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

Introduction

Ben Rudolph: Peregrine 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?

Nick Noone:  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. There were all these contrasting — but actually similar — concepts 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 the backbone infrastructure level of the modern city, we could help to preserve individuality — when we talk about privacy and other aspects of what it means to be an individual person — but also bring people together.

Main Conversation

Sonya Huang: One of the most important stories in AI is from a company that you may not have heard of, but that is almost certainly keeping you and your loved ones safe. Peregrine is building AI that protects cities and communities — and they are rejecting the surveillance state while doing it. This should be a fantastic conversation about one of the most important applications of AI, forward deployed 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 intelligence operations in the Middle East. Tell me: what does forward deployed engineering actually mean, and what does Silicon Valley get wrong about it?

Nick Noone: 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 deployed engineering meant. But I was surrounded by people who were running into customer environments and doing who knows what.

Sonya Huang: Yeah.

Nick Noone: I watched them leave the office, and I watched them come back to the office and tell stories. It 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.

There are these two truths that I learned as an early forward deployed engineer, and that we've tried to embrace as we built our company. First: 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, and all 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 or team could do it. Everything flows from there. And second: recognizing that at the end of the day, it's the customer's win. It's not our win. Taking deep pride in the impact we have on the institution, and on the people sitting across the table from us — that's where the magic is.

Sonya Huang: Culturally, what does Silicon Valley get wrong about it?

Nick Noone: When we first started building Peregrine, the forward deployed engineering concept had been around for a while. And we got feedback that when really smart people from top-five, top-ten or equivalent schools 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? — 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. I think Silicon Valley misses that sometimes. Letting go of our intelligence, letting go of our own skills and abilities, trying to suspend our ego and 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.

Sonya Huang: Yeah, I love that. Ben — while Nick was off deploying into the 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 about the world and technology's role in it. Maybe say a word on that.

Ben Rudolph: Yeah, absolutely. For me, I've always been obsessed with this idea of how you make impact with technology. I graduated school, and I remember I had these two offers: one was to go work at a small startup at the time — Airbnb — and the other was the UN Refugee Agency, an organization that helps refugees who are making these extremely dangerous treks across country lines, usually because of war, to try to get help. And so I took that path, and deployed to the Sudanese border, the Colombian border, and was deeply humbled. I came to deeply understand these types of situations — where technology can help, and honestly, where it can't.

The thing I took away from that experience: UNHCR is this deeply disconnected organization. It does phenomenal work, but it's deeply disconnected. They have some data; it's often in spreadsheets, and it's very difficult to actually make sense of. That's where I started to form this thesis that a lot of these problems are downstream of data problems, where you can have a lot of impact.

After UNHCR, I left to join an organization you probably haven't heard of called Dimagi. They're a small company that builds last-mile healthcare solutions in under-resourced places around the world. One specific project I worked on there: we were working with the Indian government, building applications to help folks in rural India adhere to their tuberculosis drugs. At the end of the day, we got it deployed, and that technology rapidly spread throughout the country. That was a really cool moment for me, and it really formed my thesis around how Peregrine can have impact — how technology can have impact in the communities that we live in. Peregrine 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?

Sonya Huang: Take us back to 2017. You cold-called your way into the San Pablo Police Department. What was going through your heads when you did that?

Nick Noone: In 2016, Ben and I got together — actually on a vacation with a bunch of our gymnastics teammates — and we had a conversation, basically ten years after we graduated, saying: should we build something together? We got together in San Francisco, with my experience in the national security space and the US government, and Ben's experience working on humanitarian causes. 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 — the city — is the place where people live. This is the place where people thrive. This is where the majority of our lives are spent.

Then we started thinking about how to deconstruct working with American cities. And what we came to is that at the bottom of the pyramid, when it comes to how to make cities awesome, is the idea of safety: that people need objective safety and also need to feel safe. When that stability is there, it's amazing what's possible. There were all these contrasting — but actually similar — concepts we were wrestling with at the time. We were thinking about how to deploy inside of institutions, and 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 the backbone infrastructure level of the modern city, we could help preserve individuality — privacy, and other aspects of what it means to be an individual person — but also bring people together. The idea that we could do both of those things: preserve and protect individual privacy, while also bringing people together and helping them realize that if we're staring at the same information, we kind of want the same thing. That's what I find really beautiful about cities. And I think we're very aligned on that.

Ben Rudolph: Totally.

Sonya Huang: Beautifully said. How many people said no before San Pablo said yes?

Nick Noone: 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 a month or so earlier, but February 26th is the day 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.

The reason we got there is that we started to 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 Bubar, who had been catapulted up the ranks inside a paramilitary organization that's very structured, that takes years and years to reach higher levels of leadership. He was young and intelligent and creative. There's this article we found online about Operation Red Reach, which was one of the most iconic cross-jurisdictional, gang-affiliated narcotics investigations that happened in Northern California at the time. Brian was a covert operator trying to figure out how to move this stuck investigation forward. And I thought it took so much courage for someone like Brian to come in and be the reason — and it obviously wasn't just Brian; so many people surrounded that work — and do something so impactful. It made headlines, but more importantly, it 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. We led with that level of trust, and that was what finally got us access.

Sonya Huang: He took a chance on you.

Nick Noone: Yeah. Truly.

Sonya Huang: Did you do ride-alongs?

Nick Noone: We have amazing photos from those days.

Ben Rudolph: Our first ride-along was actually with Oakland PD, back when we were still in this deep research phase. That was a really eye-opening and interesting experience.

Nick Noone: Overnight, I think.

Ben Rudolph: Yeah — in a suit.

Nick Noone: And tie. Overnight. And it was also eye-opening because most of the people coming from Silicon Valley to go on ride-alongs at the time were not engineers — they were reporters, or other types of community interest organizations. So I think we got the white-glove service at the time. And it really was eye-opening how difficult it is to penetrate and build trust with these organizations. There were so many lessons in that period that signaled to us that we just had to take our time.

Sonya Huang: You started this company in 2017, 2018. I think "defund the police" 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?

Nick Noone: I'll share, on a personal level: I tend to be a very experience-based learner. Very tactile. I want to hear the concepts, but build a bottoms-up understanding through action. And I've found in my life that that mentality fundamentally leads me to question assumptions — not in a low-trust, skeptical way at all times; I think there's a danger of becoming overly cynical — but to really understand truth. A curious adventure. I think that's the psychological backing that led us to build our business in the midst of a really tumultuous time in America.

I have a personal affinity for working with people who sometimes feel misunderstood, frankly. It's a very intuitive passion for me to help them feel more understood, and I think many people at our company have character qualities like that. When I think about moments like COVID, and the protests happening at the same time — the convergence of social unease in America — we were holding a lot of complexity. We had very close friends, people we'd graduated with and built things with in the past, who were standing outside the Women's 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. We were holding those two truths at the same time.

To me, it's not enough to look at a very difficult situation from the outside. The privilege is jumping into the pool and swimming. Through that process, we establish empathy. We realize: oh, this thing we thought was wrong is actually really complicated. There are a lot of gray zones. The deeper we go, the more nuanced and textured the problem actually is. How empowering is that? And so, even though in that period — our business is much more diverse now; the through-line is delivering safety and prosperity to cities, and now it's police, fire, emergency management, health services, all kinds of things, but at the time we were only working with police departments — engineers wouldn't even respond when we made a recruiting call. It was a fascinating time. I think we've seen the pendulum shift, and we're trying to deliver solutions that are apolitical: what people, communities, and the institutions that serve them truly want and need. In many ways, I don't think that's controversial at all, if done well.

Sonya Huang: How is your business structurally different from the data collection companies, like a Flock or an Axon?

Nick Noone: We've been operating in public safety since 2018, and I think most of the companies that came before us built their business fundamentally on the back of data collection: the idea that a hardware or software solution is installed inside a customer base and is fundamentally about the input, whether it's passive collection through a sensor or human beings inputting information directly. Historically, those companies have grown because once they have information that belongs to the customer inside their system, plus a distribution advantage, they can sell more stuff — and the additional stuff tends to be more collection systems.

Peregrine is the inversion of that entire model. We are fundamentally in the business of joining disparate information to provide a more secure, properly governed solution that sits on top of the pre-existing systems — that helps people do better work, and get more precision and accuracy in the answers to their questions. Being not in the business of bringing more data to a customer, but in the business of building solutions that drive greater precision in how a human being interacts with their data — it's flipping the historical model on its head, so to speak.

Sonya Huang: Okay. So structurally, the business incentive is almost the opposite. It's not about maximizing the amount of data you're collecting and creating network effects on that data.

Nick Noone: I think the world is very concerned about the amalgamation of data — this central, whether public sector or private sector, authoritarian body that has access to this privileged information, and what will they do with it? For us, we almost want to decentralize that: to get to a world where we are 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 they have — or utilize it in a way that's secure and high-trust for the communities they serve.

Ben Rudolph: I'll just say, from day one we've been really focused on this idea of permission controls, data governance, sovereignty. These are necessary ingredients to deploy to these types of high-stakes institutions. And Nick is right when he talks about trust. We think a lot about trust — trust with the community, because these public servants are ultimately serving the community members, and trust with those public servants themselves — and we have to uphold both. So we think a lot about how we build what we build, and how it interacts with the end user.

Sonya Huang: Yeah. It seems like the data governance — the data being so locked down — is almost the way you reconcile the tension between public safety and not becoming the surveillance state. That is the answer: local intelligence, local data, everything locked down. Maybe share a bit about your philosophy on data ownership and data sharing.

Ben Rudolph: We deeply believe in the idea that each customer, each institution, owns their own data. That organization is serving the community: it is the community's data, it is the organization's data. It's not Peregrine's data. We think about providing the controls and capabilities that allow them to very securely roll this out within their own department, but also share select pieces of information when they need to. Without these types of solutions, folks will put a bunch of data in the back of a car and drive it across the city — and there's actually less control there. So we think a lot about where we can enable the outcome for our user, and provide controls that are specific and robust, so they don't overshare, and they can trust what they're delivering.

Sonya Huang: What are the biggest use cases you see for your customer base? What are people starting to play with now, and where do you see it going?

Ben Rudolph: This is what excites me the most.

Sonya Huang: Me too.

Ben Rudolph: When you initially deploy this type of AI to these organizations, you often just get what I'd call nice search. Maybe you were looking for an address, and now you don't have to look at a bunch of rows — you see everything that happened at that address, in a nicely formatted way. As users get more used to the product and understand how it works, you start to see the floor get raised for everybody across the department, and you start seeing these really interesting, deep types of analysis that previously were just impossible.

I'll give a couple of examples, because this stuff is really interesting to me. We're working with a county in Florida, and the other month they had to do over 100 water rescues. And they asked: why? We've never had to do this many water rescues before.

Nick Noone: A water rescue is — when there's a flood, rescuers need to get on a boat and literally go out and rescue someone from their car, which might be stalled in the middle of the flood, or from their home.

Ben Rudolph: So they started asking Peregrine, interrogating this question. And what they started to uncover — these weather patterns had happened before, but after a couple of iterations, and the agent doing some deep research, out came the pattern: these weather patterns had never occurred for three consecutive days. And those types of weather patterns 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. That is actionable for the agency — they can think deeply about that.

Nick Noone: And overlay their terrain maps, and all of the unique things that are happening.

Ben Rudolph: This is all fed by — they obviously have incident information, 911 calls, weather information. All of that is integrated, and at the disposal of the AI to help the customer.

Nick Noone: And we just couldn't have made this up. If we were sitting in our headquarters trying to pontificate on what emergency responders in a hurricane needed, there's zero chance we would have figured that out.

Ben Rudolph: Super interesting. Another one I'm excited about: there was a detective investigating a threat against a synagogue, and what he was trying to do was figure out whether there had been any other antisemitic threats against these two synagogues in the area. When you think about that question as a detective — what are the keywords you search for to find that information? It's actually very difficult for keywords to find it. What gets me excited — and this is another thing that was previously impossible — is that with the help of AI, they're able to semantically understand all the information they have access to. And they were able to pull out a pattern of these threats that were occurring against these synagogues. That was pretty interesting to me, and a novel use case.

Sonya Huang: Interesting. Okay, so you've got semantic search and embeddings going on; it sounds like reasoning models that uncovered the water rescue use case. One of the most interesting things is that because you've brought in all this context and stitched it together, you can kind of let the AI do its thing.

Ben Rudolph: Certainly 95% of the work is what happens before the user types in the question: all the preparation you do to get to a place where the AI can answer accurately, [cite] accurately. That's a really hard problem. We spend a lot of our engineering effort on that data preparation — getting the data AI-ready — and we have a lot of agentic use cases.

There's also this idea that AI allows you to write software rapidly. What happens when the cost of generating software is virtually zero? What if we could provide the platform for our deployment team that gives them the security and governance controls, gives them the APIs they need, and then they're allowed to write the world with software? What we're seeing is that these deployment strategists — you know, not every customer needs an agent; maybe they need a hurricane simulator — are able to build these things really well, with AI. This rapid innovation cycle is just super interesting to me.

Sonya Huang: So cool. Is it your deployment strategists who are coding things in customer environments, or do you think your customers — who have all these ideas in their heads of what they want — can go from idea to working application themselves, in your environment?

Ben Rudolph: A hundred percent, we enable that. What we see most often is a partnership with our deployment team, to provide that technical expertise and take the idea to fruition. We do have some customers who are able to leverage the platform in that way, but a lot of that motion comes with our deployment team.

Sonya Huang: Do you think that'll change over the coming years?

Nick Noone: I see it changing right now. I'd love to get your thoughts on this, actually. I think it takes confidence to embrace it and say: you know what, let's innovate, and may the best method win.

Ben Rudolph: So much of what Peregrine does and is, on the technology side, is emergent. Peregrine has been providing domain expertise — 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. That technology can change over time, and that's the great thing about it. Most of it is downstream of really high-quality data: you can build charts, and that chart is completely useless if it's not accessing the right, accurate information. The same is true with agents, and the same will be true with the next technology. So I believe the core strategy of Peregrine has remained very unchanged in this new age of AI. We're just applying a new technology to see how it impacts our customers.

Sonya Huang: Are you doing anything on the long-horizon agent side, or background agents?

Ben Rudolph: Yeah, absolutely. A couple of things here. Stepping back a little: what is the Peregrine platform? About 50% of our engineers spend most of their time working on the data platform that enables our deployment team to integrate data the customer already owns. At this point we've integrated tens of thousands of datasets across all these customers. And we've built an agent that enables our deployment strategist team to integrate this data agentically. We have this amazing eval set where we can deterministically evaluate these agents for completeness and correctness on integrations. At this point — 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 deployment team. And these agents run for hours. They'll analyze, look at the databases, understand the ontology, start to piece together the different pieces that need to be integrated. Those split off into subagents that all do a bunch of work, communicating back to the orchestrator agent. So that's one bit on long-horizon agents.

Sonya Huang: It reminds me of how people are using coding agents for codebase migrations. Similarly: long-running, unglamorous work.

Ben Rudolph: A hundred percent. I love this problem because 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.

The second piece we spend a lot of time on is: what do our agents look like for operational outcomes, for our end users? The very first agent we built was a cold case agent. To give you some context on what some of these very high-profile investigations look and feel like: you're uploading 200 to 300 gigabytes of data. Videos, audio, images, a ton of PDF files. And the detective is tasked with going through all of that, which in and of itself takes a really, really long time. I've been in police departments where they have paper records of these cases in boxes that are this big, with CDs attached. It's an outrageous amount of data.

Our cold case agent — we first built this with a customer, and again, this goes back to how we like to build. We were working with a customer who had worked a case where a man was wrongly convicted, and they were able to exonerate that individual. They said: hey, can you reproduce this result with an agent? So we took all the evidence and data they had on that case and started to work on an agent that would run for 30, 60 minutes and start to glean insights. Eventually we got to the place where it could reproduce the results those detectives had gotten to. That was the manifestation of our first cold case agent, and now we're using it in a few departments across the US. We were recently working in a county in Wisconsin — again, a similar type of case, 300 gigabytes of data — and they were able to identify and place the suspect not just at the scene of the crime, but where the body was found. And this was from a few cell call detail records — the ping of a phone — scattered among a lot of data. That was really interesting to me, and I think it points to this area where we can really up-level our public servants: unburden them from the administrative work of watching hours and hours of video and listening to hours and hours of audio.

Nick Noone: A quick comment: this example Ben brought up was so impactful to the organization, as a culture, for our business. The way we maintain trust is by not taking credit — not shouting from the rooftops about the awesomeness of what happened here. I think that's one of the fastest ways to break trust with these organizations: the idea that we'd scoop up the work and trumpet our skill and the way we impacted the world. Being the quiet professionals in a context like this, and empowering the customer, is why we get access to the next problem.

Sonya Huang: I wish you would talk about it more, though. We're in this moment where public distrust in AI is so high, and this is a wonderful story.

Nick Noone: Coming back to the 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. The convergence of this idea of a centralized, authoritarian AI capability and surveillance in American society — that convergence of forces has created immense pressure, and I think distrust, in many of the organizations and, frankly, the communities we work with. That can't be ignored.

And if we look at the organizations we work with, and the data landscape, the network effects are there, and they are unbelievably strong. For example: it would be very easy for an organization that's trying to hack its way to building something useful to go hit open source and pull in data where there might be a gray zone on whether they can or should access it, based on laws, regulations, ordinances, or the standard operating procedures of the department. For us, we have to protect against those moments. We don't need to break the rules — nor, obviously, should any organization break the rules — to introduce AI or technology into these complex environments. The opposite is true: we have to protect the data, and protect these organizations that, by virtue of their structure, have astounding network effects. It's almost the anti-network-effect proposition: how do you preserve the sanctity of the data, the ownership of the data, and the ways of working for every individual agency and organization — and then build the connective tissue? The interoperability is going to happen. And this is where things that might seem mundane, but that we find really fascinating — how you think through data governance, how you think through permissioning logic in the context of AI — become unbelievably important. I feel really motivated to talk about that in particular.

Sonya Huang: Peregrine: the anti-network-effects business. I think it's important, especially given the heat right now around how powerful AI is. A lot of this data used to just exist — and I think people now fear what happens when it's all swept up into a central panopticon. That's deeply un-American.

Nick Noone: Yeah. And I think, as a business — I find that our business is a big practice in letting go. How do we build high-integrity, transparent solutions for our customers? How do we make that translate to our customers and their constituents? And then how do we effectively let go, and not try to grow too fast?

Sonya Huang: Are there technology decisions you have to make that are morally nuanced? For example, facial recognition — I'm curious about your stance on that. And more generally: what is your North Star for hard decisions?

Nick Noone: Quickly, on facial recognition: I think the idea of a Silicon Valley company imposing a general-purpose decision on an industry is fundamentally wrong. The idea that we as an institution would assert, whether it's the utilization of a technology or a retention policy — it's very much not how we think. Instead, the idea is to help a customer understand the context in which they're operating, bring to light all the considerations they may or may not know, and then — and this requires patience — help usher in the right way of doing business, the right way of thinking about deploying these technologies. On facial recognition specifically: most public safety agencies in America, and their communities, choose not to implement it. Some take a very strong stand; for some it's a matter of subjective preference that they'll assert. But creating a technological red line without understanding the texture and context is not a boundary we think we can assert on top of our customer.

Sonya Huang: So: follow the customer, follow the law.

Nick Noone: Yeah — and expose it, and create clarity around what the left and right limits are, and what the institutional norms may be. It's astounding how cities call each other. They call each other for advice. If you really listen to the way people learn inside the industries we serve, what we've realized is that helping them streamline the way they get the best possible information — so they can make their best decision, even about things like which technologies to use or not — goes a really long way.

Sonya Huang: In a similar vein, I want to 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, you know, the other company that has notoriously scaled this motion — Palantir — which notoriously doesn't take anything less than eight-figure contracts. Did your mentors try to convince you not to do this? Did people tell you the economics of this were not going to scale? What made you think it would be possible to deliver technology to the underdogs and serve them this way?

Nick Noone: I think it takes confidence. Confidence to believe that if you build something well, you can solve problems that have never been solved before — and that it will scale later. The idea of going deep to build a vertically integrated tech stack — everything from network access to the permissioning, the governance, the ETL, the pipeline, the ontology, the UX, the APIs — configuring all of that and creating a system that's open, interoperable, and can check those boxes... the audacity, I think, is real. And building each of those components in a way that's first-class, not just to check the box. We were truly thinking about how to deliver these types of technology solutions to an end market that has never been able to use them before — let alone at the price point we were able to deliver them at. The uniqueness of Peregrine is that for each organization, we'll take the time to deliver solutions all the way to the outcome, and we'll own the responsibility, with our customers, of getting to that outcome. That might seem extremely manual, extremely laborious, extremely unscalable. And what we found is that being able to do that resulted in a technology platform that could show up, do something very different for these organizations — and also scale.

Sonya Huang: I've had the chance to talk to a few deployment strategists at Peregrine, and they're amazing. They're so smart, so humble, and they so deeply embody the mission. I think you've 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?

Nick Noone: First of all, that's the greatest compliment, because we love our team. When you can build a culture where people are trying really hard and can achieve excellence — how could you not create the environmental factors, and the feedback culture, which sometimes isn't easy? Sometimes it can be really intense. Ben and I have aligned — we've taken tests and aligned on some character qualities — and one of them is definitely the pursuit of excellence.

I think a lot about the environmental factors of trusting the individual contributor. It's a very important thing we're trying to hold on to. To do that with a forward deployed engineer — to truly do it — really requires trust. We use this analogy of a dark cave. We'll send a person into a cave, and they have a tool belt, and hopefully the tools are pretty good. And there might be a rope, with a friend at the entrance of the cave, so if they yell for help, you can pull them out. But truly, we are sending people into these zones and saying: maintain your integrity, maintain your principles, here are the tools — go do good. 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 our engineering, product, and design organizations are fed is absolutely through that innovation process across the country, and now across multiple countries, with our forward deployed team.

Sonya Huang: It's fascinating. So different from when I talk to companies who view forward deployed as a cost center. It's fundamentally different talking to you.

Nick Noone: Definitely R&D — definitely R&D and growth. After we land inside a customer base, we have these models where we'll do within-customer pilots, for all intents and purposes, where we keep sprinting on additional use cases. In many ways, how we land and then expand in a customer base is fundamentally about leading from the front through our forward deployed engineers.

Sonya Huang: Have you discovered any new products through this motion?

Ben Rudolph: A hundred percent. It's just the whole thing — it's hard to even separate it. I remember driving across the Bay Bridge to San Pablo, typing on the computer: okay, we've got to get these reports done, this is what this detective needs — literally typing as fast as I could to code what we needed at the time.

Nick Noone: And it wasn't theoretical. It was Aaron Blaisdell who had an extraordinarily urgent request.

Ben Rudolph: Yeah — you feel it. So it's hard to separate. Very rarely are we in a room pontificating about what to build. I really think about the FDE motion and how it impacts our product in two ways. One is: what are the basic primitives the technology platform needs? There are a couple of examples of this that I think are fascinating, and reflect the engineering ingenuity of our forward deployed team. For the longest time, Peregrine had no ability to edit specific fields and properties. So one forward deployed engineer, to get around that, made an integration based on the comments people would write on these objects: the comment would be read by Peregrine and would then update a property. That was the way they implemented editing. We saw that and said: we need to implement editing.

Nick Noone: And by the way, the reason that teammate did that is because our intent was — and this is the way the coaching and leading happens — your job is to hit the objective. To hell with the technology. Our job is to build the technologies and tools that empower you to do higher and higher levels of work, but your job is to achieve. That's what creates these crazy things that might look very unscalable, very hacky, but ultimately are the absolute best signaling network.

Ben Rudolph: It's great signal for what actually works — the quintessential rapid prototyping. So that's category one: the product primitives you learn you need to provide the team so they can achieve the objective. Category two is almost what I call the innovation lab, where a deployment strategist goes and builds something totally unique that we're probably not going to integrate back into the product, because it's such a specific capability for that customer. This stuff gets me really excited, and I love watching what our deployment team builds. I was just looking at one the other day where someone had built a hurricane simulator in Peregrine, and I was like: how did you do that? I didn't know you could do that. There was another deployment strategist who built this thing that integrated data from all these different sources — 911 call times, budgets — and you could place a fire department in the city, and it would give you an estimate of how many people that would impact.

Nick Noone: Unbelievable. And she'd been at the company for a month.

Sonya Huang: Maybe the last set of questions. Ten years from now, suppose everything's gone right: Peregrine is the institutional memory layer for 10,000 cities. That's great power — power that outlasts any one administration. And with great power comes great responsibility. How do you think about that?

Nick Noone: I think there's the outcome, and then there's the path to get there. The end game of 10,000 cities requires an operating model that is fundamentally about infrastructure, actually. We, as a technology organization, are delivering technology infrastructure that empowers these organizations to do with their data as they would like — and as they are required to do. Every city, every jurisdiction, I deeply believe, is unique, and I think the idea of preserving that uniqueness is actually beautiful. How do you make each one — the potpourri of cities — more awesome, in its own 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. The way we think about the next marginal customer we support requires a level of hand-holding and delicacy that, candidly, I don't think was possible even five years ago. The marginal cost of doing what we do — to drop it below a million bucks a year — is radical. Coming back to the origin stories: supporting state, county and city-level public safety — there's a lineage of failed business units of major, astounding organizations that tried to do it and failed, because delivering these solutions in a tailored way, at a price point these organizations can afford, was never possible. To create the efficiency to drop the price by an order — or orders — of magnitude has earned us the right to try to deliver on what you just said. And who are we to think that we hold any power over that? It's the institution that has the power.

Sonya Huang: You'll make every city awesome, for sure. I love that. I think you're in a very, very serious seat, and just in the course of this conversation, it's clear you're approaching it with great care and great stewardship. Thank you for what you do — I think the world needs more examples of AI being used to improve lives and improve communities. And thank you for joining the pod today.

Ben Rudolph: Thank you, Sonya.