The Conversational AI Ecosystem
The companies, products, and technologies shaping the future of conversational agents.
Conversational AI is a fast-growing industry with a number of start-ups and established companies offering a wide variety of products and services for an even wider variety of customers.
We compiled, reviewed, and curated nearly 200 companies and technologies, created one big list and categorized them in several ways to try to help understand what’s taking place in the space:
- Category of Product / Service Offering
- Domain or Industry Area
- Investment / Funding
Builders vs. End-Users
As we started reviewing the various companies and their offerings it became clear there were broadly two classes of offerings: those companies that offer technologies for builders: Developer Platforms vs. companies that offer products and services for enterprise end-users: Enterprise Platforms.
Within the builder category, there are several types of companies most of which tend to be closer to the machine learning software itself and designed for software developers or product analysts. Within the builder category, these are the high-level categories of offerings:
- Data & Annotation: Data is the basis of training cutting-edge machine learning (ML) models and for this reason, there are many tools to label (annotate) data for text, speech and images also. Moreover, some companies also offer access to proprietary data.
- NLP/ASR Libraries: Organizations (mostly universities, research centers, etc.) that open software libraries for building ML models, bot logic, deployment utilities, and other development tools specific for bots used by developers and ML engineers.
- NLP/ASR APIs: Companies that offer NLP (Natural Language Processing) or ASR (Automatic Speech Recognition) models as a service through APIs. For example, speech recognition, noise removal, and intent and entity recognition.
- No-Code Bot Builders: Here we include a really big space of platforms oriented to building bots without the help of a developer, usually with some sort of nice visual drag-and-drop system.
- Bot Analytics: Like Google Analytics, but for bots. Maintaining and improving a production bot requires monitoring and tracking several performance metrics and helping to understand and address several performance questions e.g. is the bot properly recognizing the intentions? what kind of mistakes is the bot making?
For companies that are looking to leverage conversational AI as a service or to directly solve a business problem, there are different classes of companies:
- Conversational Analytics: Companies that apply ML to provide substantial insights from conversations, documents, emails, speech, and any text or voice-based source.
- Conversational Coaching: When scale customer service and call centers, one key issue is how you standardize the quality of the team. For this reason, there are many players creating coaching or assistant tools that help during the interaction between an agent and a customer. Many companies also provide performance metrics from these interactions.
- Transcription Services: With COVID-19 and zoom fatigue, transcription of meetings became one new productivity tool for remote teams — perhaps you can read the notes if you couldn’t attend the live meeting. Companies utilizing video captioning can also take advantage of these kinds of products. Some features can include detecting the speaker, noise removal, collaborative tools to edit the transcription, matching captions and video, real-time and batch services for different loads of tasks, and more.
- Omni-channel Bots: Now that you have a nice working bot, how can you fully take advantage of it in all your digital channels? Many companies offer bot services for integration into channels like Facebook, Whatsapp, Web, and Mobile apps. In addition, sometimes you want a human in the loop that takes control of the conversation if the user needs to talk to a live agent.
- Industry Specific Bots: There are many players who are applying conversational AI technology to solve specific business problems in industries like banking, insurance, health, customer service, recruiting, marketing, and many more. In some cases, the companies offer an end-end service, and in other cases, they offer components necessary for a specific industry solution — e.g. pre-trained intents, NER (named entity recognition), ontology, or other parts of bots.
As mentioned in the previous blog, we found interesting domain-specific bots in the following areas: finance & insurance, health & medical, HR & recruiting, restaurants, and contact centers & customer service. Because of the volume of activity and interest in the area, we’ve also included sales and marketing/lead generation as another domain-specific area. Some sectors, like education, make use of bots substantially but primarily leverage customer service and general-purpose bot platforms, rather than industry-specific bots.
5 Areas Where Conversational AI will be a Game Changer
Market size and opportunity for conversational AI technology.
Follow the Money
As you may have read from the first article in the series, this is a big space with huge market potential.
Conversational AI Explained
An exploration of voicebots, chatbots, intelligent virtual assistants, and the latest machine learning technologies…
Players in this space include the big F-MAGA companies — Facebook, Microsoft, Apple, Google, and Amazon — that invest heavily in natural language processing, speech, and conversational AI R&D for a wide variety of technologies and tools across the spectrum of ASR, NLP, bot builder services, intelligent virtual assistants and more. We’ve referred to this category as Big Tech — and they have virtually all developer and enterprise offerings.
And while investment funding is not the best indicator of the success of a company, it is one quick way to get a sense of how investors see the possibilities and expected value in the future of the space. The list of companies compiled includes a number of private companies who, according to Crunchbase, have over $5 billion in aggregate private investment and VC funding (as of the publication date of this article).
Here’s a visualization that shows how the companies break down in terms of funding and focus area:
We’ve also included a link to a google sheet containing the list of companies, URL, funding (if available and found on Crunchbase), category, industry, and some news articles about the companies. We’ve done our best to collect, curate, and categorize the companies and technologies but if you have any feedback or corrections you’d like to suggest, please let us know.
* Update Apr 9, 2021: We updated the google sheet with several companies that were shared with us. We also updated the ecosystem image at the top of the post. I don’t expect we’ll do another update of the image itself anytime soon — but we’ll do our best to keep the google sheet updated.