The Exchanges

Q coffee with, you know, the head of product at X company, right? Update the lead to a nine, right, in my, in my dashboard. So I love that it's conversational, but I still have to do work to put the data in, unless you guys are doing something or working towards something that figures out a way to kind of Automatically know or surface these things. What's the plan there?

A Yeah. So that was our big insight. The way that we started, we actually started with expense management and you know, you will type, Hey, I had a really good chat with Nathan and then we have a breakfast and I spent 10 dollars and then the system was able to understand it. We were able to capture the intention. You were trying to make an expense. And these are the data fields that you already talked about in the chat, and we're gonna ask you missing data, data fields. We did, we tried to apply the same thing into CRM, where we created like a free format thing where people will try to update things, and the problem was that, um, it's essentially the same paradigm that we're trying to disrupt. As you mentioned, it's like you have to be against, again, proactive, and sometimes you forget and everything. So the big insight that we had in the company is that you really want to flip the problem around. You want to be able to integrate, grab all the interactions, phones, emails, meetings, like virtual meetings, and extract information from that, surface it to the rep, and try the rep to answer as little as possible around that. Example, you send an email to someone. That email is not on Salesforce. The chatbot tells you, do you want to add this contact to Salesforce? And we have two buttons on Slack. Yes or no. So now, like, instead of like you going to the system, adding this, the, t…

AI assessment note: “grab all the interactions, phones, emails, meetings... and extract information from that, surface it”

Q When one of these beta users signs up, I'm sure you guys have dashboards there in your office. You're actively refreshing like every minute of every day going, did they do the first thing we know they have to do to make them sticky? What are some of those key things they have to do in the first one, two, three, four, five days to be sticky?

A Um, so this was, this was one of the, Francisco mentioned the, the big insight here is flipping this problem on its head. And so we're about an 80 to 90% of the interactions now are prompted by pseudo. So the most important thing for us is they have to get all the way through the onboarding process where we get connected to their underlying data stream. So we get connected to their calendar, we get connected to their email, we get connected to their Salesforce. Once we have that, and now we're, and now we're proactively Um, proactively pinging them with actions that they can take. And so what we see typically is we get a lot of interactions in the first few days because there's kind of this backlog of stuff that hasn't been entered into the system. A lot of customers you're interacting with that aren't on the system, and then it kind of gets to a steady state. And so the key thing for us is making sure people add contacts, making sure people are adding their notes from their meetings. Those are the most common interactions. Those are the most common things that our sales reps are doing. As soon as we start getting them into that Um, mode of interacting with Pseudo and taking actions that Pseudo recommends, it becomes much stickier. Yeah, the interaction before, when it was essentially reactive, it was like two, three times for the 20% of the customers that are the most engaged,…

AI assessment note: “making sure people add contacts, making sure people are adding their notes from their meetings”

Q So, Francisco, before we get more into what Sudo does, and, and how you guys hope to kind of really reinvent this industry, tell us a little bit more about, about your background. So, specifically, We know we have folks from hedge funds and PE firms on here all the time. What was the hedge fund you were running and help our audience understand what trigger power means?

A Uh, means that I could make decisions based on whatever I wanted on investments. And this was a hedge fund that, you know, I ran previously in Chile and for who became the president of the country, so it was his family office or his own money. Uh, but it operated like a hedge fund and a very successful one. Uh, when I arrived there, there were, he like, The size of the hedge fund was three hundred millions, and when I left it was 2.5 billion, so we, we had a really good ride there. Um, but you know, I really wanted to move into the creativity aspect of things. I was very strong technically, so I decided to come to the GSB to do at Stanford to do an MBA. And out of that, I started building my career as an entrepreneur here in the Valley, like an immigrant and, you know, trying to create jobs and disrupt industries.

AI assessment note: “means that I could make decisions based on whatever I wanted on investments.”

Q Okay. And what, tell us more about pseudo now. What are you using that money on? What are you hoping to change?

A Uh, so the problem that we're trying to solve is that we think CRM customer relationship management systems and enterprise are broken. And we think that the reason that they're broken is that they rely on salespeople to be proactive about manually entering data into the system. And so that leads to lots of problems with the underlying data because the reps are not inherently motivated to do this. It's not something that they get value out of generally. It's something that the company gets value out of. So you have the incentive problem. And then you've also got the software problems. Most of the CRM systems were in the best case designed in the nineties, worst case designed in the eighties. And, and they just, um, they're hard to use. If you've ever tried to use, you know, Salesforce or Salesforce's mobile application, you know, it's a time consuming process. And so if it's time consuming and it's annoying and you're not motivated to do it, you're generally not going to do it or you're not going to do it well. And so the underlying data is just messy. It's, you know, it's missing. It's incorrect. It's duplicative. It's not timely. And as a sales manager, as a sales operations leader, That makes it very difficult to run an efficient and effective sales team. You don't have the underlying data to know, do I need to have more sales reps or do I need to have fewer sales reps? Do I …

AI assessment note: “the problem that we're trying to solve is that we think CRM... are broken”

Q Got it. Okay, so give us a sense of kind of where the business is at today in terms of, uh, in terms of customers or revenue or users. Do you, first of all, are you guys post-revenue or pre-revenue?

A We are, we're pre-revenue. We're private beta. We don't believe in the MVP philosophy in the systems because building a chatbot, and we can go more over the conversational problem, but it's an incredibly hard problem to solve. So what we're doing is that we're working with like, uh, 30 or 40 customers right now, users, and, you know, we've learned tons from them, and, you know, also we've, uh, you know, we're not charging them because sometimes we have bugs, and like, you know, all those So, um, and, and, and we're engaging pilots with five companies right now. And with one of them, we are about to close them finishing a pilot with them. They're very excited about the product.

AI assessment note: “We are, we're pre-revenue. We're private beta.”

Q Ok, what was his? Oh, ok. Got it. Got it. Very cool. And what was the, I mean, how do you enter in, you guys obviously have great backgrounds, which probably gives you power over negotiating a valuation to minimize dilution, but walk me through that negotiation. How'd you kind of get to the valuation?

A Okay. So for that, so it was just super easy because I had a really good exit last time. Um, you know, the first time that I tried to raise money, I got 42 no's until I got one yes. This time, essentially we had one investor passing. So out of all the people we pitched, everyone chipped in. Um, and like, I think like at seed is very easy. You know, it's proven that like people who have had exits before the probability that they're successful is like way higher. Um, you know, statistically, so it was way easier to get people, especially people like that. I knew that like, kind of like put money before those guys really wanted to chip in and that helped me. And quite frankly, Joe really believes in this smart enterprise idea. And like, you know, the conversation with him was like a 10 minute conversation until he kind of like start negotiating numbers with us. We showed him a video of how the enterprise software works today. And he said, I'll write you a check right now. If you Let me stop watching this video.

AI assessment note: “it was just super easy because I had a really good exit last time.”