Chapter 1: Who is Arto Minasyan and what is his background?
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The main challenge back then was data. So we tried to record a lot of noises, noisy conversation, tried to apply different tactics to generate a huge amount of data to train our proprietary machine learning model.
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Chapter 2: What inspired the creation of Krisp.ai?
And it took about a year before we got a really working prototype for the technology. And our primary idea back then was to license the technology to the big companies, right? But then we realized that we need to have some demo for them, right? We need to give our potential customers a tool to assess the technology faster and easier. That's why we decided to build our desktop application.
My name is Artur Minesian. I'm co-founder and president at Crisp.ai.
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Chapter 3: How did COVID-19 impact the adoption of Krisp.ai?
I don't exactly know what to do next. It took many guys to get right. Who built the teams that have their back. A company is its people. The teams help each other achieve more. Most proud of our team. Keeping scalability top of mind. All that infrastructure was a pain. Yes, we've been fighting it as we grow. Total waste of time. The stories you don't read in the headlines.
It's not an easy thing to achieve, mind you. Took it off the shelf and dusted it off and tried it again. To ride the ups and downs of the startup life. You need to really want it. It's not just about technology. All this and more on Codestory. I'm your host, Noah Laupart. And today, how Arto Menazia built the best AI meeting assistant with bot-free recording and noise cancellation.
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Today's episode is brought to you by .Tech Domains. And this one hits close to home. Back in 2016, I was building my startup and went hunting for that perfect .com and found next to nothing. So I did what every founder does, settled. Here's what I wish someone had told me. You're building a tech startup. Just get a .Tech domain. It instantly tells investors and customers what you're about.
Don't overthink it. Get a .Tech domain for your startup today. Arto Menazian is originally from Armenia. He's a serial entrepreneur, having started seven companies, selling four of them. He used to be into the sciences, having his PhD in mathematics and machine learning. But outside of tech, he's married with two kids.
He loves to read novels and, in fact, writes books himself, mainly his memoirs. He loves to ski, and aligned with his Armenian heritage, he loves to spend time with his big family. Arto and his colleague got breakfast together and started talking through an idea around clean audio for conferencing and beyond. They built a prototype and then COVID hit, which made their tool very popular.
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Chapter 4: What challenges did the team face while building the technology?
So I got excited, and after a year, we got a prototype of the technology. Then we moved to U.S., got into Berkeley Sky Deck Acceleration Program, raised some seed capital. And by 2020, we already had a very well-working technology and an app. And then when COVID hit, we basically had a tech which was very useful for everyone working from home, right?
Because at home, you have a lot of background noise. You have kids crying, dogs barking, just neighbors making random noise and so on. And Crisp basically became very popular back then. But we didn't stop at this one technology, and we decided to expand our offering with different AI technologies.
So we introduced accent conversion, meeting transcription, summarization, and now recently we also introduced voice translation technology. So the offering expanded from just one feature to a lineup of different voice AI technologies.
And basically the ambition to grow the company number one voice solution in the market by bringing like the best voice I take either like in-house, built in-house or just like integrating the right voice I take into Crisp applications.
Let's dive into the first version of Crisp, that MVP version that you built. How long did it take to build and what sort of tools were you using to bring it to life?
First we built the technology and it was like a machine learning model, a neural network that we trained in-house. And back then we didn't have a lot of GPUs and we didn't have knowledge in like different frameworks, like TensorFlow and others. So we built like a lot of things in-house trying to train the neural network. And the main challenge back then was data.
So we tried to record a lot of noises, noisy conversation, tried to apply different tactics to generate a huge amount of data to train our proprietary machine learning model. And it took about a year before we got a really working prototype for the technology. And our primary idea back then was to license the technology to the big companies, right?
So any other conferencing solution or a phone manufacturer can embed our technology inside their product. But then we realized that we need to have some demo for this, right? We need to give our potential customers a tool to assess the technology faster and easier than we just can do, let's say, with API or SDK. That's why we decided to build our desktop application.
And the idea was to build like a virtual microphone, which sits on desktop computer. The first version was for Mac. And then when audio goes through our virtual microphone, we can apply Argoid and basically modify the audio stream and get rid of the noise. It took probably around a year before we got the first working version.
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