Making Cities Awesome: Peregrine’s Nick Noone & Ben Rudolph

Pyrogram is really around this idea of how can we leverage technology to work with our cities, our counties, our states to impact the places that we live in? At the bottom of the pyramid, really, when it comes to how to make cities awesome is the idea of safety. The idea that people need objective safety and also need to feel safe. And when that stability is there, it's amazing what's possible. And there are all these kind of contrasting but actually similar concepts that we were wrestling with at the time. We were thinking about how to deploy inside of institutions and also find ways to eliminate the mishandling of information inside of really complex organizations that have access to super, super sensitive data. And then if we could create and of the backbone infrastructure level of the modern city, then we could help to preserve individuality. We talk about privacy and other aspects of what it means to be like an individual person, but also bring people together. One of the most important stories in AI is from a company that you may not have heard of, but is almost certainly keeping you and your loved ones safe. Paragrand is building AI that protects cities and communities, and they are rejecting the surveillance state while doing that. And this should be a fantastic conversation about one of the most important applications of AI, forward-to-ployed engineering, civil liberties, and much more. So let's get into it. Nick, you ran Palantir's SOCOM unit. You deployed into dangerous high stakes and thousands operations in the Middle East. Tell me what does forward-to-ployed engineering actually mean? What does Silicon Valley get wrong about it? Palantir was a really fun experience and a really formative one because before joining the company, I had no idea what this concept of forward-to-ployed engineering meant. But I was surrounded by people that were running into customer environments and doing who knows what. I watched them. I watched them leave the office. I watched them come back to the office and tell stories. It just felt to me, like the adventure of a lifetime, to be able to go into a customer context and own the customer's problem, but also take pride in the fact that the problem belongs to the customer and then take pride in getting the customer to the win. These two truths that I learned when I was an early forward-to-ployed engineer and that we tried to embrace as we built our company. Those are that we psychologically go in and own or co-own the problem. We talk internally about getting all the way to the outcome with and on behalf of our customers. Technology in all of the skills and ways of delivering our tech is part of the answer, but the real answer is just getting to the outcome at all costs, ideally three to five times faster than any other person our team could do. And everything flows from there, but recognizing that at the end of the day, it's the customers win, it's not our win, and taking deep pride in the impact that we have on the institution and on the people that we're sitting across the table from. I think that's where the magic is culturally. Would a Silicon Valley get wrong about it? When we first started building Paragrin, the forward-to-played engineering concept had been around for a while, and we got feedback that when really smart people from top five, top 10 or equivalent schools would go into a really complicated place like the LAPD and quickly try to understand 30 years of institutional context. Not just at the data level, but at the human level. How does this organization work? How are decisions made? How do teams interoperate with each other? The idea that moving fast is even possible in that environment is very counterintuitive to the customer. And unfortunately, I think that shows up as ego to a lot of organizations where people have dedicated their entire lives to a particular role. And I think Silicon Valley misses that sometimes, that letting go of our intelligence, letting go of our own skills and abilities, trying to suspend our ego, and then really get into the customer's context. It's an easy thing to say, but it's such a high empathy, patient way of working. Yeah, I love that. Ben, while Nick was off deploying into Middle East, you were in almost the opposite part of the world, in every sense of the word. You were doing refugee work in Africa, in India. But you landed on a similar thesis of the world and technology's role in it. Maybe say a word on that. Yeah, absolutely. So for me, I've always been obsessed with this idea of how do you make impact with technology? I graduated school and I remember I had this two offers. One was to go work at a small startup at the time, Airbnb, and the UN Refugee Agency, which is an organization that goes and helps refugees who are making these extremely dangerous tricks across country lines, usually because of war, to try and get help. And so I took that path and deployed to a Sudanese border, the Colombian border, and was deeply humbled and really understood, deeply understood these types of situations and where technology can help, and where honestly it can't help. And I remember the thing that I took away from that experience is UNHCR is this deeply disconnected organization. It does phenomenal work, but it's deeply disconnected. They have some data that's often in spreadsheets, and it's very difficult to actually make sense of. And so that's where I started to form this thesis of a lot of these problems are downstream of data problems where you can have a lot of impact. After UNHCR, the UN Refugee Agency, I left to join an organization that you probably haven't heard of. It's called Demagi. They're a small company. They work building last mile health care solutions and under resource places around the world. And so like one specific example, one project that I worked on while I was there, we were working with the Indian government on this project, building applications to help folks in rural India adhere to their tuberculosis drugs. And at the end of the day, we did get it deployed, and that technology rapidly spread throughout the country. And that was a really cool moment for me, and really formed my thesis and idea around how Paragon can have impact, and how you can have technology can have impact in the communities that we live in. And so Paragon is really around this idea of how can we leverage technology to work with our cities, our counties, our states to impact the places that we live in? Take us back to 2017. You called your way into the San Pablo Police Department. What was going through your heads when you did that? In 2016, when Ben and I got together actually on a vacation with a bunch of our gymnastics teammates, we had a conversation basically 10 years after we graduated saying, "Should we build something together?" We got together in San Francisco and with my experience in the national security space and similar in the US government, Ben's experience working on humanitarian causes. And on my end, I'd been flying back and forth between Baghdad and similar places in the Middle East, and Ben was commuting, God knows how many flights into Africa, and we looked at ourselves and said, "This is the place where people live. This is the place where people thrive. This is where the majority of our lives are spent." And then we started thinking about how to deconstruct working with American cities. And what we came to is at the bottom of the pyramid, really, when it comes to how to make cities awesome is the idea of safety. We were thinking about how to deploy inside of institutions and also Find ways to. eliminate the mishandling of information inside of really complex organizations that have access to super, super sensitive data. And then if we could create a backbone infrastructure level of the modern city, then we could help to preserve individuality. And the idea that we could do both of those things, preserve and protect individual privacy while also bringing people together and help them realize that if we're staring at the same information, we kind of want the same thing. Yeah. That's what I find really beautiful about cities. And I think we're very aligned on that. Beautifully said. How many people said no before San Pablo said yes? You know, it's such a blur at this point, but it's definitely over two dozen. In my opinion, we started the company on February 26th of 2018. We'd incorporated the company a month or so earlier. But February 26th is the day that the San Pablo, Northern California Police Department, permitted us to step into the building with our badges. We had desk space and we had access to information so that we could actually get to work. And the reason we got there is we started a research subject matter experts in the art of public safety at the city scale, at the municipal level. And we found this amazing article about a young, up-and-coming commander in a local police department named Brian Boobar, who was catapulting up the ranks inside of a paramilitary organization that's a very structure that takes years and years to reach higher levels of leadership. And he was young and intelligent and creative. And there's this article we found online about Operation Red Reach, which was one of the most iconic cross-terrestrial gang-affiliated narcotics investigative work that happened in Northern California at the time. And Brian was a covert operator trying to figure out how to move this stuck investigation forward. And I thought that took so much courage for someone like Brian to come in and be the reason Operation Red Reach. And it obviously wasn't just Brian, but so many people surrounded that work and did something so impactful and made headlines. But more importantly, you know, changed the state of safety in Contra Costa County. We just called him and said, Brian, we don't know that much. We may have some utility, but I don't know. Can we come in, ask you some questions, learn from you, try to understand some of the awesome things you've done, including some of these amazing prior investigations, and then maybe build something over time and just see what happens and lead with that level of trust. And that was what finally got us access. It's a good chance on you. Yeah, truly. Do you do Redalongs? We have amazing photos from those days. Our first Redalong was actually with Oakland PD, yeah, which was, which was way, when we were still in this like deep research, and that was a really high-opening and interesting experience overnight, I think. Yeah, it was also eye-opening. It's so, I mean, the most people that are coming, I think, from Silicon Valley going on Redalongs are not necessarily engineers at the time, you know, reporters or other types of community interest organizations. And so, I think we had the wake love service at the time. And it really was, it was eye-opening that it's so difficult to penetrate and build trust with these organizations. And so there are so many lessons at the time that signal to us that we just have to take our time. You started this company 2017-2018. I think D-Fund the police was, you know, was starting to get going maybe in the early days of the company. What did that feel like? And how did it make you dig deep and think about your values? I'll share that, you know, on a personal level, I think I'm a very, I tend to be a very experienced, based learner, like very tactile, very, you know, I want to hear the concepts, but build the bottoms up understanding through action. And I have found in my life with that mentality, it fundamentally leads me to question assumptions. And the intent is not to question assumptions in a low trust way, in a skeptical way at all times. It's not, you know, I think there's a danger of becoming overly cynical. But to really understand truth and love, like curious adventure, I think that is the kind of that the psychological backing that led us to build our business in the midst of a really tumultuous time in America. You know, I have a personal affinity for working with people who sometimes feel misunderstood, frankly, and it just is. It's a very intuitive passion for me to help help them feel more understood. I think many people at our company probably have character qualities like that, frankly. And when I think about moments like COVID and the protests that were happening at the same time, the convergence of, you know, social unease in America, we were holding a lot of complexity. We had very close friends that we've graduated with and built things with in the past that were standing outside of the Women Center in San Francisco, protesting the police. And we were driving a Honda Accord across the Bay Bridge out of San Francisco every day to work with gang homicide investigators. And we were holding those two truths at the same time. And I, to me, it's not enough to look at a very difficult situation from the outside, like the privilege is jumping into the pool and swimming. And I think through that process, we established empathy. We realized that, oh, this, you know, this thing that we thought was wrong is actually really complicated. There are a lot of gray zones. And actually, the deeper we go, the more nuanced and textured, the problem actually is. How empowering is that, right? And so in some ways, even though in that period of time, our business is much more diverse now, right? I mean, we work. The through line is delivering safety and prosperity to cities. But at the time, we were only working with police departments now, police fire emergency management health services, you know, all kinds of stuff going on. But engineers wouldn't even respond when we made a recruiting call. I mean, that was a fascinating time. And I think that that level of polarization, to me, for both of us in our lives, I think we've seen the pendulum shift. And we're trying to deliver solutions that are apolitical that are what people, communities, groups of people, these institutions that serve them truly want to need. And in many ways, I actually don't think that's controversial at all, if done well. How is your business structurally different from the data collection companies like a flock or an axon? So we've been operating in public safety since 2018. And I think most of the companies that have come before us have built their business fundamentally on the back of data collection. So the idea that a hardware or software solution is installed inside of a customer base and is fundamentally about the input, whether it's passive collection through a sensor or, you know, human beings like inputting information directly, the collection and storage of that information. Historically, those companies have grown because once they have information that it belongs to the customer inside of their system, plus distribution advantage, they can kind of sell more stuff. And those the additional stuff tends to be more collection systems. Peregrine is like the inversion of that entire model. The idea for us is we're fundamentally in the business of joining disparate information to provide a more secure, properly governed, governed solution that sits on top of the preexisting systems that helps people do better work, helps people get more precision and accuracy in the answers to their questions. And I think for companies like ours being not in the business of bringing more data to a customer, but in the business of building solutions that drive greater levels of precision and how a human being interacts with their data is kind of like flipping the historical model on its head, so to speak. Okay. So structurally, almost the business incentive is the opposite, almost. It's not about maximizing the amount of data that you're collecting and creating network of X of that data. I think the world's very concerned about the idea of the amalgamation of data and the kind of central, whether a public sector or private sector or authoritarian body that has access to this privileged information and what will they do with it. And for us, we almost want to decentralize that to get to a world where we are actually not in any shape or form in the business of bringing more data to the customer that they don't already have. The fundamental problem is that they can't utilize the data that they have or utilize it in a way that's secure and high trust for the communities that they serve. I'll just say from day one, we've been really focused on thinking about this idea of permission controls, data governance, sovereignty. These are necessary ingredients to kind of deploy to these types of high stakes institutions and Nick is right when he talks about trust and we think a lot about trust, trust both with the community. These are the, these public servants are ultimately serving the community members. We have trust with those public servants and the community that we have to uphold. And so we think a lot about this and how we build what we build and how it interacts with the end user. Yeah. It seems like the data governance and data being so locked down is almost the way to reconcile the tension between public safety and not be humming the surveillance state like that is the answer. Kind of this idea of you have local, local intelligence, local data, everything's locked down. Actually maybe share a bit about your philosophy on data, data ownership and data sharing. We deeply believe in the idea that each customer, each institution owns their own data. This is ultimately the, that organization is serving the community, that is the community's data, that is the organization's data, it's not paragraph and data. And we think about providing the, the controls and capabilities to allow them to very securely roll us out within their own department, but also share this with select pieces of information when they need to. Without these types of solutions, you get folks, you will put a bunch of data in the back of a car and drive it across a city and there's, there's actually less control there. And so we think a lot about where, where can we actually enable the outcome for our user and provide the controls that are specific and robust, that enable them to not over share too much and that they can trust in what they're, what they're actually delivering. One of the biggest use cases you see for your customer base, maybe whether people starting to play with now and then where do you see that going? Yeah. This is what excites me the most. I. I think, I think, I think when you initially do deploy this type of AI to these organizations, you often just get what I'd say a nice search, you know, maybe you were looking for a address. And now you don't have to look at a bunch of rows and you see everything that happened that address in a nicely formatted way. I think the, as we have had users get more use to the product and understand it better and how it works, you start to see the forget raised for everybody across the department. You see these really interesting deep types of analysis that previously were just impossible. So I'll give a couple examples here because this stuff's really interesting to me. We're working with this county in Florida and the other month they had to do over a hundred water rescues and they're like, why? We've never had to do this many water rescues before. Water rescues is a, when there's a flood, rescuers need to get on a boat and literally go out and rescue someone from their car that might be stalled in the middle of the flood or their home. Yes. And so they started, they started asking Paragrand and starting interrogating this question. And what they, what they started to uncover is that, okay, these weather patterns they've happened before, but after the couple iterations and the agent doing some deep research, it came out with the pattern that, oh, these weather patterns have never occurred for three consecutive days. And those types of weather patterns, they create these sand channels and those sand channels are the perfect conditions for rip currents. And those rip currents cause a lot of issues for people who are in the water at that time. And that is actionable for the agency and they can think deeply about that. And overlay that, you know, they're terrain maps and all of the unique things that are counties. Exactly. This is all fed by a bunch of, like, they obviously have to have incidents, information, neither one calls, they have to have weather information. All that information is integrated and then kind of at, at the disposal of the AI to help the customer. Yeah. And we just can't, I mean, there's no way we could have made this up, right? It's like we, if we were sitting in our headquarters, trying to pontificate. Yeah. And what, you know, emergency responders in a hurricane needed, like there's zero chance we would have figured that out. Super interesting. I mean, another one that I'm like excited about, you know, there was a detective who was investigating a threat against a synagogue and the, what he was trying to do was trying to figure out whether any other anti-Semitic threats that had happened to these two synagogues in the area. Now when you think about that question, and you're a detective, what are the keywords that you search for to try and find that information? It's actually very difficult for keywords to find that. And what gets me excited, this is another area that is just previously impossible. And with the help of, yeah, they're able to semantically understand a lot of the information all that they have access to and they're able to actually pull out and they were able to find a bunch of pattern of these threats that were occurring against these synagogues. And I thought, you know, that was, that was pretty interesting to me and a novel use case, yeah. Interesting. Okay. So you got semantic search and embeddings going on. You got, um, sounds like reasoning models that uncovered the use case. Um, so one of the most interesting thing is, is because you've brought in all this context and you've stitched it together, you can kind of let the AI do its thing. Only 95% of the work is what happens before the user types in the question. What is all the preparation that you do to get to a place where the AI can answer accurately, cite those questions accurately. That's a really hard problem. We spend a lot of points on that. We have a lot of agentic use cases and I, you know, are a lot of our engineering points are spent on that data preparation, getting that, that AI ready. There's also this idea that AI allows you to write software rapidly and what happens when the cost of generating the software is virtually zero. And what if we could apply, uh, uh, provide the platform for a deployment team that gives them the security and government's controls, that gives them the APIs they need. And then they're allowed to write the world with software. And so what we're seeing is that these deployment strategists, you know, not every customer needs a, a, an agent. Maybe they need a hurricane simulator. And the way they're able to write this really well and is with, is with AI. And I think this rapid innovation cycle is just super interesting. Yeah. So cool. Is it your deployment strategist that are kind of coding things in customer environments? Or do you think your customers might even, you know, they, they have all these ideas in their head of what they, they want. Can they go from idea to working application themselves in your environment? 100%. We enable that. I think the, you know, what we see most often is a partnership with our deployment team to, to provide that kind of technical expertise and be able to take the idea and bring it to fruition. We do have some customers that are able to, to kind of leverage the platform in that way, but a lot of that motion comes with our, with our deployment team. Do you think it'll change over the coming years? I see it changing right now. I mean, I love to get your thoughts on the, on the spend. We, I, I think it takes confidence to embrace that and say, you know what? Like, let's, let's innovate and, you know, may, may the best method win so much of what Paragrand does and it on the technology side is emergent. Paragrand has been providing domain expertise and for our customers, so we deeply understand and empathize with law enforcement, fire departments, EMTs. And then we provide technology to enable them to achieve their most important missions. And that technology can change over time and that's like the great thing about it. Most of that stuff is downstream of really high quality data. And you can build charts and that chart could be completely useless if it's not accessing the right information and accurate information. The same is true with agents and the same will be true with the next technology. And so I, I believe like the core strategy of Paragrand has remained very unchanged in, in this new age of, of AI and it's just we are applying a new technology to see how it impacts our customers. Are you doing anything on the long horizon agent side or background agents? >> Yeah, absolutely. A couple of things here. So I think stepping back a little bit, what is the Paragon platform? We think about 50% of our engineers spend most of their time working on the data platform that enables our deployment team to integrate data that the customer already owns. And we've, at this point, integrated tens of thousands of data sets across all these customers. And we have built an agent that enables our deployment strategy team to integrate this data agentically. And I think at this point, we have this amazing Eval set where we can, you know, deterministically evaluate these agents for completeness and correctness on integrations. At this point, we're seeing about 90% of our, we use a version of Python notebooks to do a lot of our integrations. About 90% of that is written by agents with the oversight of our, of our deployment team. And these agents run for hours, right? And they will, they will, you know, analyze, look at the databases, understand the ontology, start to piece together, different pieces that need to be integrated, those split off into sub agents that all do a bunch of work, communicating back to kind of the orchestrator agent. I have. That's one bit on kind of long horizons. Yeah. Reminds me of how people are using these coding agents for like code-based migrations, also similarly just long running unglamorous work. 100%. And I love this problem because it's, it's verifiable. And that makes the problem a lot easier. This is why coding agents are in a lot of ways a lot easier. And so the second piece that we spend a lot of time on is, what do our agents look like for operational outcomes for our end users? And the very first agent that we built was a cold case agent. And this agent, you know, just give you some context on what some of these very high profile investigations look and feel like. You are uploading 200 to 300 gigabytes of data. These are videos, they are audio, it is images, it is a ton of PDF files and the detective is tasked with going through all that. So that in and of itself takes a really, really long time. Ben and these police departments where they actually have paper records of all these. And they are boxes that are like this big. And you know, they have all these CDs attached to it. It's really an outrageous amount of data. And so our cold case agent, we first did, we built this with customer. And again, like this comes back to like how we like to, how we like to build. And we were working with a customer who had worked a case where a man was wrongly convicted. And they were able to exonerate this individual. And they said, hey, can you reproduce this result with an agent? And so we took all of the evidence and data that they had on that case and started to work on an agent that would run for 30 minutes, 60 minutes to start to clean insights. And eventually we got to the place where it could reproduce the results that those detectives had gotten to. So that was where that was like kind of the manifestation of our first kind of cold case agent. Now we're using this in a few departments across the US. We recently were working in a county in Wisconsin where this, again, similar type of case, 300 gigabytes of data, and they were able to identify and place the suspect at not just the scene of the crime, but where the body was found. And this was a single, there's about a few cell records, cell call detail records, kind of ping of a phone. That was scattered amongst a lot of data. And so this was really interesting to me. And I think points to this area where we could really up-level and help our public servants unburden them with all this administrative of watching hours and hours and hours of video and listening to hours and hours of audio. As a quick comment, this example, Benbroke, is so impactful to the organization as a culture for our business. The way that we maintain trust is not actually taking credit and shouting from the rooftops about the awesomeness of what happened here. I think that is one of the fastest ways to break trust, frankly, with these organizations. The idea that we're going to scoop up the work and trumpet our skill and the way that we impacted the world. So I think being the quiet professionals in a context like this and empowering the customer is why we have access to the next problem. I wish you would talk about it more, though, because I think that we're in this moment where public distrust in the AI is so high. And this is a wonderful story, right? I think that coming back to an earlier example of trying to identify threats to a synagogue, I think about network effects. And I think about what motivates us to tell our story. I think the convergence of this idea of a centralized authoritarian like AI capability and surveillance in American or national society, the convergence of these forces has created this immense pressure and I think distrust in many of the organizations and frankly the communities that we work with. That can't be ignored, first of all. And I think if we look at the organizations we work with and also the data landscape, the network effects are there and they are unbelievably strong. Like for example, it would be very easy for an organization that's trying to kind of hack their way to building something useful, to go and hit open source and pull in data that they might not, there might be a gray zone on whether they can or should access that based on laws, regulations, ordinances, standard operating procedures of the department. And for us, we have to protect against those moments where we don't need to break the rules nor should obviously should any organization break the rules in order to find a way to introduce AI or technology into these complex environments. The opposite is true. We have to protect the data, right? And we have to protect these organizations that by virtue of their structure have astounding network effects, right? It's almost like the anti network effect proposition. Like how do you preserve the sanctity of the data and the ownership of the data and the ways of working for every individual agency, every individual organization? And then building the connective tissue, building the interoperability is going to happen. But and this is where things that might sometimes seem mundane, but we find really fascinating. Like how do you think through data governance? How do you think through permissioning logic in the context of AI become unbelievably important? So I feel really motivated to talk about that in particular. - Paragrand the anti network effects business. I think it's important, especially given the heat now around AI is so powerful. And so I love this data that used to exist. I think people now fear what happens when it's all swooped up into a central panopticon. That's deeply un-American. - Yeah, yeah, yeah, yeah. And I think that as a business, it's like I find that our business is a big practice and letting go. It's how do we build high integrity transparent solutions for our customers? How do we make that transparent to our customers and their constituents and then how do we effectively let go and not try to grow too fast? - Are there technology decisions that you have to make that are morally nuanced? So for example, facial recognition, I'm curious, your stance on that. And then more generally, what is your North Star for hard decisions? - Yeah, quickly on facial recognition. I think the idea of a Silicon Valley company imposing a decision that is kind of general purpose for an industry is fundamentally wrong. I think the idea that we as an institution would assert whether it be the utilization of a technology or a retention policy, I mean, it's very much not how we think. Instead, the idea is to help a customer understand the context in which they're operating and actually bring to light all of the considerations they may or may not know and then help them. And this requires patience. Help usher in the right way of doing business, the right way of thinking about deploying these technologies on facial rec most public safety agencies in America and their communities choose not to implement facial recognition. Some take a very strong stand and some are a, it's just a matter of subjective preference that they'll assert. but the idea of creating a technological red line without understanding the texture and context is not a boundary line that we think that we can assert on top of our customer. - So follow the customer, follow the law. - Yeah, and expose it and create clarity around what left-right limits and what the institutional norms may be, I mean, it's astounding how cities call each other or they call each other for advice. And so if you really just listen to the way that people learn inside of the industries that we serve, what we realize is helping them streamline the way that they can get the best possible information so they can make their best decision, even about things like what technologies to use or not goes a really long way. - Maybe that's similar vein. I wanna talk about delivering technology to the underdogs and how you're able to scale this very deep customer motion into a very different customer base than the other company that notoriously has scaled this motion balance here, which notoriously doesn't take anything plus than eight bigger contracts. Did your mentors try to convince you not to do this? Did people tell you the economics of this are not gonna scale and what made you think it was gonna be possible to deliver technology to the underdogs and serve them in this way? - I think it takes confidence. I think it takes confidence to believe that if you build something well, you can solve problems that have never been solved before they will scale later. And the idea of going steep to build a vertically integrated tech stack, that everything from network access to the permissioning and the governance and the ETL and the pipeline and the ontology and the UX and the APIs, it's a to configure all of that and create a system that's open interoperable and can check these boxes. It, the audacity, I think, is real. And to build each of those components in a way that's first class, not just to check the box, I mean, we were truly thinking about how to deliver these types of technology solutions to an end market that has never been able to use these solutions before, let alone at the price point that we were able to deliver them. - Yeah. - I think the uniqueness about Paragran is, for each organization, we'll take the time to deliver solutions all the way to the outcome and then we'll own the responsibility with our customers of getting to that outcome, which might seem extremely manual, extremely laborious, extremely unstable or unscalable. And what we found is being able to do that, resulted in a technology platform that could show up and do something very different for these organizations and also scale. - I've had the chance to talk to a few deployment strategists at Paragran and they are, they're amazing. They're so smart, so humble. They so deeply embody the mission. And so, I mean, you guys have selected for and trained a really extraordinary group of people. How do you do it? What do you look for, what do you interview for? How do you train these people up? - First of all, I mean, it's the greatest compliment because I and we love our team. When you can build a culture where people are trying really hard and can achieve excellence, it's almost like how could you not create the environmental factors and the feedback culture, which sometimes isn't easy, right? Sometimes it can be really intense. Ben and I would say, you know, we've aligned, we've taken tests and we've aligned on some character qualities, which is definitely the pursuit of excellence. I think a lot about these environmental factors of like trusting the individual contributor. It's a very important thing that we're trying to hold onto and to do that with a forward to pull out engineer actually to truly do it really requires trust because we use this analogy of a dark cave, right? We'll send a person into a cave and they have a tool belt. And hopefully the tools are pretty good, right? And there might be a rope, you know, with a friend at the entrance of the cave that if they yell for help, you can kind of pull them out. But truly, we are sending people into these zones and we're saying maintain your integrity, like maintain your principles. Here are the tools, go do good. And to do that at scale, I really believe requires institutional trust and a belief system that innovation actually happens at the farthest fringes of our organization that the way that our engineering product design organizations are fed is through that innovation process across the country and now across multiple countries with our forward to play team. - Yeah. - It's fascinating. It's so different when I talk to companies who view forward to play as a cost center. You guys are, it's fundamentally very different talking to you. - Definitely R&D, definitely R&D and growth. I mean, they're after we land inside of a customer base, we have these models where we'll do these within customer pilots for all intents and purposes where we'll keep sprinting on additional use cases. And in many ways, how we land and then how we expand in a customer base is fundamentally about leading from the front through our forward to play engineers. Have you discovered any new products through this motion? - 100%, I mean 100%. It's just the whole thing. Like, I think it's hard to even separate that. I mean, I remember walking, or walking, driving across the Bay Bridge, San Pablo, Typing on the computer and it's like, okay, we gotta get these reports done and like, this is, and this is what this detective needs and literally typing as fast as I could to try and code what we needed at the time. And it wasn't theoretical. It was Aaron Blaistel, who had an extraordinarily urgent request. Yeah, and like, I feel it. And so it's so hard to separate very rarely. Are we in a room pontificating about, you know, what to build? I really think about the FD motion and how it impacts products, our product in two ways. One is, what are the primitives, basic primitives that the technology platform needs? And there's a couple of examples of this that I think are fascinating and reflect the ingenuity of our forward deployed team. The longest time in Paragon, we had no ability to edit specific fields and properties. And so one forward deployed engineer to get around that, made an integration based on the comments people would write on these objects. And that comment would be read by Paragon and then update a property. And that was the way they implemented editing. And so we saw that and we're like, we can implement editing. And by the way, the reason that teammate did that is because our intent was, and this is the way that the coaching and leading happens, it's like, your job is to hit the objective. Like to hell with the technology. Yeah. I mean, our job is to build the technologies and the tools that empower you to do higher and higher levels of work. But your job is to achieve. Yeah. And that's what creates these crazy things that might look very unskilled, very hacky, but ultimately are the absolute best signaling network. It's great signal for what actually works. And it's like the quintessential rapid prototyping. So that's a category one. You get these product primitives that you know that you need to provide the team in order to achieve the objective they're trying to achieve. I think like category two is almost what I call like innovation lab where the deployment strategist goes and builds something totally unique that we're probably not going to integrate back into the product just because it's such a unique capability specifically for that customer. And this stuff gets me really excited. And I love watching and seeing what our deployment team builds. I was just looking at one the other day where someone had built a hurricane simulator in paragraph. And I was like, how did you do that? I didn't know you could do that. And so that was really interesting. There was another deployment strategist who built this thing where you could integrate it a bunch of data from all these different sources. 911 call times like budgets. And you could place a fire department in the city. And it would give you an estimate on how many people that would impact. Unbelievable. And she was at the company for a month. Maybe last last other questions, 10 years from now, suppose everything's gone right. Paragran is the institutional memory layer for 10,000 cities. I think that's great power that outlasts anyone administration. And with great power comes great responsibility. How do you think about that? I think there's the outcome. And then I think there's the path to get there. The endgame of 10,000 cities, I think, requires an operating model that is fundamentally about infrastructure, actually. It's like we as a technology organization are delivering technology infrastructure that empower these organizations to do with their data as they would like and as they are required to do. And every city, every jurisdiction, I deeply believe is unique. And I think the idea of preserving that uniqueness is actually beautiful. And so I'm really-- excited about that. How do you make each, you know, the popery of cities make each one more awesome and they're a unique way. That's cool. The path to get there requires serious integrity, moral compass, core values, not just in words, but in our ways of working, our actions, and that never goes away. It never goes away. The way that we think about the next marginal customer that we support requires a level of handholding and delicacy that candidly, I don't think was possible even five years ago because the marginal cost of doing what we do to drop it below a million bucks a year. I mean, it's radical, right? Coming back to the origin stories. In many ways, you know, it's supporting state, county, and city level public safety was, you know, there are lineage of failed business units of major astounding organizations that tried to do it and failed because to deliver these solutions in a way that's tailored at a price point that these organizations can afford was never possible. And so to drop the to create the efficiency to drop the price by an order orders of magnitude is has earned us the right to try to deliver on what you just said. It's like, who are we to think that we hold any power over that? I mean, it's the institution that has the power. Go make every city awesome. I mean, you're in a very, very serious seat. And just in the course of this conversation, it's clear that you're approaching this with with great care and great stewardship. So thank you for what you do. I think the world needs more examples of AI being used to improve lives, to improve communities. And thank you for what you're doing. And thank you for joining the pod today. Thank you, Sonia. [Music]