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Top 4 Chatbot Ecosystem Maps Compared [2024 Update]

Cem Dilmegani
Updated on Jan 12
3 min read
Top 4 Chatbot Ecosystem Maps Compared [2024 Update]Top 4 Chatbot Ecosystem Maps Compared [2024 Update]

Chatbots are one of the hottest areas of Artificial Intelligence, and we have explained chatbots in detail before. Social media revolutionized customer service by enabling customers to digitally reach any brand round-the-clock. However, social media also meant that companies needed to cover numerous communications channels effectively, and at times, simultaneously. Then came chatbots.

Companies are excited to take advantage of 24/7-functioning, intelligent, and self-improving chatbots that can handle most queries and transfer customers to live agents when necessary. These benefits of chabots would reduce customer service costs and increase customer satisfaction.

Given the potential value of chatbots, it comes as no surprise that an ecosystem (or an end-to-end field, if you will) is developing around them. In this article, we will explain what a chatbot ecosystems are, and compare the top 4 ones on the market today.

Understand the chatbot ecosystem in 3 minutes

At the heart of the chatbot ecosystem are chatbots themselves, so we put companies that build them at the center of the ecosystem. You can work with 2 types of companies to provide a chatbot solution:

End-to-end solution providers

Large companies generally need a relatively complex chatbot solution, and do not want to invest management time and engineering effort to build such a solution in-house.

Therefore, a significant number of enterprises are working with end-to-end solution providers that work with the client team to understand requirements, process customer data and use it to train chatbots and maintain them to ensure customer satisfaction.

These companies leverage latest developments in machine learning, deep learning and Natural Language Processing (NLP) to create chatbots. While bots still can not create conversational experiences like human conversations, they are beginning to handle basic customer queries.

Self-Service solution providers

Smaller businesses have less demanding requirements and a much smaller budget for their chatbots. For these companies, a self-serve solution is the right solution most of the time. Their technical personnel can leverage existing APIs or self-service tools to build a chatbot in a matter of days. Or bot developers at large companies may want to use self-service solutions to build their solution on top of an existing framework to save time.

  • If you are interested in building your company’s chatbot in-house, take a look at all vendors in this space from our blog.

Now that you know how to quickly build your chatbot, you need to get familiar with testing and analyzing its performance.

Chatbot analytics

Analyze how customers are interacting with your chatbot by leveraging chatbot analytics tools.

Chatbot testing

Semi-automated and automated chatbot testing frameworks facilitate bot testing.

Bot platforms

Once your bot is ready, it’s time to let it loose. Viral and useful bots have managed to acquire loyal followings in platforms like Facebook Messenger and Slack. For example, a successful virtual assistant, meekan acquired a loyal following on Slack.

We have an article that explains what chatbot platforms are in more detail.

There is more than one way to categorize the chatbot ecosystem

With interest in chatbots increasing, we are not the only ones attempting to categorize the chatbot ecosystem.


Let’s start with a relatively simple one. Oreilly’s market map is informative because it shows the ownership relations across the ecosystem, but it includes very few players:

Source: 1

BI Intelligence

BI Intelligence also prepared a ecosystem graph:

bii chatbot ecosystem
Source: BI Intelligence

The main difference between Oreilly’s and Business Intelligence’s ecosystem maps is that they provided a number of third party chatbots (chatbots used by companies for customer service and marketing) in the ecosystem as well.


Chatbot developer KeyReply prepared the most exhaustive but also the most complicated chatbot ecosystem map on the market:

Exhaustive map of chatbot ecosystem
Source: KeyReply

They spent a large part of the ecosystem map on company chatbots, like CNN’s chatbot that provide marketing or support services. The axis are a good aim to distinguish between chatbots that are built for support and those that are built for marketing. However, because of poor categorization, we weren’t able to see that much difference between bots in differing categories.

Finally, the tools category probably needs to be segmented further. For example, testing and analytics are different use cases. In the next version of our chatbot ecosystem, we will do that. also published a relatively simplistic map of the bot ecosystem:
Source: Applied AI

For more on chatbots

If you are interested in learning more about chatbots, read:

Finally, if you are interested in building a chatbot, we have a data-driven list of chatbot platform vendors.

We will help you choose the best one tailored to your specifications:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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