When Sam Liang co-founded Lundr.ai in 2016, AI was not yet a household word. However, in the years since, the transcription and note-taking platform has evolved in tandem the technology that powers it. Today it is integrated conversational knowledge engine helps users understand their business and personal lives using their database of dating records. Liang envisions a future where typing — even on a chatbot — becomes largely obsolete.
“When people use chatbots like ChatGPT or Claude, one of the biggest problems is context,” Liang told the Observer. “It takes a lot of effort to write a good request and provide all the context, but when you bring AI into the conversation, it has all the context.”
Used by 86 percent of Fortune 500 companies, Otter delivered $100 million in annual recurring revenue last year. company raised $73 million in venture funding in its first five years and surpassed 35 million users at the end of last year. It combines external integrations – such as Google Workspace and Anthropogenic‘s Claude—with built-in innovations like agent chat, which can pull data and perform tasks. Even large banks with strict compliance requirements are in discussions with Otter about incorporating the technology into their workflow, Liang said..
Otter has managed to compete with Big Tech firms like Microsoft and Google. While the note-taking features of Microsoft Teams and Gemini for Google Workspace benefit from being native to their ecosystems, Otter differentiates itself by working across platforms, including LargerGoogle Meet, Microsoft Teams and even in-person meetings through its mobile app.
Whether college students are trying to understand lectures, recruiters are screening candidates, or CEOs are reviewing missed meetings, AI agents aren’t enough to solve productivity challenges alone, Liang said. Instead, it argues that access to full contextual data from past encounters enables agents to execute tasks more effectively.
The Observer spoke with Liang about the state of AI in voice communication, competition with tech giants, and why in-person dating avatars are Otter’s next frontier.
The following conversation has been edited for length and clarity.
Otter began primarily as a transcriptionist and note-taker. When did it become clear that you needed to move on to understanding and acting on the conversation? And how did you handle that change in a way that was different from the paths we’re seeing at the big tech competitors?
This has always been our vision from day one, but it took us a few years to get meeting transcription and notes working well. With large language models and new agent technologies, it makes the knowledge engine possible.
There is another aspect to why we are making faster progress in the knowledge engine now. It is the change of mentality. It is the desire to seek AI solutions to transform the way people work. We are still in the early days, but it is starting to change.
Business or personal, it seems to require a change in mindset to make the most of AI. I actually have a smart fridge at home so I don’t have to manually add anything to my shopping list anymore, but it took me a while to get used to it. Now, as you’ve matured into this evolution that you’ve described, how do you measure success against giants like Microsoft and Google, who no doubt have a broader scope, but whose ambitions increasingly match yours?
When you build something new, you always face competition from big tech. Google, Zoom, Microsoft, their biggest advantage is their distribution channel. They already have enterprise penetration, so it’s easy for them to put something together, even for free.
But this may be their biggest disadvantage. Because it’s so easy for them to screw things up, they don’t really have a strong motivation to build the best product. Microsoft is not known for building innovative new products. They are known to copy others. For us, with the conversational knowledge engine concept, I don’t see them working on that right now.
My prediction is that they don’t care what we do until we hit $1 billion in revenue. Microsoft copied Lazybut they did not do it until they were already grown up. Salesforce won Slack when rated with 27.7 billion dollarsso we still have many years before Microsoft really pays attention.
You mentioned that AI benefits from as much context as possible. For business and personal use, some contexts are very sensitive. How do you balance the productivity benefits of voice data with privacy concerns inside and outside the walls of an organization?
A common security model already exists. In Google, Microsoft and notionyou can control who has access to any content. We created a similar structure at Otter. You can create private Otter channels, but there is some content that can be public to anyone in the company.
Many enterprises have information silos, which slow people down. Sales teams don’t necessarily know what marketing or product teams are doing, and vice versa. If you share more information between teams, you can speed up workflows.
From a similar lens, the question of authenticity is increasingly important. Video AI, for example, is facing many questions about authenticity. Outside of the obvious issues with deepfakes, do you see those concerns emerging in AI-enabled voice technology, or do they take a different form?
I believe that communicating by voice is actually more authentic, especially if you meet someone in person. However, if it was written by an AI, it doesn’t mean it’s not authentic. It can still come from authentic human ideas.
Voice communication is actually more likely to be authentic because you are speaking out of your mind in real time. In the future, we might build an avatar that can speak just like you, but hopefully that avatar is actually using your brain to speak, so it’s still largely authentic.
Do you have any plans to handle the face-to-face avatar in the future?
Yes, we plan to do that, but there are definitely still a lot of challenges to make it work really well. We have built an avatar for me already. or Bloomberg columnist interviewed my avatar at a live event. It went pretty well, and eventually, I see avatars for busy professionals to represent them in some meetings.
And in that regard, what are you hearing from your users and industry peers about the risks associated with that type of technology? How are they thinking about it?
Of course, one risk is fraud. There have already been a few cases of people using a clone of a relative to trick people into sending money, so that’s the best way to detect this and warn people. There are risks to any new technology.
Looking ahead, what do you think is the next frontier for voice communication in collaboration with AI, and how do you see Otter shaping it, especially alongside big tech and these AI giants?
I think more and more enterprises will adopt the conversational knowledge engine. You put all the appointments in this system and use it to drive human and agent workflows, making enterprises much more productive. There is always competition from Big Tech, but I believe we can innovate much faster.
