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

24:13Nick Noone:And so they started asking Peregrine and started interrogating this question. And what they started to uncover is that, okay, these weather patterns, they've happened before. But after a 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

24:57Ben Rudolph:and they can think deeply about that. And overlay their terrain maps and all of the unique things that are county-specific.

25:04Nick Noone:This is all fed by a bunch of, like they obviously have to have incidents information, 911 calls, they have to have weather information, all that information is integrated and then kind of at the disposal of the AI to help the customer.

25:21Ben Rudolph:And we just can't, I mean, there's no way we could have made this up, right? It's like we were sitting in our headquarters trying to pontificate on what, you know, emergency responders in a hurricane needed. There's zero chance we would have figured that out.

25:35Nick Noone:Super interesting. I mean, another one that I'm like excited about it 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 were there 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 just previously impossible. And with the help of AI, 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.

26:28Nick Noone:And I thought, you know, that was pretty interesting to me and a novel use case, yeah.

26:33Ben Rudolph:Interesting. Okay, so you got semantic search and embeddings going on. You got, sounds like reasoning models that uncovered the use case. So one of the most interesting thing 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.

26:51Nick Noone:Certainly, 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 a lot of our engineering points are spent on that data preparation, getting 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 provide the platform for our 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?

27:42Nick Noone:And so what we're seeing is that these deployment strategists, you know, not every customer needs an agent. Maybe they need a hurricane simulator. And the way they're able to write this really well is with AI. And I think this rapid innovation cycle is just super interesting to me.

28:00Ben Rudolph:So cool. Is it your deployment strategists that are kind of coding things in customer environments? or do you think your customers might even, you know, they have all these ideas in their head of what they want. Can they go from idea to working application themselves in your environment?

28:18Nick Noone:A hundred percent, we enable that. I think the, you know, what we see most often is a partnership with our deployment team 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 kind of leverage the platform in that way, but a lot of that motion comes with our deployment team.

28:43Ben Rudolph:Do you think that'll change over the coming years? I see it changing right now. I mean, I'd love to get your thoughts on this, Ben. I think it takes confidence to embrace that and say, you know what, let's innovate and may the best method win.

28:59Nick Noone:So much of what Peregrine does on the technology side is emergent. Peregrine has been providing domain expertise for our customers. 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 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.

29:47Nick Noone:And so I believe like the core strategy of Peregrine has remained very unchanged in this new age of AI. And it's just, we are applying a new technology to see how it impacts our customers.

29:59Ben Rudolph:Are you doing anything on the long horizon agent side or background agents?

30:04Nick Noone:Yeah, absolutely. A couple things here. So I think stepping back a little bit, like what is the Peregrine 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 deterministically evaluate these agents for completeness and correctness on integrations.

30:50Nick Noone: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 deployment team. And these agents run for hours, right? And they will 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.

31:24Ben Rudolph:Yeah.

31:24Nick Noone:That's one bit on kind of long horizon agents.

31:29Ben Rudolph:Reminds me of how people are using these coding agents for like code-based migrations. Also similarly just long-running unglamorous work.

31:36Nick Noone:100%. 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. And so the 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 to 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.

32:24Nick Noone:And the detective is tasked with going through all that. So that in and of itself takes a really, really long time. I've been in 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 goes 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.

33:02Nick Noone: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 glean 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 U.S. 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.

33:53Nick Noone:And this was a single, this is 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.

34:25Ben Rudolph:As a quick comment, this example, Ben Breve, 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 AI is so high.

35:11Ben Rudolph:Yeah. 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 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. um i that can't be ignored first of all um and i i think if we look at the 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, of the department.

36:34Ben Rudolph:And for us, like we have to protect against those moments where it's not like we don't need to, we don't need to break the rules, nor obviously should any organization break the rules in order to find a way to like 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.

37:18Ben Rudolph:And then building the connective tissue, building the interoperability is going to happen. 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. I'll share a grin, the anti-network effects business. I think it's important, especially given the heat now around AI is so powerful. And so a lot of this data that used to exist, I think people now fear what happens when it's all swooped up into a central panopticon.

38:06Ben Rudolph:That's deeply un-American. Yeah, yeah, yeah, yeah. Yeah. And I think that as a business, it's like I find that our business is a big practice in 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.

38:54Ben Rudolph:I think the idea of like 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, you know, 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.

39:42Ben Rudolph: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, 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.

40:28Ben Rudolph: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 in a similar vein, I want to talk about delivering technology to the underdonks 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 notoriously has scaled this motion, Palantir, which notoriously doesn't take anything less than eight-figure contracts.

41:11Ben Rudolph:did your mentors try to convince you not to do this? Did people tell you like the economics of this are not going to scale? And what made you think it was going to 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 that will scale later. And the idea of going deep to build a vertically integrated tech stack, 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, to configure all of that and create a system that's open, interoperable and can check these boxes, the audacity, I think, is real.

42:01Ben Rudolph:and to build each of those components in a way that's first class, not just a 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. I think the uniqueness about Peregrine 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.

42:40Ben Rudolph: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 Peregrine and they are amazing. They're so smart, so humble. They so deeply embody the mission. And so I think 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.

43:20When you can build a culture where people are trying really hard and can achieve excellence,

43:28Ben Rudolph: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, one of which is definitely the pursuit of excellence. I think a lot about these environmental factors of trusting the individual contributor. It's a very important thing that we're trying to hold on to. And to do that with a forward deployed engineer, to truly do it, really requires trust. because we use this analogy of a dark cave, right?

44:06Ben Rudolph: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, you know, 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, um, institutional trust and, 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.

44:55Ben Rudolph:And and now across multiple countries with our forward deploy team. It's fascinating. It's so different from when I talk to companies who view forward deployed as a cost center. You guys are fundamentally very different talking to you. Definitely R &D. Definitely R &D and growth. I mean, 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, the, you know, how we land and then how we expand into a customer base is fundamentally about leading from the front through our forward-to-plate engineers.

45:34Ben Rudolph:Have you discovered any new products through this motion?

45:38Nick Noone:A hundred, I mean, a hundred percent. 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 Nick's like, okay, we got to get these reports done. 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.

46:03Ben Rudolph:And it wasn't theoretical. It was Aaron Blaisdell who had an extraordinarily urgent request.

46:09Nick Noone:Yeah. And I feel it. And so it's so 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 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 deploy team. For the longest time in Peregrine, 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.

46:53Nick Noone:And that comment would be read by Peregrine and then update a property. And that was the way they implemented editing. And so we saw that and we're like, we need to implement editing.

47:04Ben Rudolph: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. 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. And that's what creates these crazy things that might look very unscathed, very hacky, but ultimately are the absolute best signaling network.

47:34Nick Noone:It's great signal for what actually works. And it's like the quintessential rapid prototyping. So that's like 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.

48:15I was just looking at one the other day where someone had built a hurricane simulator in Peregrine.

48:22Nick Noone: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, it integrated 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. It's unbelievable. And she was at the company for a month.

48:49Ben Rudolph:Maybe last set of 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 any one 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 end game 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.

49:44Ben Rudolph: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 potpourri of cities, make each one more awesome in their unique way? That's cool. Uh, the path to get there requires serious integrity, moral compass, core values, um, not just in words, but, but in, um, 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.

50:47Ben Rudolph: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 a 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 or orders of magnitude 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?

51:26Ben Rudolph:I mean, it's the institution that has the power. We'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 that you're approaching this 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.

51:54Nick Noone:Thank you, Sonia.

52:13Thank you.