Progress Software 财报超预期，上调全年指引

Well, it's great to have you here. The software space, the application software space has certainly been on our radar in the last year or so. Tell us about the quarter and maybe why you think possibly investors are. Initially they sent the stock up, but uh, they pulled back a little bit. But walk us through the business. Well, thank you and happy to be here. And, you know, we're delighted with the quarter that we delivered.

Um, our customers are clearly, uh, excited about the offerings that we have. Um, as you pointed out, we exceeded our, um, expectations, both on top line and bottom line. We raised guidance for the full year. You know, investors in the markets do what they do. Um, but from our perspective, our business is doing well. And we continue to be confident about, uh, where it's going. And, uh, one of the big drivers for actually for any software company that is doing better today is around AI.

And so we're seeing significant interest in our offerings to help our businesses, uh, get their arms around, uh, controlling and putting, giving context to AI so they can get better outcomes and get better value. You argue that's a driver of your business, but I think it's fair to say that a lot of software companies have been caught up in what analysts are referring to as the so-called SaaS apocalypse, and the concern that some of those, uh, companies that are offered on a per seat basis or software as a service, companies that are subscription services might be replaced by, um, companies that are created or apps that are created, or services that are created through people using AI to code.

How would you respond to to that and your moat, making sure that you can still succeed in a world of coding? Oh, absolutely. So, you know, what we offer is very different than than the characteristics that you share, which is what makes us excited about what I can do for our business. So, uh, when you think about AI, right? When you think about white coded applications or when you think about even agents built with AI.

And so I, uh, agents running on top of information, fundamentally, what they need is context, because without context, they have no way of getting the right answers and the right outcomes for a business. They can't be reliable, they hallucinate, etc. so what happens is businesses say, oh, let me take all my data, let me take all my content, let me take all my documents that I have and try to give it to I. And then you end up with two economics issues, right.

You're just beginning to see that today, that as, uh, these AI companies are finally sort of truly passing the costs along to the customer, uh, of the AI infrastructure, uh, the token omics have gone through the roof and expenses are going through the roof, so you need to control that as well. Yeah. I was talking to a friend about this over the weekend. He has a group subscription to one of these labs. Yeah. Um, and he's talking about.

Okay, well, he could he can burn through the token allotment, you know, just a couple hours in a given period of time, you know, in that order, it'll, you know, he can buy more, it'll reset. But they really have to allocate, as you know, as his, his enterprise version of the software, he really has to allocate his token usage. So Jim, you have to correct. And so the question is that's one way of doing it. But then you can't have an agent that works like a human being 24 by seven or more than a human being.

That's the promise of I. If you have to then control the tokens it can use. So, so what we do and what our products do is provide that context to I in a much more effective and efficient way. So we have a data platform business that can aggregate all data structured and unstructured data content as well as, you know, systems of record data, bring it together, do analysis on it and provide it in a way that it's AI ready, dramatically reducing the amount of tokens needed to process that and to answer questions for agents to run, etc..

In addition to that, uh, we also have other product offerings, what we call around our infrastructure management that help manage the AI infrastructure that is coming around and make sure that it is secure and reliable and consistent. So when you think about it, our business is very different than than the business of, you know, seed based applications and those kind of things. We we our products are not seed based. They are based on things like data volume and data consumption, which, as I'm sure you can imagine, is only growing.

It's kind of like having a CFO over your shoulder with every little task that you do and kind of determining. But I mean, help me understand because I feel like there's a lot of companies out there. You know, when we talk about AI, is is that kind of what it is that, you know, give me an idea or give us an example of a company that you work with and exactly what you're doing for them. Yeah. So I'll give you an example.

Right. Um, a large organization, one of the, you know, top four consulting firms in the world has terabytes of data, terabytes of documents, and they need to use those to address business decisions, business inquiries, analysis for customers, etc.. Now, if you upload it all to I, the context window for AI becomes extremely large and the amount of tokens consumed becomes ginormous. Instead, what they do is they use our platform.

Our platform analyzes that figure out which documents are truly relevant for what type of work it produces, the actual documents that need to be provided from, let's say, millions of pages down to a few hundred pages. And now the amount of tokens will be used. Is is, you know, 1000, right. Or even smaller. And that completely changes the equation. For example on on the cost. While at the same time it actually improves accuracy because the eye doesn't get all the other garbage. 999,000 pages of documents that it really gets confused on.

Hey Yogesh, before we let you go, before we let you go, how how is your team use and how have you instructed your team to use AI internally? Oh, internally we are using it extensively. Our development teams are all building products using AI. Uh, our operations team are using AI to, uh, to, to do their work on a day to day basis across the board. We are using it to interact with our customers. Uh, we're using it for all kinds of things, including, uh, truly helping sales folks, helping and driving folks, helping finance folks.

It is it it has been a transformational journey over the last three years.