SPEAKER_00 | Training Data Summary

1. Key Themes

Safety as the Foundation of Thriving Cities

Peregrine was built on the thesis that safety is the bedrock of everything that makes cities work. Nick and Ben's origin story — Nick flying back and forth to Baghdad, Ben working on refugee crises in Sudan and Colombia — converged on the same conclusion: the problems people face at home are just as consequential, and safety is the prerequisite for everything else.

"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." 00:00:12

The Anti-Network-Effect Business Model as a Competitive Moat

While most govtech and data companies compete by accumulating as much data as possible to create lock-in, Peregrine deliberately inverts this. The business is structured so that data stays with the customer — Peregrine never owns it. This is not just ethics; it is strategy, because it is the only model that can earn trust from institutions that have access to extraordinarily sensitive community data.

"The world is very concerned about the idea of the amalgamation of data and the kind of centralized, whether public sector or private sector, kind of 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." 00:20:03

Forward Deployed Engineering as R&D, Not a Cost Center

Peregrine treats its forward deployed team not as support or professional services, but as the primary engine for product discovery and growth. Nearly every major product primitive and innovation lab feature has originated from deployment engineers solving real problems in the field, often with hacky workarounds that became product roadmap signals.

"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, how we land and then how we expand into a customer base is fundamentally about leading from the front through our forward deployed engineers." 00:45:10

Data Preparation Is 95% of the AI Work

The glamorous AI outputs — semantic search, reasoning agents, cold case analysis — are only possible because of unglamorous upstream data integration work. Peregrine has integrated tens of thousands of datasets across customers, and approximately half of its engineering org is dedicated solely to the data platform layer.

"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." 00:26:50

Long-Horizon Agentic Workflows Are Already Production Reality in Government

Peregrine is not experimenting with agents — they are running in production. Approximately 90% of their data integration Python notebooks are now written by agents with human oversight, running for hours, spawning sub-agents, analyzing databases, and reconstructing ontologies autonomously.

"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." 00:30:58

Cold Case Justice as an AI Beachhead Use Case

The cold case agent is arguably the highest-stakes, most verifiable AI product in production today. It ingests 200–300 gigabytes of mixed-media evidence, runs for 30–60 minutes, and has already reproduced exonerations and placed suspects at crime scenes from scattered cell detail records — work that previously required weeks of detective hours.

"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 glean insights. And eventually we got to the place where it could reproduce the results that those detectives had gotten to." 00:32:49

Dropping Price by Orders of Magnitude Unlocks a Previously Untapped Market

A long lineage of major enterprise software companies — including Palantir — tried and failed to serve municipal and county public safety because the economics didn't work. Peregrine's vertically integrated stack has pushed the annual cost below $1M per customer, unlocking an entirely new addressable market.

"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, supporting state, county and city level public safety was — 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." 00:50:28

The Institutional Memory Layer as Critical Infrastructure

The ten-year vision is for Peregrine to be the infrastructure layer for 10,000 cities — not a software vendor but an operating system for civic institutions. The framing is deliberately infrastructure-like: decentralized, sovereignty-preserving, and designed to outlast any particular administration or political cycle.

"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. And every city, every jurisdiction, I deeply believe is unique." 00:49:13

2. Contrarian Perspectives

Ego Is the Biggest Enemy of Enterprise Sales, Not Product Quality

Silicon Valley's reflexive "smart people move fast" culture actively destroys trust with institutional customers. The conventional startup wisdom of moving fast and demonstrating intelligence is read as ego by organizations full of people who have dedicated their entire careers to a domain. The correct posture is radical suspension of intelligence and patience.

"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 is an easy thing to say. But it's such a high empathy, patient way of working." 00:04:56

Not Taking Credit Is a Moat, Not Modesty

In most software businesses, case studies and success stories are marketing assets. In high-trust institutional deployments, trumpeting your wins is actually one of the fastest ways to destroy the customer relationship that earned you the next problem. Silence is a competitive advantage.

"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." 00:34:25

A Tech Company Should Not Set Moral Red Lines on Its Own Technology Deployment

The conventional tech ethics posture is for companies to draw unilateral bright lines (e.g., "we will never enable facial recognition"). Peregrine argues this is fundamentally wrong — it is a form of Silicon Valley paternalism that ignores the legal, community, and institutional context of each individual customer.

"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 — 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." 00:38:53

Deliberately Not Growing Too Fast Is a Strategic Choice, Not Timidity

In a market obsessed with hypergrowth, Peregrine frames intentional restraint as integral to their business integrity. The trust-first, handholding-intensive model means growth has to be earned customer by customer, and trying to shortcut that destroys the core value proposition.

"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?" 00:38:10

3. Companies Identified

Peregrine

AI platform for public safety institutions — police, fire, emergency management, health services. Integrates a customer's existing data into a governed, permissioned AI layer that enables semantic search, agentic analysis, cold case investigation, hurricane simulation, and operational intelligence. Founded 2018 by Nick Noone and Ben Rudolph.

"We are fundamentally in the business of joining disparate information to provide a more secure, properly 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." 00:19:11

Palantir

Enterprise data analytics and forward deployed engineering company. Mentioned as the origin training ground for Nick Noone, and as the most notable comparable — but one that serves only large clients at eight-figure contract minimums, leaving the municipal market entirely unserved.

"The other company that notoriously has scaled this motion, Palantir, which notoriously doesn't take anything less than eight figure contracts." 00:40:48

Airbnb

Early-stage startup that Ben Rudolph turned down an offer from in order to join the UN Refugee Agency — cited as a marker of the caliber of opportunity he passed up to pursue mission-driven work.

"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 UN Refugee Agency." 00:05:46

Flock Safety

Hardware-based public safety data collection company. Cited as a structural contrast to Peregrine — representative of the incumbent model where business growth is driven by adding more sensors and collecting more data.

"How is your business structurally different from the data collection companies like a Flock or an Axon?" 00:18:16

Axon

Public safety technology company known for body cameras and Taser. Cited alongside Flock as representative of the data-collection-centric model that Peregrine explicitly inverts.

"How is your business structurally different from the data collection companies like a Flock or an Axon?" 00:18:16

Dimagi

Global health technology company building last-mile healthcare applications for under-resourced communities. Ben Rudolph worked there after UNHCR, building tuberculosis drug adherence tools for rural India that scaled nationally.

"I left to join an organization that you probably haven't heard of. It's called Dimagi. They're a small company. They work building last mile healthcare solutions in under-resourced places around the world." 00:07:14

4. People Identified

Nick Noone

Co-founder of Peregrine. Former Palantir forward deployed engineer who ran the SOCOM unit and deployed into intelligence operations in Baghdad and the Middle East. Architect of Peregrine's forward deployed culture, data governance philosophy, and institutional trust model.

"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 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 or team could do." 00:02:59

Ben Rudolph

Co-founder of Peregrine. Former UNHCR refugee worker deployed to the Sudanese and Colombian borders, then Dimagi. Leads product and deployment strategy at Peregrine. Deeply operational builder — was literally coding in the car on the way to customer sites in the early days.

"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." 00:45:38

Brian Bubar

Commander at the San Pablo Police Department. The first customer to take a chance on Peregrine. Known for leading Operation Red Reach, a landmark cross-jurisdictional gang and narcotics investigation in Northern California. Identified by Nick and Ben through a public article as someone with the creativity and courage to try something new.

"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 moved forward." 00:12:10

Aaron Blaisdell

Early detective contact at the San Pablo Police Department, cited as an example of the urgent, real-world customer requests that drove Peregrine's earliest product decisions.

"It wasn't theoretical. It was Aaron Blaisdell who had an extraordinarily urgent request." 00:46:02

5. Operating Insights

Use Customer Workarounds as Your Most Reliable Product Roadmap Signal

When a forward deployed engineer invents a hacky workaround — like using a comments field to trigger property updates because the product lacked an edit feature — that is not a failure to be cleaned up. It is the highest-quality signal the product team can receive about what primitive needs to be built. The workaround proves that the use case is real, urgent, and that users will go out of their way to solve it.

"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 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." 00:46:38

Separate "Product Primitives" from "Innovation Lab" to Manage What You Build Back In

Ben Rudolph explicitly distinguishes between two categories of field innovation: primitives that reveal gaps in the core platform, and one-off custom builds that are so customer-specific they will never be productized. Maintaining this distinction keeps the core product focused while still honoring the full scope of customer needs.

"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." 00:47:34

Set Outcome Accountability at the Field Level, Not Task Accountability

Peregrine's coaching model for forward deployed engineers is outcome-driven, not method-driven. Engineers are told their job is to hit the objective — not to use the platform correctly or follow a prescribed process. This unlocks creative problem-solving and surfaces real product gaps simultaneously.

"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." 00:47:04

Build Your Cold Outreach Around a Specific, Named, Public Achievement of Your Target

Rather than generic outreach to police departments, Nick and Ben found a specific article about a specific operation (Operation Red Reach) led by a specific person (Brian Bubar), and called him about that specific work. This transformed a cold call into a conversation about something the prospect had invested their career in — the highest possible form of personalization.

"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." 00:12:33

6. Overlooked Insights

Cities Actively Call Each Other for Peer Advice — Making Institutional Word-of-Mouth the Most Powerful GTM in Govtech

Nick mentions almost in passing that cities call each other when making technology and policy decisions. This is a hugely underappreciated distribution dynamic. In a market where traditional sales motion is brutally slow and trust is hard to earn, a single successful, quiet deployment creates a peer-to-peer referral network that is far more powerful than any marketing. The implication is that govtech companies should optimize relentlessly for reference-ability and quiet excellence in their first few customers, because those customers are actively advising the next hundred.

"It's astounding how cities call each other. 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." 00:40:16

The Verifiability of Agentic Tasks Is the Actual Unlock for Enterprise AI Trust

Ben briefly notes that the reason long-horizon agents work well for data integration is that the outputs are verifiable — and he draws the parallel to why coding agents work. This throwaway observation is actually the key to understanding which AI agent use cases will achieve enterprise adoption versus which will remain stuck in pilot purgatory. Enterprises will only trust agents for tasks where correctness can be deterministically evaluated. Any AI product targeting enterprises should be designed so its outputs are verifiable, or it will never scale beyond a demo.

"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 second piece that we spend a lot of time on is what do our agents look like for operational outcomes for our end users?" 00:31:36