based) on NLP.

What if your chatbot contains not just 4 or 5, but 40 or 50 intents?

When approaching this kind of chatbots, doing everything inside the designer may prove too complicated and difficult to maintain in the long run.

With the idea of giving you the maximum control over your chat flow when using Natural Language Processing intensively, we’ve created the conversation control ability directly inside each and every Intent.

Let’s see how it works.

Automatic Redirection

With automatic redirection, you can instruct Xenioo to activate a specific Behavior/Interaction whenever a given intent is detected.

There’s no designer action involved as everything is controlled by the Chatbot AI engine and it works automatically for you.

Whenever the user says something in the chat, the Xenioo NLP engine will parse the active intents and automatically redirect the conversation to the given Interaction when it crosses the Confidence threshold

Chatbot AI Immediate Reply

What if you do not really need an interaction and the answer may very well be a simple text?

The Immediate Reply conversation control let you do just that: you type any text you wish and, if the intent is triggered during the conversation, Xenioo will reply with it.

Note that you current flow position will always stay the same: after the text is sent to your user, the conversation will be exactly where it was before.

Test Run

Ready to give these two new features a try?

We’ve built a showcase page with a full support bot that will try to help you configure your printer.

The very same chatbot is also available as as template in your account templates section! Remember: templates do not count toward your account resources until you decide to publish them on one of the available channels. So feel free to try and test everything!

No limits, as always

If you’ve been using Xenioo for a while, you should know by now that we hate putting limits to how you should approach your chatbot creation.

Everything around Xenioo is built to let you build things the way you prefer and these features are not different. You are free to use them together with the global bot actions. You can also mix and match results for an even greater control.

We can’t wait to see what you will be able to create!

Do you have a great chatbot experience created with Xenioo and want to share it with all of us? Do you want to keep track of all the wonderful features of Xenioo?

Our Xenioo Chatbot Community is the right place to go! Join the discussion!

The post Using NLP to Fuel Your Chatbot AI and boost conversational experience appeared first on Xenioo.