Key learnings for deploying enterprise conversational AI at scale from Gartner Magic Quadrant Leader, Cognigy.
Presented by Deepgram and Symbl.ai
Deepgram is a Speech Company whose goal is to have every voice heard and understood. We have revolutionized speech-to-text (STT) with an End-to-End Deep Learning platform. This AI architectural advantage means you don’t have to compromise on speed, accuracy, scalability, or cost to build the next big idea in voice. Our easy-to-use SDKs and APIs allow developers to quickly test and embed our STT solution into their voice products. For more information, visit:
See how easy it is to add simple but powerful call coaching and call tracking functionality to your customer experience solutions with Symbl.ai’s customizable Conversation Intelligence APIs. From calls to videos to text conversations — apply best in class contextual AI in no time by getting started for free.
Conversational AI at scale
Differentiating any product is hard, let alone differentiating a product in a crowded marketplace…
Crowded markets often lead to a decline in price, as everyone scrambles to win loss-leading deals with the hope of making the revenue up on the back end of a contract.
The conversational AI market is crowded
In the conversational AI technology market, there’s apparently over 2,000 different providers. If you’re one of those companies, how do you differentiate among competitors? If you’re a company trying to find a technology to use, how do you decide?
Some conversational AI vendors have focused on specific industries. ‘Conversational AI for banking’, for example. Others have focused on ‘low-code’ platforms in an effort to make their product more accessible.
There’s lot of ways to slice it and no ‘wrong way’, as such.
The shared goal of conversational AI platforms
However conversational AI platforms decide to differentiate themselves, they all have a shared goal. Growth. And when you grow, you need to be able to manage scale.
Scale of the technology to be able to handle the volume of queries hitting your servers. And scale of the tooling to enable large organisations with multiple teams to utilise it.
Conversational AI at scale
Earlier this week, I sat down with Cognigy CEO, Philipp Heltewig, to discuss how Cognigy approaches this challenge. How do you provide technology and tooling to large organisations and enable them, not just to launch a small bot handling a few use cases, but fully capable, multi-channel assistants handling thousands of customer interactions per minute?
This conversation is incredibly timely, given that Gartner’s Magic Quadrant for Enterprise Conversational AI Platforms was released this week, and Cognigy made the Leaders position.
I left the conversation being convinced of one thing: Philipp has paid real attention to detail when building out the Cognigy platform and taking it to market. Details that become absolutely critical when you’re an enterprise searching for technology that can operate at scale. Security, permissions, technical proficiency, flexible hosting, you name it. Things that some platforms can overlook.
You can learn more about how to scale your organisation’s enterprise conversational AI strategy effectively by watching the full conversation with Phillip Heltewig Or you can listen to it on any podcast player you desire.
Learn at the Cognigy Academy