Answered raw tape
D 4 · C 5 · P 4 · Cm 4 4.30
Q And so can you tell our audience a little bit more about Globunt and, and how you are tackling these problems?
A We believe that, um, all these AI, AI things will kind of, uh, evolve in, in, uh, in a pretty, I would say, um, Fast way, but also there won't be, in my opinion, one model that will dominate the whole landscape. Instead, we will have, like, different models having different, you know, specialties. And the secret here to future solutions don't rely on just paying attention to one model, but to understand which is the best model to solve your specific problem. And I will separate The race for AI in two specific, you know, uh, portions. The first portion is who's gonna get the best LLM or the best model for the specific business case, and that's one, that's one race, right? And then the other race, which I'm more interested in, is how to apply those things to make real cases and how to apply those things to change the way companies operate. Uh, on that second race is Our race is what we are doing every day in front of our customers. While then many, you know, LLMs today we counted about 140 different models plus versions of the model, uh, that are integrated into our platforms. Um, so, so what I'm saying is all these technologies here, the difficult part for me is creating the agentic, you know, workflows, uh, to be able to solve the problem. So our approach to that Has been to create a platform which we call a global enterprise AI. On one side, it connects to 140 models in which …
AI assessment note: “our approach to that Has been to create a platform which we call a global enterprise AI”
Answered raw tape
D 4 · C 4 · P 3 · Cm 3 3.60
Q And can I ask you, so you talked in the beginning about these are probabilistic systems. So when do you know, let's say you're in the procurement phase, that you can trust the system to automate?
A Well, look, I think that all these probabilistic systems Must, must be matched with human supervision. I mean, there's not any of those systems can work without a human supervising, uh, an enterprise class with humans supervising what those systems produce. So I think at the end of the day, you gain a lot of efficiency when you chain the agents together, but you can never forget about humans Just checking what those agents are producing, because sometimes they could hallucinate, they could do something that is wrong. Uh, so I think human intervention as always is very, very important. Um, but humans become much more efficient supervising those things than, you know, just running the whole process by themselves. So I think that this is, um, um, a quite interesting way of understanding. I think also that we Time and with models becoming better. And I don't know it becoming better. The, the largest problem that you will face in any corporation is to put the right context in front of them all. And that's the largest challenge. I mean, one, the context is correct. The probabilities of a hallucination goes much lower. So the largest question we need to answer to implement these things and make it, you know, Enterprise class is about the generation of the context, and in pretty much every company, generating that context is extremely complex, and that's why I'm saying this probability…
AI assessment note: “all these probabilistic systems Must, must be matched with human supervision”
