The Exchanges

Q Do you want to talk about what decision intelligence, decision automation, what, what all of that means?

A Yeah, definitely. So I think it's, it's kind of easy to start with the, the fact that billions have been invested in, uh, you know, both data science and AI. And, uh, and this is a fact and like people have been talking about this space for awhile, you know, you built on, uh, kind of the digitization era, which was like, make sure we have event data for everything. Right. And then from there you went to kind of like BI. So, you know, what's happening in my world, right. Can I understand what happened in the last like week, et cetera. And then you went to data science, which was fundamentally like answering the question of like, what is possibly going to happen or like predictive modeling and really where decision intelligence is like the next layer on top of that. Uh, so it is the space around what, what should I do about it? Right. And so that's actually why we picked the name next move. So what's your next move? Uh, and you know, it's, it's really that space. And so, um, if you think about that, it's kind of the next evolution of a data stack. I like to think about a simple example could be like a subscription box, like I am a user of Stitch Fix or Birch Box, right? And these types of companies, they may have a data science group that's working on what is the likelihood that I'm going to like an item, right? And so they're trying to predict, right, if I'm going to like this s…

AI assessment note: “decision intelligence is like the next layer on top of that”

Q Okay. And, uh, there is, um, I think a lot of overlap with a space known as operations research. Uh, is that correct? Do you want to talk about what is, what, what's the, what the overlap is?

A Yeah, definitely. Um, so the space around operations research is really around optimization, um, as a technology and, and simulation as a technology. And really, these are what we kind of refer to as decision-making paradigms. These are technologies that have been around for a while, uh, that are used to make decisions, uh, but they are typically thought of in very academic communities, um, and implemented on kind of like legacy tech stacks. And so we use some of these, the platforms around that space. So, so you think about, um, in the optimization space, thinking about solvers, solvers are a fancy way of saying, Hey, I'm going to generate all the possible plans and pick the best one, um, based on my criteria. My criteria could be, I care about not being late for a delivery service or in a stitch fist example, I care about what is my, my maximum return right on, on that, uh, that allocation. Right. And so these are KPIs. If you want to think about it in the most general sense, And so operations researchers are these PhDs that are used to thinking about that space in mathematical terms. They take all of that business context that we just talked about, and they boil it down to matrix math. And so effectively they're translators, um, and they're translators that are, you know, very well educated, obviously, and trained, um, to use these legacy tech stacks. And they come into a bu…

AI assessment note: “Yeah, definitely. Um, so the space around operations research is really around optimization”

Q again, was that a, how, how, how broadly horizontal space it is. So like the simplest way of thinking about it is like all the, like the routing problems and delivery and logistics and all those things, but that's actually a, a small part of like the very wide range of, of different use cases. Uh, can, can you talk about some of those, uh, use cases across the enterprise?

A Yeah, definitely. Um, so I think there's, there's been some interesting ones recently. Uh, we're kind of working on, on a project right now, uh, with some folks around like how to basically route different x-rays, um, to providers to give feedback, right? So, uh, like call center routing. And so people don't kind of like think about this as, as an optimization problem, but I thought this one was kind of interesting. It's like, I, you know, if I had like a horrible break, right? And I'm like, I'm looking for someone to read my x-ray. Uh, that was a problem that, that, that came up recently that we were working on. Um, we have another customer who's doing, like, humanitarian aid. Like, how do you allocate, uh, different resources to provide aid in the fastest manner and to cover, um, the most need, uh, the quickest, right? And so, like, there's some different interesting, uh, applications there. I obviously talked about a little bit about, like, the matching and allocation problem around inventory, uh, with, like, subscription boxes. So, um, there's that case, but there's also things around, like, pricing, price optimization, uh, Marketplace matching. So like, how do I efficiently match supply and demand? Um, really at the end of the day, like we kind of think about this as, as very horizontal. This should be like how you represent decisions for your operation as code, right? And…

AI assessment note: “route different x-rays, um, to providers to give feedback, right?”

Q And the fundamental premise of next move is, um, to effectively democratize this, um, whole area, which, um, has been the, the, the province of like math PhDs and sort of like older soft, uh, older, yeah, software platforms. Um, how, how do you, how do you go about that? What's the, what's the sort of the, um, thinking and ethos behind the product and platform?

A Yeah, definitely. Uh, we think about making, you know, every engineer decision engineer. Uh, so in the same way that Twilio gave, you know, a bunch of engineers, the primitives around how to create messaging, right? We're giving people the primitives about how to make decisions. And so when we think about that, we think about our platform from the perspective of you, you and I should be able to sit here and define a new decision for our operation, whether that's allocating marketing budget, or that is creating a dispatch service. Uh, we can define what the input output is. How, how we think about caring about it and what the business rules are for possible plans. Right. And so our platform enables kind of all those steps. You can, you can build a model from scratch, defining input and output. You can push it into deployment. Right. So deploy it via something like serverless, right. Or into our cloud architecture. Right. And then you can also define what you care about. Right. So that's the value function or kind of a fundamentally like what is guiding your decision. Um, so defining that is like, is the KPI that you care about. Um, so that's kind of how we think about, about going about building it. And, and really that's the, we think about as an end to end, you know, decision automation platform, which is model management, which is the work bench basically for, for building an…

AI assessment note: “we're giving people the primitives about how to make decisions”

Q So what's, um, what's next for the company in the next, uh, year or two? What's, uh, what's on the roadmap? You alluded to some of this, uh, but what's on the roadmap and what, what, uh, what do you want to be able to do?

A Yeah. So, uh, we recently launched in our, in our like cloud console, um, the understanding of, uh, like configuration. So being able to configure, uh, different models, what, what we're excited about kind of, and we're already prototyping now is the ability to, like I said, create that custom decision and to push that up into our console environment, to collaborate on that with other, other users, um, and to really build on top of that. And so we're excited about that for a few reasons, like, There's, uh, we're really intrigued by what developers will create, um, and what, you know, what our users will create on top of this. Like, we've been in this space, like, we've lived optimization simulation tech for, for a long time, and there's, like, this academic mindset around that for problems that, that it can apply to, but I think the cool part about a platform is you're really building generative technology, um, so we're excited about how people kind of stitch these pieces together from, you know, the I.O. perspective to what their, what their decision is, Um, you know, Ryan, one of my co-founders built a Sudoku solver on top of our platform, right? I mean, like, that's silly and not, and not really business relevant, but it's really interesting. Can you do Wordle? I really want a Wordle solver. We've been talking about it. Uh, I would like to be able to solve Wordle in millisec…

AI assessment note: “what we're excited about kind of, and we're already prototyping now is”

Q Great. Um, so yeah, let, let's double click on that. And what, what are the different parts of the platform and what can you do? And, um, what kind of, uh, skills do you need to have as a developer to be able to use the platform?

A Yeah. So the things that you can do and, and, you know, I kind of, I started with this, right? You can build any custom decision, right? So we think about, you know, decisions being plans, right? And so like, how do you generate the plan that is addressing your business need? And so as a developer, you are typically already going to be thinking about this in the context of your business problem, right? You're going to say, okay, what is the input data that I can, uh, use to make this decision? What is like required? So in the inventory example, right? For Stitch Fix, I have to have all my potential inventory, and I have to have scores for inventory that matches to the, the people that I already have subscribed, right? And so that would be your input data. And so you're saying like, hey, this is my contract. This is my data contract between the model and my services. Um, and so you can define that. You can define your output contract, right? And your output contract is the, what you're going to go operate on, um, what your system needs to go effectively make that plan a reality. Um, so when we were at Grubhub, right, that would be What is the route for a driver so that I can send it to a driver application, right? Um, because they need to see it. And so those assignments, that sort of thing. And so you can do both of those things. Um, you can also define what's possible. So, uh,…

AI assessment note: “You can build any custom decision”