Shabtai Adlersberg22:41
Okay, so it all started with me sitting daily in six, seven, eight meetings a day, 30 minutes long, and getting out of those meetings you don't remember what was decided, what were the action items. So we started to develop it prior to the pandemic, and it was hopeful because the voice systems, the recording quality, the meeting rooms were awful. So it was really a slow start. We really struggled with all kinds of issues. Then came the pandemic which drove a lot of the collaboration solutions like Teams and Zoom, so that made the system essentially better in quality. It was spread. And now Microsoft with their meeting room initiative and Zoom, they all want to make your meeting room the center of the business and decisions. So we were able to evolve from that initial product. The idea, listen, there are two levels, and basically here is something that I don't think many people paid attention to, but we are not the first note-taker in the industry. You have a series of startups, names like Otter and Fireflies and others, which are doing it mainly more in the Zoom space. And then there are the big guys, you have Zoom and Microsoft who are also developing their own first-party. Now, so I'm building the product as follows. We try to excel on the meeting level. So we take care that recording and transcription, by the way, transcription is not an easy thing simply because yes, you can get 80, 90% accuracy, but once you get to different organizations, one from the sports arena or from government or politics or healthcare, the huge amount of concepts and terms that they typically, speech-to-text will not recognize. And arguably the most important terms for them and their company, right? They are the terms that decide whether we, they, or what they meant exactly. Now it's a chain, and the strength of the chain is, as you know, related to the strength of each of the little pieces in it. So if you don't have a perfect start, your inference, your LLM solution and prompts and everything comes afterwards will suffer because it will take different terms. So we pay a lot of attention of course to come up, and we just launched a SaaS solution back in March. We're happy these days, we have already tens of customers, about 50 customers, growing fast, more than 10, 15 a month. But that's on the meeting level. But then, myself, I consider myself also not only an engineer but a manager. And to me, the fact that there's an important meeting taking place today in San Francisco with an important customer, and I, the decision maker, have no direct access to it because yes, I'm told, yeah, it was a great meeting, yes, they are interested. At the end of the day, like in any other step in the industry, you need to validate. And once you get access to that recording, and of course all those recordings, three years ago nobody in the industry wanted to hear about it. Nowadays they ask you, well, you have a recording of the session? I'd like to get it. So you get the recording, you get to understand who said what, speaker recognition technology, who said what, and then you get the insight for, I'm a project manager, you are a sales manager, we have different perspectives on what's the summary of that meeting. So we're getting all those recaps. And now we're putting them all at AudioCodes because these days we have about a thousand employees and about 500 are US-based. So in that environment we have today close to 200 meetings a day. All those meetings, recording, transcription, and insights, they all go into one repository. And now management can assign small teams that focus, let's say, on SBC or IP phone or recording. We can identify, we attach tags to every meeting so we can pull out a bunch of meetings relating to a certain topic. And then I can work with, guess what, the best chatbots in the world, could be Copilot, could be Q, could be Gemini, could be Claude. And now because these guys doing nice work but not great work, right? I think everybody that's been using those chats know that it's accurate up to a certain limit. Why? Because they do the search on a huge, humongous amount of data. If you narrow it down and take now Meeting Insights as a preprocessor who identifies those X meetings that relate to it, now the chatbot will do substantially better work. So that's how we're trying to position Meeting Insights.