We’re living in a golden age of conversational AI platforms. From customer support automation to enterprise-wide AI-driven workflows, businesses have more choices than ever. But with so many options, choosing the right solution can feel overwhelming.
Some platforms promise human-like conversations, while others focus on structured, reliable task execution. Some are built for scalability, others for regulatory compliance, and a few are simply hype-driven ventures still stuck behind waitlists.
So how do you separate practical solutions from empty promises?
This Telerivet article breaks down the most talked-about conversational AI platforms today and analyzes their strengths, limitations, and where they fit in your business. We’ll cover:
- What is conversational AI?
- How is conversational AI different from generative AI?
- What are the main use cases for conversational AI?
- The difficulty of choosing a conversational AI platform
What is conversational AI?
Conversational AI is about using software to replicate the experience of having a conversation with another person.
Conversational AI is a branch of artificial intelligence (AI) centered around a specific use case—in this case, chatting. It falls within the broader category of machine learning, and current solutions approach the challenge from different directions.
Even though generative AI has dominated the news over the past three years, the field of conversational AI goes back decades. Many foundational practices, such as natural language processing (NLP) and natural language understanding (NLU), continue to be just as useful—if not more useful—than certain elements of generative AI. However, the effectiveness of these technologies depends on their intended use. We’ll explore that in more detail later.
Conversational AI is currently one of the biggest use cases for AI technologies. It has played a key role in making AI more productive, revenue-generating, and valuable within business processes. So far, conversational AI has been the killer use case for AI.
How is conversational AI different from generative AI?
Conversational AI and generative AI have significant overlap, but in many respects, they refer to broadly different things.
Conversational AI is focused on a specific use case—maintaining conversation and doing something productive with that conversation—whereas generative AI is perhaps a broader category or refers to AI creating new things. It may seem like generative AI is the broader concept, but when we talk about Gen AI, we're typically referring to large language models (LLMs) and their outputs. In contrast, when we talk about conversational AI, we might be referring to LLMs, natural language processing (NLP), natural language understanding (NLU), or a series of other approaches.
The problem with generative AI, as we know it from OpenAI’s ChatGPT, which is most people's primary exposure to it, is that it is difficult to have it execute tasks in a consistent and standardized fashion.
Many natural language processing applications allow conversations to be acted upon and tasks to be executed.
Right now, LLMs can be very good at retrieving information from a dataset, though they do hallucinate. But they are not very good at fitting consistently into a standardized workflow, where they must interpret information and then undertake tasks.
That more action-oriented approach is typically where other conversational AI methods are being employed. Some platforms have tried to meld the two approaches in order to create an effective, functional application of AI, using NLP and NLU while leveraging the advantages of the more human-sounding LLMs to create a better overall user experience.
What are the main use cases for conversational AI?
There are lots of use cases for conversational AI.
Conversational AI is typically packaged around conversation as the use case, but many of the core underlying technologies are not limited to that.
Right now, it is a very popular approach for customer support. In fact, customer support is an ideal use case for conversational AI, as most support functions within organizations already have a hierarchy of help in place—for example, Level 1, Level 2, and Level 3 support.
Level 1 support is typically about pointing users to relevant help articles, answering basic questions, and retrieving simple information.
Level 2 support may involve a more skilled human agent, such as a customer support engineer or an account manager.
This Level 1 support, which is primarily based on information retrieval, is quite easy to automate—or at least partially automate—with AI.
This is one of the use cases where generative AI also performs well. In fact, generative AI has some advantages over NLP or NLU in this context, as it can retrieve information across vast datasets without needing to be explicitly directed.
However, it does come with a challenge: it is still prone to making things up—so be careful.
Where NLP or NLU might come into play is in walking a user through a problem, guiding them step by step through a triage process.
This allows the user to interact with the AI in a meaningful way—providing information, having that information understood, and being guided to the appropriate next step.
This demonstrates how NLP and NLU solutions can help people engage with software through conversation, rather than relying on the more open-ended nature of a large language model.
These same principles can be applied in sales, conducting surveys, providing emergency relief, and a range of other front desk activities.
The technology behind conversational AI can even be used on the back end—for example, to turn qualitative data into quantitative data for better analysis and decision-making.
The difficulty of choosing a conversational AI platform
On some level, we are living in a golden age of conversational AI platforms, with a wide range of software to choose from.
The flip side of this is that it becomes difficult to make a decision. With such a heavily saturated market, it's hard to weed out the best choice.
Beyond that, it's also difficult to bet on startups that seem like a good fit for your needs—because you don’t know whether those startups will succeed, shut down, get acquired, or be forced to pivot in the near future.
There's also the issue that many companies advertise their ability to perform various tasks associated with natural language processing (NLP), when in reality, they are simply a wrapper on a large language model (LLM).
This means they can't always deliver on their promises to the extent they claim. This is a broader issue in AI right now—there are lots of big promises, but very few solutions that truly fulfill them.
Another challenge is that for many of these AI solutions to work at scale, they require a lot of processing power and need to integrate with other areas of your company.
For instance, they need to:
- Know what other interactions have occurred between your product and that customer.
- Have context about the customer, including how they use the platform.
- Understand the platform and its help materials.
- Reach the customer where they are most likely to be.
If budget is not a constraint, you can likely find a solution in the market that meets these needs. However, if you are budget-constrained, you may have to prioritize certain outcomes over others.
This is a difficult product to deliver at scale, which is one reason why many very large brands have been able to quickly take dominant positions in this space.
Conversational AI Platform 1: Sierra AI
We're kicking off with Sierra AI because I want to start with the most hyped tool in the space.
Six months after first applying to try it, Sierra AI is still a waitlist-only product, and I have yet to gain access.
It was founded by Brett Taylor and Clay Bavor.
Brett Taylor was co-CEO of Salesforce. He founded Quip, served as CTO at Facebook, and started his career at Google, where he co-created Google Maps. He is also on the board of OpenAI.
Clay Bavor spent 18 years at Google, where he led Google Labs, Google's AR/VR efforts, Project Starline, and Google Lens. He also led the product and design teams for Google Workspace.
It's hard to find two founders with more reputation and expertise in this industry to lead a product like this.
If Sierra AI can do everything it promises, then—wow—it would likely be the most intelligent tool on the market.
It's also raising money at high valuations, but the jury is still out on whether it can truly deliver the level of conversational AI it claims.
This is what conversational AI aspires to be, but it's unlikely that Sierra AI is actually as functional for your business right now as other tools in the market.
Feel free to apply to their waitlist, and if you get in, let me know all about the product!
Conversational AI Platform 2: Telerivet
Telerivet is primarily a connectivity platform—it provides connectivity while also allowing you to centralize multiple sources of connectivity into a single, unified command center.
As part of this, Telerivet offers:
- Interactive voice response (IVR)
- Natural language processing (NLP)
- Natural language understanding (NLU)
So what does this all mean?
It means that with Telerivet, you can utilize conversational AI in more structured ways across all the channels your customers actually use.
You can engage with customers in any country, through text, WhatsApp, voice, Viber, and more.
Telerivet’s NLU capabilities don’t provide the open-world, highly human-like interface of a generative AI large language model (LLM).
However, at the same time, Telerivet’s product is more functional, with broader practical use value.
It is more effective for structured applications, such as:
- Conducting surveys
- Running a triage flow in customer service or emergency response
It’s also likely a better choice for heavily regulated industries that cannot afford LLMs generating incorrect information on the fly.
Telerivet can operate at serious scale, supporting super apps in Southeast Asia like Grab.
It can also function in challenging conditions, such as supporting humanitarian aid efforts in war zones with organizations like the Red Cross.
Additionally, Telerivet’s pricing is significantly lower than other options on this list.
So if you need NLP and NLU at scale, across the full range of channels your customers already use, give Telerivet a try.
Conversational AI Platform 3: Amelia AI
Amelia AI is a popular conversational AI platform that makes some pretty big claims.
It was recently acquired by SoundHound AI, a California-based company listed on the New York Stock Exchange.
Amelia AI positions itself around the idea of providing autonomous agents, and its services scale up along functional lines.
To that end, you can use their services as what they describe as:
- An Answer Engine
- An Action Engine
- An Autonomous Agent
This reflects the same synthesis of approaches we've discussed earlier in this article.
I don't know exactly how Amelia AI builds its software under the hood, but:
- An Answer Engine is a good way to describe what an LLM can do for customer service.
- An Action Engine is a good way to describe what NLP and NLU can do for customer service.
- An Autonomous Agent, which can deliver support while still being augmented by humans, is the natural synthesis of these two approaches.
Amelia AI boasts some major European and Asian brands as customers, so it may be worth exploring how they fit into your market.
Conversational AI Platform 4: Boost AI
Boost AI also makes a big song and dance about its ability to synthesize natural language processing (NLP) with generative AI—and to do so through both text and voice.
One of the interesting qualities of Boost AI is its claim that it allows you to adjust how hybrid you want your solution to be.
For example:
- You can tone down the generative AI, which may reduce its human-like qualities but increase its accuracy across a wide range of use cases.
- Or, you can turn it up to maximize its human-like qualities and retrieval abilities.
They identify this as a useful feature, particularly for highly regulated industries.
Highly regulated industries often want the ability to reduce the generative AI component while still keeping the benefits of NLP.
This recurring theme highlights part of the difficulty in this market.
This is a very complex technological problem, and it is expensive to run, especially at scale. As a result, nearly all offerings that attempt to combine generative AI with NLP are targeted at the enterprise segment.
However, the enterprise segment includes many highly regulated industries that cannot afford to have a customer-facing AI hallucinating information.
How a platform like Boost AI performs long-term in this market will depend on its ability to tailor the hybrid model to its customers—ensuring value gains without running into regulatory or quality issues.
That said, Boost AI is a useful platform to consider, particularly if you need voice capabilities. However, its voice solutions are significantly more expensive than Telerivet’s.
Conversational AI Platform 5: IBM Watsonx Assistant
IBM’s latest assistant, this time called Watsonx, is IBM’s very capable entrant into this crowded field.
Watsonx seems highly capable and is well-liked by its users.
Broadly speaking, it does everything you would expect relative to the rest of the market. It provides conversational AI support, primarily through NLP or a lightweight LLM, across different channels—all while integrating deeply and effectively with your company's existing tech stack and materials.
Really, this is exactly what you would expect from IBM.
IBM’s software products tend to offer:
- A deep range of functionalities and customizations
- Strong suitability for large enterprise customers
- A fairly high price tag
With IBM, you are likely getting some of the smartest software, as they have a range of internal models they can leverage and deploy using proprietary techniques—rather than borrowing from other vendors.
That said, as with many IBM products, you may be missing out on some cutting-edge options—or at least on the latest trends in UI and UX.
IBM won’t necessarily move fast, but it also won’t break things.
In a field full of startups that may not be here tomorrow, IBM will be here tomorrow—which brings low risk on that front.
That said, if you were going to buy IBM, you probably would have already bought IBM.
Conversational AI Platform 6: Sprinklr
Sprinklr is a conversational AI platform, yes—but it’s also much more.
Sprinklr positions itself as a platform for running the entire front office, covering marketing, customer support, and customer success functions.
This is a big play that appears to be aiming more at competing with HubSpot, but from an AI-first starting point.
The ability to be integrated across the entire customer-facing side of your business presents clear advantages, and Sprinklr has a strong conversational AI offering to support that vision.
If you’re looking for a solution to transition your entire customer-facing organization, Sprinklr is one to consider.
However, if you’re simply looking to deploy conversational AI in a chat button—whether as part of a specific customer channel, a regular workflow, or a one-off project—Sprinklr may not be the right solution for you.
After all, you don’t sign up for HubSpot just to send an email.
Conversational AI Platform 7: Amazon Lex
Finally, there’s Amazon Lex, which appears to be the parent branding for Amazon’s conversational AI offering.
Amazon’s product here does a lot, and it’s definitely a great tool for large organizations—as one would expect from a solution offered by Amazon Web Services (AWS) rather than a more consumer-friendly Amazon product.
Amazon Lex and its conversational AI tools are a very DIY set of solutions, where some of the products are marketed toward customer success teams, support teams, or marketing teams.
Amazon’s offering is deliberately designed for developers, but it is also very powerful. It can accomplish many of the same conversational AI capabilities that other businesses on this list claim to provide.
Of course, it doesn’t come cheap, but it follows a usage-based pricing model, so you pay for the value you get.
If you’re new to AWS, there are also various credits and schemes available, allowing you to piece together your own version of a free trial.
Amazon’s offering is strong—but who is going to buy it?
Well, if you’re already paying Amazon tens of thousands of dollars a month (or more), then what’s the harm in adding a few thousand extra on top of that?
If you’re already well-integrated into the AWS ecosystem, then Amazon Lex might be a good choice for you.
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