Your AI Meeting Agents Aren’t Enough: Otter.ai's Sam Liang on Enterprise Knowledge
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What is the background of Sam Liang and Otter.ai?
Hello, everyone, and welcome, humans, to the Neuron Podcast. I'm Corey Knowles, editor of the Neuron, and we're joined, as always, by Grant Harvey, writer of the Neuron Daily AI Newsletter. How's it going, Grant?
Going well, going well. Today, we've got Sam Leong, the co-founder and CEO of Otter AI. Before Otter, Sam built the blue dot for Google Maps, you know, that thing that tells you where you are. Now he's transcribed over a billion meetings. And last I heard, crossed $100 million in annual revenue, which is pretty awesome. With less than 200 people at the time, that's pretty awesome. Welcome to the show, Sam.
How is Otter.ai evolving beyond meeting transcription?
Thank you. Thank you for having me here.
Yeah. So you recently announced that Otter is moving beyond being just a meeting note taker app and building into an enterprise knowledge base with agentic workflows, MCPs in there, kind of trying to take the insights that people get from meetings and then expand that and connect all of your enterprise tools to it. That is a big, awesome strategic shift. Could you explain the thought process there and what all you're doing in the enterprise space? I think it's really awesome.
Yeah, of course. I wouldn't use the word shift. I would say it's an evolution.
What is the significance of a meeting-centric knowledge base?
We created the AI meeting note taker space. Basically, we started back in 2016. We launched our first product in 2018. Then we built the other AI meeting note taker that can join your Zoom meeting, Google Meet, Microsoft Teams, WebEx. No matter what tool you use, Otter can help you. You can work for online meetings, for offline meetings. Like if we meet in person at Starbucks or restaurant, we can use Otter mobile as well. We also recently released Otter MacBook. We're working on a Windows version as well. So I would say there are two stages. The stage one is the meeting note taker. The idea is that all of us spend so much time in meetings. There are a lot of data show that enterprise knowledge workers spend at least 30% of their time in meetings.
And if you're a manager, if you're a VP, you spend maybe 50, 70, 80% of your time in meetings. Traditionally, all this data is lost.
How can voice data improve enterprise efficiency?
People use a paper notebook. I still have a paper notebook in front of me. Or Google Docs or Notion to manually take notes. So we create the AI meeting note taker to automate all of that. um then we see that the uh more and more people are using this uh even in fortune 500 companies or tons of people using honor but most people are still using it as a individual tool um you know they they record something they keep it to themselves um but the Value is way bigger if you aggregate all these meeting notes as a team. For ourselves, for example, we have just over 200 people now. We record almost all our meetings in the last eight years. Sales meetings with customers, marketing meetings, product, project management, design, recruiting.
Both external meetings and internal meetings. That allows us to operate really efficiently. We organize the meetings in either public channels or private channels, very similar to the way people organize their workspace on Slack. We actually build Otter workspace using the same model as Slack because we see the similarity between Slack and Otter. Because basically Slack you communicate using text messages. But on Otter, we capture all the meeting contents where you communicate using voice.
What are the potential applications of AI in meeting workflows?
The similarity is really strong because between Slack and Otter, you basically talk to the same group of people on the same set of topics. So that's why we built Otter Workspace in a very similar way to Slack. So Otter Workspace allows you to organize and manage all your meeting contents. So effectively, it create a meeting-centric knowledge base. The reason I use a meeting-centric, the reason is interesting is that traditionally when people think about knowledge base, they only think about the written documents, like documents in Google Doc, Notion, emails for Slack message, or some data in MySQL CRM. People rarely think about voice data because traditionally all the voice data is all lost.
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Chapters
8 chapters
1
What is the background of Sam Liang and Otter.ai?
0:07–0:42
2
How is Otter.ai evolving beyond meeting transcription?
0:42–1:22
3
What is the significance of a meeting-centric knowledge base?
1:22–2:24
4
How can voice data improve enterprise efficiency?
2:24–4:18
5
What are the potential applications of AI in meeting workflows?
4:18–5:51
6
How does Otter.ai ensure data security and privacy?
5:51–7:08
7
What challenges does AI face in understanding human conversations?
7:08–8:28
8
What future innovations can we expect from Otter.ai?
8:28–50:27
Speakers
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