The Chatbot Landscape, 2017 Edition The Chatbot Landscape, 2017 Edition
To help decision makers and users wade around the vast landscape of bots, this landscape gives a high-level overview of providers... The Chatbot Landscape, 2017 Edition

To help decision makers and users wade around the vast landscape of bots, this landscape gives a high-level overview of providers and tools.


Why this landscape, now?

Since we started building bots more than 2 years ago, the landscape has seen massive interest and change. This makes it hard for companies and customers to figure out what’s really happening and what they should do if they really want to build a chatbot for their business.

Through this exercise, we deeply explored various bot platforms, bot use cases, and bot frameworks — and we’ve arrived at some interesting observations and insights that may be useful to you (hopefully ).

Obviously, there’s no way to squeeze everyone into the landscape, hence we selected those which fulfill these objectives:

  1. Give readers an overview of the industry, such as the industry’s structure, notable examples, dominant providers, and tools widely used to develop chatbots.
  2. Help decision makers understand and choose the most appropriate chatbot development strategies by crossing business needs and capabilities in the market.

The basics

The global market for chatbots reached US$88.3 million in 2016. The market will grow to 36% CAGR, to more than $1 billion, through 2023[1]. Other than this growth rate, the current state and structure of the industry is clearly useful to know better — it’s gotten pretty crowded since we started building bots more than 2 years ago!

Ready? Get ready for a long post


We began the mapping process by conducting secondary research, collating data from hundreds of sources, including published studies, companies’ websites, online articles, and personal interviews. In order to give a fair representation of dominant players in the landscape, the study spanned diverse geographical regions and 6 industry verticals, namely:

  1. Commerce
  2. Fashion & Beauty
  3. Travel & Hospitality
  4. Education
  5. News & Entertainment
  6. Finance & Insurance

Additionally, we validated the information presented on the landscape by seeking the opinion of established industry players.


To put everything into a coherent structure, we define the parameters and the terms as such:

Horizontal axis: The “marketing” function refers to a bot’s ability to drive exposure, reach, and interaction with the brand or product for potential and current customers. The “support” function refers to a bot’s ability to assist current customers with problems, and to resolve those problems for them.

Vertical axis: “Managed” refers to companies outsourcing the development of bots to external vendors, whereas “self-serve” refers to them building their bots in-house or with an 0ff-the-shelf tool.

From the inside out, the concentric circles represent:

  • Platforms: The messaging platforms that enable the existence bots through robust send-and-receive APIs, frameworks and ecosystems.
  • Brands: Companies which have launched and experimented with bots, in that particular quadrant (for example, Managed x Support).
  • Providers: Companies who have the capabilities to deliver exceptional work in that quadrant.
  • Tools: The supporting tools used by providers, brands, or developers of bots in delivering bot experiences.

Details & Explanation

Here are some of our observations about each of the concentric circles: platforms, brands, providers and tools.


In this study, text is the main interaction mode we explore (we might explore voice bots in another study).

As Facebook Messenger is one of the leading chat platforms (with over 1 billion daily active users worldwide), with a strong push internally for Messenger bots, it’s understandable that companies and developers alike have been heavily investing in Facebook Messenger bots.

Clearly, everyone is waiting with baited breath for WhatsApp to open up a bot platform, but it’s still conjecture at this stage. SMS remains a baseline option for communication, and companies continue to use it to send automated reminders and information. As more messaging apps gain a foothold among their intended audience, such as Line and Telegram, their bot platforms will become more attractive for companies to invest in.

Another point to note is that platform adoption and use vary greatly among geographic regions, and certain platforms may work better than others. WeChat, for example, has a naturally huge user base, but may or may not work for your intended target audience.

By platforms, the type of bot content and interaction paradigm also differs. Line and Kik bots tend to be more brand engagement focused, and are more likely to be “loudhailer type” bots (i.e. Announcements and promotions mostly) than SMS or Messenger bots, which tend to be more varied across support and brand engagement.


Brands are companies who have launched their own bots, split by bot type in specific quadrants as defined above. Here we highlight some interesting bots and their functions, and their respective providers, if applicable.

Marketing x Managed

  • Universal Studio promoted their horror film ‘Unfriended’ with a Facebook Messenger bot speaking in the character of Laura Barnes[2], which was developed by Imperson — a conversational AI startup from Disney’s 2015 accelerator class.
  • Maroon 5 Bot[3] on Facebook Messenger, developed by Octane AI, improves the personal touch and interactions between the band and their fans.
  • The Gov.sg bot[4] on Facebook Messenger, built with KeyReply, helps the government of Singapore provide timely and accurate news and information to their citizens, and broadcast emergency news in the event they are needed.

Support x Managed

  • Citibank India’s virtual assistant, developed by Creative Virtual, is located in the Customer Service Center of their website and is designed to provide information to customer queries about Citibank products and services[5].
  • The Bosch service assistant, developed by Artificial Solutions, makes it easy for customers to troubleshoot an issue with an appliance or arrange a service call via its website[6].
  • NinjaBot [7](for Ninja Van, a fast-growing logistics company in Asia) developed with KeyReply, helps customers track and update their parcels before they reach them, deflecting queries to the support team about parcel status.

Marketing x Self-serve

  • Cheapflights, Kayak, Expedia, and more have launched bots of their own to give recommendations on travel products, help customers book flights or hotels, and send booking confirmations on Messenger[8].

Support x Self-serve

  • Marriott International launched Mobile Requests service (via its App) that includes an ‘Ask Anything’ concierge service and ‘Anything Else’ function that allows guests to chat directly with hotel staff, 72 hours before their stay[9].


Providers are companies who have the capabilities to deliver exceptional work in that specific quadrant.

Marketing x Managed

  • Conversable AI is a software-as-a-service (SaaS) platform for messaging and voice experiences across multiple platforms, including Facebook Messenger, Twitter, SMS, Amazon Echo, Google Home, and many others[10]. Some of the notable use cases are the Marvel Interative Story Bot, Pizza Hut Ecommerce Bot, and CES Twitter Guide Event Bot.
  • Assist started out as a local services chat assistant and developed into a chat provider that deploys bots on Facebook Messenger, Twitter DMs, Kik, iMessage and Telegram, among others [11]. They have served Fandango, 1800Flowers, Hyatt, and more.
  • Msg.ai develops chatbots for multiple channels and provides a dashboard to centrally manage the experiences[12]. They have deployed bots for Sony, CLEAR, and Signal, among others.

Support x Managed

  • KeyReply is one of the top AI bot providers in Asia for enterprises and governments. KeyReply’s AI capabilities aim to completely replace humans for some specific support tasks. KeyReply’s customers include the government of Singapore, Fortune 500 companies and Asian brands across multiple verticals[13].
  • Servicefriend provides hybrid bot technology for consistent messaging experiences at scale. They offer an “Interactive Text Response” (the text equivalent of IVRs, interactive voice responders over the phone) for companies such as Globe Telecom[14].
  • Kasisto enables banks to offer services through an automated “Virtual Specialist” so that consumers can use voice or text to assess financial information and perform transactions on their mobile banking application. It requires no coding by banking implementers and is designed for banks to private label their own brand[15].

Marketing x Self-serve

  • Chatfuel lets anyone build bots for Facebook Messenger. Their users include NFL and NBA teams, publishers like TechCrunch and Forbes, and millions of others[16]. Chatfuel is great for simple and focused bots, but may not work for complex chatbots requiring lots of customization.
  • Octane AI, focuses on allowing celebrity content creators to create tree-like stories that others can engage with on Facebook Messenger. Celebrities such as 50 Cents, Maroon 5, Lindsay Lohan, Jason Derulo, have used Octane AI to build their chatbots[17].
  • Gupshup is a bot-building platform that offers businesses pre-made bot templates. It offers both a “no coding required” version and an IDE for developers to build bots[18].

Support x Self-serve

  • Flow XO’s focus is to create an easy chatbot building experience[19]. Flow XO enables the deployment of bots to different platforms, such as web, Messenger and Slack.
  • Morph.ai is comes with built-in training and understanding modules to give a more accurate understanding of the business’s target audience. However, the platform only allows limited targeting for broadcasting[20].
  • Motion AI offers turn-key templates for many chatbot use cases including customer service, meeting scheduling and surveys[21]. However, it only operates on a multiple choice or single bot statement basis. It services global consumer brands including Kia, Fiverr, Sony, and Wix.

Some notes about the brands and providers above

Marketing bots tend to be largely campaign-driven, where it can be used effectively for driving engagement in short bursts. Longer-term marketing or sales efforts in the market are still mostly experimental, as it may be hard to define metrics for success without a strong indicator from the proof-of-concepts.

Support bots, however, have been around for much longer, and is a medium that customers are already used to. Metrics for deflection and customer satisfaction may also be more well-defined, hence there will be a “flight to quality” in this space to those providers who genuinely can deliver on their promises to answer customers well, not piss them off, and elevate the support experience.


These are supporting tools used by the providers and brands, or by developers of bots.

Marketing x Managed

  • Nuance has been developing highest functioning speech software in the world, perfecting the ability for machines to recognize and emulate the human voice[22].
  • Chatsuite offers app marketing experiences with chat [23].
  • Bot Metrics, a San Francisco-based company that specializes in metrics and analysis for chat bots has landed funding to help developers and early bot enthusiasts get a better understanding of their services and users[24].

Support x Managed

So obviously this is a quadrant that is pretty hard to characterize, because many companies won’t just reveal their tech stack where you can find them. Just based on our own exploration, we find that these are some of the more useful tools for machine/deep learning around the large datasets that come from brands, and there are other natural language processing tools and papers that contribute greatly to the endeavor.

  • Theano is the grand-daddy of deep-learning frameworks that many academic researchers in the field of deep learning rely on. Numerous open-source deep libraries have been built on top of Theano, including Keras, Lasagne and Blocks which attempts to provide a more intuitive interface as compared to Theano[25].
  • TensorFlow is created by Google with an aim to replace Theano. On top of deep learning, TensorFLow has tools to support reinforcement learning and other algorithm. One limitation is that TensorFlow runs slower than other frameworks like CNTK and MxNet[26].
  • Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is a well-known and widely-used machine-vision library, which is suitable for classifying and processing image. The use of Caffe is not applicable to other deep learning applications such as text, sound or time series data[27].
  • Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. Torch comes with a large ecosystem of community-driven package in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community[28].

Marketing x Self-serve

  • Wit.ai enables developers to easily create text or voice based bots that humans can chat with on their preferred messaging platform. The platform offers support to 100,000+ developers, non-coders in Wit community to build applications and devices with its natural language processing capability[29]. Wit.ai is well-suited to support complex functionality but mainly works well for small-sized bots.
  • Api.ai, owned by Google, is a visual tool to build decision trees for a chatbot, popular for consumer-facing applications, but clients report limited functionality[30].
  • ChatScript is the next generation chatbot engine which manage dialog or NL tools. ChatScript is a rule-based engine, where rules are created by humans writers in program scripts through a process called dialog flow scripting[31].

Support x Self-serve

  • Microsoft Cognitive Services’ initial release with just four services (face recognition, speech recognition, visual content recognition and language understanding) has now been extended to over 20 APIs. Furthermore, in 2016, Microsoft launched the Bot Framework for building a conversational user interface which links to Cognitive Services[32].
  • IBM Watson is mostly known for its premium solutions that require you to build a chatbot using its services. It also has the Conversational Service, which is lower in price and offers a visual tool to build decision trees that use Watson to map intent. IBM is pretty open on language support if that is a concern[33].
  • Montreal-based Smooch empowers software developers to create conversational experiences across any messaging channel. Smooch.ai is a simple-to-use solution, with Slack support and offers a free solution when the user base is small[34].

Using this landscape

What does your business need: Marketing or support?

  • Look at the nature of the product/service. What is included in your product/service package? Does your business provide only presale-service or both pre-sales and post-sales support?
  • If your products require constant interaction with consumers to drive sales (e.g. companies selling clothes and accessories such as H&M or Victoria’s Secret), you should develop a marketing chatbot.
  • If your products require heavy customer support such as product warranties, assistance to resolve technical difficulties (e.g. electronics companies such as Apple), your company should focus on developing a customer support bot.

How do you do it: Buy or build?

  • Does your company have the resources and capabilities to build software in-house? If your existing resources (i.e. technical skills, software infrastructure) can be easily transferable to the creation of a bot, your business should consider developing a chatbot in-house, either for greater control or cost. For example, Skyscanner has a strong existing tech team and robust algorithms; hence, they can apply that to their flight search bot [35].
  • Does your company have the ambition, human resources or budget to cope with complex NLP and data science issues that might arise? If no, then you might be better off outsourcing the development of chatbots, reducing the risk of investment in complex NLP, which is not your core business.

Should you do it: Strategic or faddish?

  • If your value proposition involves providing convenient and fast service to your customers, then developing chatbots in-house may enhance your value proposition and strengthen your company’s competitive advantage in the long run.
  • For example, a bank’s value proposition lies in its ability to provide convenient and 24/7 support to its customers (such as transactions, transfers, bank loans, debit/credit card applications, etc). Capital One has built an automated bot that can communicate with the bank’s customers via text message (and Alexa) to give them information on their accounts and help them make credit-card payments whenever they need[36].

What do you want to invest: All-in or experimental?

  • Do you want or need to have a full control of conversations with customer or customer data, and already have a good idea what a bot should achieve? If yes, then find an enterprise-grade provider, or build it in-house with a specific team dedicated to the project for at least 3 months.
  • If you’re simply experimenting with bots, then it’s fine if you sandbox some of your flows or data and build a small use case on the cloud, working with a provider for a proof-of-concept, or hacking together a small bot internally.

What’s next

Carylyne Chan

Carylyne Chan

I drive and build products, design effective user experiences, manage tech teams, write posts on interesting things, and generally learn as broadly as I possibly can. I also drink a lot of tea.