Generative Now | AI Builders on Creating the Future
00:04:49 · Mike Mignano, Gabe Stengel
[00:04:49] S0: Yeah. What's a PIB? What is a PIB? [00:04:51] S1: Tell us that. A PIB is basically a senior banker says, hey, we're going to meet with, let's call it, you know, Datadog. Give me a PIV on the company. I. E. Put together all the resources I would need to get smart on this company over twenty four hours. So you pull, it's a, each MD has their own formula, but maybe the last two ten Ks, last 10 q, some initiating broker research coverage, some recent broker research coverage that's good, any sort of interesting news articles that have come out recently into one, you know, 200 page condensed PDF that you email to them, and then they can leaf through and and see if there's anything interesting or or write you a follow-up. So it's just a information packet to get started, which now is a little bit dated. Right? Like, you don't need this 200 page research packet when you can just go into a tool like Rogo or ChatchPT and say, hey, tell me about Datadog and what's been going on. But yeah, I was I was doing the very basic banking work. And I loved it. I mean, I actually, I think there's this, this perception that no one likes investment banking. I don't think that's true at all. I mean, I think you get access to some of the most interesting executives, companies, see how they think, see how they think about M and A capital allocation. And I I really enjoyed it.
00:06:01 · Mike Mignano
[00:06:01] S0: Putting together a PIB, especially without AI a couple years ago. I mean, it sounds like a ton of work, especially if you're telling me these things need to come together in like twenty four hours and this is a 200 page PDF. I mean, was the initial thinking like, hey, this is a lot of manual work. AI is obviously helping to sort of give people leverage on work and units of intelligence. Let's make this like a PIP creator. Is it... Was that like sort of the basic idea?
00:06:26 · Gabe Stengel
[00:06:26] S1: Yeah. I would say it's funny. I mean, The basic idea was, yeah, like there's all these types of work, like benchmarking revenue multiples, putting the other PIPs that are so basic and, you know, not interesting and there should be an AI tool that can do it. But I would say the thesis then and the thesis still now was always more so how do we empower people to do things they can't do, right? And that goes back to our original senior thesis, which was giving our peers the power of writing Python code through a natural language interface chatbot to do their econometrics work. And even now, I want to make sure we're doing all the mundane work that junior bankers and junior investors do. But I also want to empower them to do things they've never done before. Right? I want to help them do all sorts of new types of analysis, new types of insight generating work that wasn't possible before. And so even at the time, I think we had loftier aspirations. Took us a while to even get to the mundane automation. But it was always focused on, hey, how can I, you know, give you, Mike, a tool that allows you to do the types of analysis that before you couldn't really do if you didn't have a team of data scientists or a team of juniors underneath you?
00:07:28 · Mike Mignano, Gabe Stengel
[00:07:28] S0: Got it. So it almost seems like the thing that really becomes accessible with a tool like Rogo, at least in the early days, was sort of almost like the data science function and like the real deep analytical work. Not necessarily the stuff that, you know, as you said, you can do a lot of this stuff in Chatuchibi Tea now with through like a deep research query. A deep research, I'm sure, isn't doing a lot of the data science work, but it's probably doing some of like the basic just like pulling of of of existing data and records to keep somebody like just generally more high level informed. [00:07:57] S1: I mean, look, Brogo is in many ways like Deep Research except plugged into financial data sources and financial tools. And so instead of only having access to the web and a Python code interpreter environment, our Deep Research has access to the bank's internal data, their CRM, their SharePoint, their precedent transactions analyses. And then it also has access to tools like Excel, like filings, like FactSet, Cap IQ, PitchBook, all the financial data sources that they use. And so it's basically just deep research that's been trained to be a Wall Street analyst as opposed to just anyone's research analyst. Yeah.
00:07:57 · Gabe Stengel, Mike Mignano
[00:07:57] S1: I mean, look, Brogo is in many ways like Deep Research except plugged into financial data sources and financial tools. And so instead of only having access to the web and a Python code interpreter environment, our Deep Research has access to the bank's internal data, their CRM, their SharePoint, their precedent transactions analyses. And then it also has access to tools like Excel, like filings, like FactSet, Cap IQ, PitchBook, all the financial data sources that they use. And so it's basically just deep research that's been trained to be a Wall Street analyst as opposed to just anyone's research analyst. Yeah. [00:08:31] S0: Yeah. So, all right. Let's talk about that a little bit. So you mentioned things like PitchBook. These are, you know, these are data sources that are out there on the web. You have to pay for them, but they're out there. And then you mentioned sort of like the company's data. Is that really like sort of the secret sauce there in that it takes your internal data that's proprietary, and then it links it up with all this stuff that's on the web but maybe hard to get and something like ChatGPT can't just go and grab?
00:08:53 · Gabe Stengel
[00:08:53] S1: I would say there's three things that make Rogo special in our IP and make it a valuable product. One, yes, is the content, the data that you have access to. Right? We work with providers like S and P Global, Cap IQ to make sure that we have, you know, if they... If we have mutual customers, they can query the Cap IQ data through Roko. That is a business data licensing problem. The second is the actual tools that the model has access to. Right? Even if you have this ingenious LLM, if it doesn't have access to a tool like Excel, can it really, you know, build a full three statement model? It can in Python, but that's not auditable and it's not really built for that. Can it make a PowerPoint output? Maybe, but probably not the one that's up to your your output specification. The final thing that's different is our reasoning and the actual model quality. And the same way that, you know, if you look at Claude four or Gemini 2.5 Pro or, you know, GPT four or three, they're ingenious super smart models, but they don't know how to use the tools, the data sets, and do the workflows that bankers and investors do. And if you had, you know, Albert Einstein start at Lightspeed on Monday, just because he's smarter than everyone at light speed, which may or may not be true, doesn't mean he'll actually be a valuable... Maybe. Maybe. I don't think he would be valuable on day one. He needs to learn how you guys think about a company, how you think about investments, how you use tools, what your formatting is, right? And that's actually learning. And so we teach the model how to do that by using a technique called post training or reinforcement fine tuning to learn how to use the tools that finance professionals use.