Category: Chat

We define the concept of chatbot orchestration platform and why this is a revolution in the way that chatbots are built

Orchestration is the automated configuration, coordination, and management of computer systems and software [1]. The term became popular with the mainstream adoption of web services and service oriented architectures (SOA) as there was the need to align and complement several independent services to respond to the business needs while satisfying its constraints (in terms of quality, reliability, price,…). Xatkit brings this concept to the chatbot world.

Xatkit is the first full-fledged chatbot orchestration platform.

You can use Xatkit as a tool to simplify your chatbot development projects thanks to our chatbot DSL and state machine semantics that provide good abstractions to facilitate the creation of complex bots. Kind of a low-code approach for chatbots. But Xatkit is much more than this. Xatkit is a framework that enables you to freely combine the best NLP technologies for your needs. Do you need a neural network to understand the user intentions? A sentiment analysis feature? A language model for translations?. Take whatever you need, from the best providers and combine them in a single chatbot. Let’s see how we achieve this.

First step: Avoiding the Not Invented Here syndrome in the chatbot world

Xatkit does not pretend to reinvent the wheel. We don’t suffer from the Not Invented Here syndrome. We develop our own solutions when we see a gap in the market but aim to reuse and enable as much as possible all the great NLP/NLU technologies that already exist out there. With so much research and development around natural language technologies, we believe the best way to serve our clients is to give them access to all the cutting-edge developments instead of getting them stuck and locked-in in ours.

You can use Xatkit as a tool to simplify your chatbot development projects thanks to our chatbot DSL and state machine semantics that provide good abstractions to facilitate the creation of complex bots. Kind of a low-code approach for chatbots. But Xatkit is much more than this. Xatkit is a framework that enables you to freely combine the best NLP technologies for your needs. Do you need a neural network to understand the user intentions? A sentiment analysis feature? A language model for translations?. Take whatever you need, from the best providers and combine them in a single chatbot. Let’s see how we achieve this.

Trending Bot Articles:

1.Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3. Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

Instead, many other companies follow the opposite route and try to compete against each other by duplicating the same features again and again. How many intent recognition providers the world really needs?. According to many chatbot vendors, several dozens!, many of them using a very similar approach. Xatkit is designed to use the one you like the most. This way you can use your favorite provider while we focus our efforts on making your life easier. For instance, by using Xatkit as platform, you can in the future switch providers (e.g. going from DialogFlow to IBM Watson) without changing a single line of your chatbot definition. Xatkit comes with a few predefined connectors (regular expressions, DialogFlow or nlp.js) but thanks to our architectural design and extension capabilities we can easily add new ones.

Second step: Good chatbots need much more than just intent matching

The key NLP component of every chatbot is the intent recognition component that takes as input the user utterance and identifies the intent behind it (plus recognizing the possible parameters in it). But a quality chatbot needs more than this. You may want to have a troll detector to block those comments right away without having to fully process them. Or a sentiment analysis component that then you can use to decide how to better keep visitors engaged depending on their mood. Or maybe apply an internal translation to support a visitor that may be addressing the chatbot in an unexpected language. Some of these actions should take place before the intent recognition phase. Others after. Others before AND after.

Xatkit includes the concept of Language Processors to support these requirements. Processors can be preprocessors, postprocessors or both. And following the philosophy explained above, we either create our own processor (e.g. to remove stop words from any entities) or adopt any library/language model/… solution out there that we believe can do a better job. For instance, Xatkit integrates a processor based on Stanford CoreNLP for the sentiment analysis.

At Xatkit, we believe building a chatbot should be as fun (and as easy) as playing with Lego. You take the pieces you need and assemble them together to create your ideal NLP pipeline. It doesn’t matter where you bought that piece. And you can always buy a new extension to enhance your bot functionality.

Of course, we are working hard to provide the right abstractions to “defeat” the well-known saying that states that “Software reuse is more like an organ transplant than snapping together Lego blocks” (John D. Cook)

Final step: Xatkit puts it all together

All this sounds nice, but you may be thinking, who is gluing all these components together? Who makes sure they all execute in sync and share the data they need at the right time?. This is where our chatbot runtime engine comes into play. Xatkit is, at the same time, a chatbot definition and chatbot orchestration platform, giving you support for the full life-cycle of you chatbot. Via a simple configuration mechanism you can indicate how to deploy your bot (what components to use, what concrete providers / services connect to, etc) and Xatkit will be the genius conductor that will orchestrate the chatbot for you making sure each component follows its role in the chatbot choreography. Ready to give it a try?

The conductor in the featured image is Mirga Gražinytė-Tyla. Lego photo by Kelly Sikkema on Unsplash

Don’t forget to give us your 👏 !

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An open source chatbot orchestration platform was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Around 50% of all searches on the web are voice searches.

In 2020, Campaign.com shared this staggering stat, based on Comscore research. In 2019, Statista.com backed this data up by saying that around 42% of the worldwide population used voice search within the past month. Adobe talked about 48% of consumers using voice for “general web searches.”

However, due to the way the Internet is constructed and my curiosity, the 20th or 30th article about voice search I’ve read is debunking Comscore’s 50% prediction. According to it, Comscore projects “at least 50% of all searches are going to be either through images or speech in five years.”

Whatever the exact number is, the buzz on voice search was in the air in 2020 for a reason, so now is a great time to learn more about this trend to be prepared for the future.

Smart speaker market

Smart speakers with voice assistants, which are responsible for 39% of all voice searches, are rapidly expanding in North America.

Around 26% of Canadians and 24% of Americans owned smart speakers with personal assistants as of early 2020. These numbers are even higher for households.

Alexa is the leading brand in the US smart speaker market, owned by 74% of Americans. Сanadians are not so apparent in their choice — 53% of Canadians prefer Google Home while 48% of Canadians own Alexa.

Forecasts are even more promising. The worldwide smart speaker market revenue is going to increase by 44% in the next five years, reaching $35.5 billion in 2025.

Trending Bot Articles:

1.Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3. Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

What should marketers do?

Quick answer — optimize their websites for voice search.

The problem is that voice search and regular search are different.

With voice search, you’re playing an all or nothing game — you have one chance to be picked up by a voice assistant.

However, it is most likely to happen if you’re first in search results, particularly in SERP features.

You can’t measure ROI from voice search optimization. For now, Bing or Google web analytics don’t provide tools to identify how much traffic comes in via voice search.

But what you can do is tailor your SEO strategies based on the knowledge of smart speakers’ behaviour.

Here are some key takeaways from the SEMrush Voice Search 2020 study that might help you understand voice searches’ main characteristics and how they differ across the most popular smart speakers.

Recommendations for voice search optimization

1. Put specific questions in your titles or subtitles, starting from the 5 Ws: “Who,” “What,” “When,” “Where,” “Why” + “How.” Consider adding the FAQ section to your website.
2. Use long-tail keywords and more conversational keywords. Use AnswerThePublic to discover how exactly people are searching for stuff.
3. Give short and sweet answers to particular questions within your content.
4. Optimize your website for local voice search as 22% of voice queries are for local-based content.
5. Make your website load fast on all devices.
6. Focus on ranking in featured snippets. Most importantly, ranking in featured snippets is our primary measurement to identify how well a website is optimized for voice search.

Lionbridge case study

Some companies have already realized the importance of the voice search trend and optimized their websites for voice search queries.

Lionbridge, an American company specializing in translation and game localization, attributes a 127% growth in page views to their voice search optimization strategy. In just a year, they improved their ranking in featured snippets from 0.7% to 27% and increased their SERP positions by 22.5%. Here is a detailed case study explaining how exactly they’ve achieved such results.

Don’t forget to give us your 👏 !

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Why Should All Marketers Learn More About Voice Search in 2021? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Do you know you can increase your marketing results by integrating chatbots and email marketing? In this guide, we have discussed 5 strategies for the same.

Email marketing and chatbots have their own advantages and can help you achieve different marketing and business goals.

But, what if you integrate these to get better results from both?

Well, email marketing and chatbots integration is not just possible but is a strategy that many smart marketers use.

These two complement each other perfectly and either can be used to boost the other’s performance.

Email marketing is one of the key website traffic sources and once the users reach your website, chatbots can help them with whatever they’re looking for.

Alternatively, chatbots can collect relevant user information to fuel your email marketing campaigns.

This is a well-balanced loop where both can help to improve the other and work hand-in-hand to deliver better user experiences.

Want to learn how to use email marketing and chatbots integration to your advantage?

Here are some of the ways in which email marketing and chatbots integration can be done to get you better results from both.

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3. Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

Use Chatbots to Generate Leads for Email Marketing

The simplest way to integrate chatbots and email marketing is to use chatbots to generate leads by collecting email addresses for email marketing.

Chatbots can ask your website visitors to provide their email addresses by offering some incentives.

For example, you can train your chatbot to ask for a user’s email address at the end of a conversation. The incentive can be that they will get personalized offers and discounts, stay updated on the latest content on your website, etc.

Chatbots can also ask for your email address to get you in touch with the company’s team or to provide better assistance.

Here’s an example of a simple chatbot conversation where the chatbot collects email addresses in the natural flow of the conversation.

This is a simple, yet effective way to generate leads for email marketing while still providing good user experience.

This kind of email marketing and chatbot integration can add a new source of lead generation for your business.

Moreover, leads collected this way are already qualified because these are your website visitors who engaged with the chatbot.

You can even create customer satisfaction surveys using the chatbot and receive the response on email.

So, they have already shown some interest in your business and are ready to be nurtured further.

Create Segmented Email Lists Using Chatbots

Chatbots collect useful user data and insights that can help you segment your email lists according to different parameters. You could, for instance, use demographics like age, gender, etc. to segment your email lists. You can also take this a notch higher and create segmented email lists based on past purchase history and user preferences.

With chatbots, this can happen in real-time. Chatbots can collect this information and segment each use to the most relevant email lists. They can send this user data to your email marketing platform and also to specific mailing lists.

This increases the effectiveness of your email campaigns multifold as you can then create personalized emails for people in different lists.

Leverage Insights From Chatbot Conversations to Personalize Your Emails

Adding to the previous point, insights gained about a user from chatbots are helpful in personalizing your emails. It is not just limited to segmented email lists but can be extended further by personalizing emails on an individual level.

Chatbots are trained to ask specific questions to users, to provide them with personalized product recommendations and tips. This valuable user data is stored in chatbots’ past conversations logs and can be put to good use by using it for sending personalized emails.

Imagine, a user buys a product based on a chatbot’s recommendation and you send an email with similar product recommendations that the user might be interested in.

Given that they have already made a purchase, they would be more likely to purchase again if you show them things that they might like.

Sending personalized emails like these can help you engage your audience and encourage repeat purchases. This means that email marketing and chatbots integration should also be one of your ecommerce sales strategies for 2020.

Chatbots have a treasure trove of data waiting to be utilized, you simply need email marketing and chatbots integration to unlock its true potential.

Send Reminders Using Chatbots to Prompt People to Read Your Emails

This is a fairly unique and lesser-known way in which email marketing and chatbot integration can help your business. You can use chatbots to send reminders to people who have not yet opened your emails.

Most advanced chatbots have the option to send push notifications to users. This feature can be leveraged to send a reminder notification for unopened emails, thus improving your email open rates.

Automate Your Emails by Integrating Chatbots with Email Marketing

Many advanced AI chatbots have the option to send automated email notifications as a response to certain triggers. You can decide what these triggers are and what message needs to be sent when the chatbot is triggered.

For example, let’s say that a new user has signed up for your emails and provided their contact details to the chatbot. The chatbot can use this as a trigger and send an automated welcome or thanks for subscribing email notification to the user. It can also send an automated email to your team informing them that a new user has signed up.

This image from the Landbot chatbot builder clearly shows how this unique email marketing and chatbot integration can be achieved.

Conclusion

By using chatbots in email marketing you can take your marketing performance to a new high. Chatbots perfectly complement email marketing and can help you create better, more targeted, and personalized email campaigns.

You can use email marketing to drive traffic to your website and let chatbots handle the lead nurturing and conversion part. Or, you can collect email addresses using chatbots and then use emails to engage and nurture the leads.

Either way, email marketing and chatbots integration can be beneficial in generating more leads, nurturing them, and driving sales conversions. Use these five tips for seamless email marketing and chatbot integration for your business.

Don’t forget to give us your 👏 !

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How Chatbots and Email Marketing Integration Can Help Your Business was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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In 2020, we saw the increased use of artificial intelligence and chatbots. Chatbots have changed the digital customer experience, made educational bodies and workplaces incorporate adaptive learning in online learning, and convinced businesses and consumers to embrace artificial intelligence and chatbots. If that pandemic has taught us one thing, it’s how crucial digital communication is.

With reports from PSFK and Salesforce reporting that 74% and 69% of users prefer using chatbots for straightforward answers; and Business Insider announcing that 80% of businesses will use chatbots by the end of 2020, the purchase and operation of chatbots in 2021 will rise steadily.

If you too are interested in investing in chatbots, here are some things to consider:

This article is pointing out security threats and attack vectors of typical chatbot architectures — based on OWASP Top 10 and adversarial attacks.

The well-known OWASP Top 10 is a list of top security threats for a web application. Most chatbots out there are available over a public web frontend, and as such all the OWASP security risks apply to those chatbot frontends as well. Out of these risks there are two especially important to defend against, as in contrary to the other risks, those two are nearly always a serious threat when talking about chatbots — XSS and SQL Injection.

Recently another kind of security threat came up, specifically targeting NLP models — so-called “adversarial attacks”.

Cross-Site Scripting – XSS

A typical implementation of a chatbot frontend:

• There is a chat window with an input box
• Everything the user enters in the input box is mirrored in the chat window
• Chatbot response is shown in the chat window

The XSS vulnerability is in the second step — when entering text including malicious Javascript code, the XSS attack is fullfilled with the chatbot frontend running the injected code:

<script>alert(document.cookie)</script>

This vulnerability is easy to defend by validating and sanitizing user input, but even companies like IBM published vulnerable code on Github still available now or only fixed recently.

Possible Chatbot Attack Vector

For exploiting an XSS vulnerability the attacker has to trick the victim to send malicious input text.

1. Attacker tricks the victim to click a hyperlink pointing to the chatbot frontend including some malicious code in the hyperlink
2. The malicious code is injected into the website
3. It reads the victims cookies and sends it to the attacker without the victim even noticing
4. The attacker can use those cookies to get access to the victim’s account on the company website

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3. Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

SQL Injection — SQLI

A typical implementation of a task-oriented chatbot backend:

• User tells the chatbot some information item
• The chatbot backend queries a data source for this information item
• Based on the result a natural language response is generated and presented to the user

With SQL Injection, the attacker may trick the chatbot backend to consider malicious content as part of the information item:

my order number is "1234; DELETE FROM ORDERS"

Developers typically trust their tokenizers and entity extractors to defend against injection attack.

Possible Chatbot Attack Vector

When the attacker has personal access to the chatbot frontend, an SQL injection is exploitable directly by the attacker (see example above), doing all kind of SQL (or no-SQL) queries .

Adversarial Attack

This is a new type of attack specifically targeting at classifiers — the NLP model backing a chatbot is basically a text classifier.

An adversarial attack tries to identify blind spots in the classifier by applying tiny, in worst case invisible changes (noise) to the classifier input data. A famous example is to trick an image classifier to a wrong classification by adding some tiny noise not visible for the human eye.

A more dangerous real-life attack is to trick an autonomous car to ignore a stop sign by adding some stickers to it.

The same concept can by applied to voice apps — some background noise not noticed by human listeners could trigger IoT devices in the same room to unlock the front door or place online shop orders.

When talking about text-based chatbots, the only difference is that it is not possible to totally hide added noise from the human eye, as noise in this case means changing single characters or whole words.

There is an awesome article “What are adversarial examples in NLP?“ from the TextAttack makers available here.

Possible Chatbot Attack Vector

For voice-based chatbots one possible risk is to hand over control to the attacker based on manipulated audio streams, exploiting weaknesses in the speech recognition and classification engine.

• The attacker tricks the victim to play a manipulated audio file from a malicious web site
• In the background, the voice device is activated and commands embedded into the audio file are executed

To be honest, it is hard to imagine a real-life security threat for text-based chatbots.

• User Experience is an important success factor for a chatbot. An NLP model not robust enough to handle typical human typing habits with typographic errors, character swapping, emojis provides a bad user experience, even without any malicious attacker involved.
• It could be possible to trick a banking chatbot to doing transactions with some hidden commands and at the same time deny that the transaction was wanted, based on the chatbot logs … (I know, not that plausible …)

Security and Penetration Testing with Botium Box

Botium Box includes several tools for improving the robustness of your chatbot and your NLP model against the attacks above.

Penetration Testing with OWASP ZAP Zed Attack Proxy

Botium Box provides a unique way of running continuous security tests based on the OWASP ZAP Zed Attack Proxy — read more in the Botium Wiki. It helps to identify security vulnerabilities in the infrastructure, such as SSL issues and outdated 3rd-party-components.

E2E Test Sets for SQL Injection and XSS

Botium Box includes test sets for running End-2-End-Security-Tests on device cloud and browser farms, based on OWASP recommendations:

• Over 70 different XSS scenarios
• More than 10 different SQL Injection scenarious
• Exceptional cases like character encoding, emoji flooding and more

Humanification Testing

The Botium Humanification Layer checks your NLP model for robustness against adversarial attacks and common human typing behaviour:

• simulating typing speed
• common typographic errors based on keyboard layout
• punctuation marks
• and more …

Read more in the Botium Wiki.

Paraphrasing

With the paraphraser it is possible increase test coverage with a single mouse click — read on here:

Tutorial: Using Paraphrasing to Increase Conversational AI Test Coverage

Load Testing

With Botium Box Load Testing and Stress Testing you can simulate user load on your chatbot and see how it behaves under production load — read on in the Botium Wiki.

Don’t forget to give us your 👏 !

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Security Threats and Security Testing for Chatbots was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.