How AI Will Impact Emergency Response: The Tech That Could Soon Save Your Life w/ Michael Chime

Can AI Help Save Lives? | Michael Chime (Prepared) x Kimberly Tan (a16z) In this episode, a16z partner Kimberly Tan sits down with Michael Chime, the CEO and co-founder of AI emergency response platform Prepared, for a powerful conversation about how AI is transforming public safety — starting with 911 call centers. They discuss how Prepared went from an idea in a Yale dorm room to a fast-growing platform augmenting emergency response nationwide. Michael shares insights into the company's go-to-market journey, the challenges of integrating voice AI into highly sensitive environments, and what it takes to build trust with government agencies. Together, they also dive into the real-world impact of AI agents in emergency services, what "human-in-the-loop" really means in crisis moments, and why AI’s future in government is only just beginning. Follow everyone on social media: Kimberly Tan: https://x.com/kimberlywtan Michael Chime: https://www.linkedin.com/in/michaelchime/ ⏱ Topics Covered: 00:00 – Intro to Prepared 00:55 – Founding Story 02:32 – GTM Path Journey 04:34 – How Prepared Is Using AI To Solve Macro Problems For 911 Call Centers 07:12 – Customer Reactions To Using AI 09:08 – The Balance Of Augmenting The AI Work With Humans 11:57 – Managing Data Sensitivity 12:51 – Voice AI Integration 15:21 – Will AI Agents Handle 911 Calls? 15:59 – Customer Onboarding Process 17:14 – Prepared Employees: Tech Or Industry? 17:57 – Managing Company Culture 18:41 – The Role Of AI In Government And Public Services 20:28 – Prepared’s Scale And Future Trajectory 21:11 – Goals For 2025 🔔 Subscribe to stay updated on more conversations at the intersection of AI, public sector innovation, and the future of emergency response.

• 0:00Speaker 0

  Somebody needs help help them. Right. And you can draw like any big and small thing that we do in government to that idea. And through one or even that one, that's like the epitome of that. It's in 911. Like, I really need help. Please help me. And we don't do it great today from a technology perspective.

• 0:20Speaker 1

  Maybe just to start for people, what is prepared do? What is prepared?

• 0:24Speaker 0

  We are AI assistants for 911 calls. I mean, we really do that in two ways. We have a set of tools that totally offload mundane work off of a 911 call taker, right? Things like noise complaints, parking tickets, non-emergency traffic, or backend processes like quality assurance. We will do that completely. And then on emergency calls, we're uh co-pilot. So transcription, translation, handling of CAT entries. So summarizing that entire interaction, we'll sit next to the call taker and make sure no critical detail is missed and that everything gets out to the responder.

• 0:58Speaker 1

  And so this is obviously like transformative and life-changing technology. It's also an area that I think a lot of people haven't thought of applying tech to. So we'd love to understand, yeah, like the founding story. How did you guys decide to actually come and build for public safety and for 911 centers?

• 1:12Speaker 0

  Yeah. So I started working on about five years ago. I was an undergrad at Yale of all things. So people are like, why public safety out of school? A unique life choice. So I start there. It was personal for me. So I so I grew up in a town right outside of where there was an active shooter event in 2012. I was 13. Yeah, my time was small with color. And so you just saw how it rippled. And that was catalyst for me. I went to college thinking about those types of problems. I didn't know it'd be a business. I met two classmates that turned out to be co-founders later, and we built this simple app for schools. And we would go to schools and we'd say, hey, if you have an emergency, as opposed to you using a walkie talkie or a PA system that's built for the announcements, why not use your phone? It's much better, faster means of communication. And we had a couple hundred schools that did it and took us up on it. And it was only through doing that work that we kind of stumbled upon 9-1-1.

• 1:58Speaker 0

  Because we would have schools that would have a situation and would collect information, like a picture, and then want to share that to 9-1-1 and couldn't. And then we started talking to 911 centers and we'd find out that the systems that they're using every day built on the assumption the calls are landlines, that they were that outdated. We couldn't, like I could communicate better with you right now or text than I could to 911. And I felt so backwards. And so we started there solving problems. And then it became, you know, this whole thing of how could we rethink 911?

• 2:25Speaker 1

  Yeah, because I think a lot of the technology people use today, 911 centers are from like the 1960s or something like that. It's really not built for the way in which people actually communicate today.

• 2:34Speaker 0

  Yeah, that's right.

• 2:34Speaker 1

  You know, one of the other things that's really interesting about prepared is uh I don't think people historically thought that selling into state and local government was an area that you know technology venture backed companies could sell into. It'd be great to hear actually the initial story of the go-to-market path that prepared went on, because it's quite a unique one, I think, for a lot of reasons.

• 2:51Speaker 0

  When we were first starting to totally zoom out again, when I first walked through a center, it was clear that technology could get better. It was unclear why it hadn't. It wasn't because there weren't, there were not better tools. There's actually some you know, like call handling systems that were better or CAD systems that were better. And so, okay, well, what actually is the constraint here? And our perspective was that nobody had hacked or or really had solved go to market. I was really inspired by companies like Slack or Yammer. And it used to be true that the large enterprise was not territory for a startup. It was, you know, that's Oracle, Salesforce, like only they could punch it that way because systems IT would block everything. The innovative idea there was well, what if we just got to the end users, proved it to them that it was valuable? And then, you know, we'd still have to go to IT, but we'd have the users at our back.

• 3:38Speaker 0

  And it worked. And it was crazy at the time. People are like, no, you just can't go. They're employees in an enterprise. And our belief was if we could just get to the end users, show them better technology, and this was a technology fight that we'd be well prepared to win that one. And so the simple uh answer to that was we launched a free tier of our product and we said, hey, use it. And we felt like if our technology was side by side the incumbent technology that over time they would we'd be able to build more powerful things, and that's beared out. And so started that way. We got from zero to a thousand centers across the country, which is about six of the US on our platform really, really quickly. And we built a lot of goodwill because people are like, oh, actually, I can do better. I'm seeing it. It's actually working and it doesn't go down, right? So it's actually credible. And so then when we came back with additional products, people took us up on that.

• 4:25Speaker 0

  We've always liked to think of ourselves as just technology partners trying to solve problems. And so it's not about any underlying technology necessarily. It is just like, what is the problem we're trying to solve, or what are the problems our users are facing, and how do we go about solving this? And now we think AI is we should rally the business around AI because it can solve a ton of problems that were unsolvable two, three years ago. The kind of macro problems for a 9-1-1 center are two. One is that call volume is going up. So, but that doesn't necessarily mean emergencies are going up. A lot of that call volume is non-emergent. So I'd say there's two buckets of products we offer. One is full automation. So we will take, and this is for mundane things that just add burden to the call taker. This is non-emergency calls. So often cities will have a 10 digit line or 311, et cetera, where these calls were out to.

• 5:10Speaker 0

  There's big agencies out in California where the average hold time on non-emergency calls is 40 minutes. Nobody's answering those calls. So oftentimes you'll have, especially in large cities, hold times on those non-emergency calls. We reduce those hold times to zero. And then when you're in emergency, there's a lot of people limitation or gaps that people just have that create challenges. I'll give you an example. We had a call a few weeks ago where someone calls in and they're panicked and there's weapons on scene, and that they mention in a roundabout way that there is a person on site that has autism. And the call taker didn't hear that, missed it. Our transcription caught it, but they didn't type it into the notes and they found out later. But the responder there was responding to weapons. And so when they're knocking on the door, nobody's responding. They're not giving any verbal cues back. They're like, okay, I need to like go in and like figure this out.

• 5:57Speaker 0

  And so small details like that really have an impact. And then there's things like when you call, or if you call and you don't speak English, you get a dramatically different level of service than if you do. A language like Vietnamese could have a whole time of seven minutes for a human interpreter. Whereas where we come in and we just translate it instantly. So there's a ton of opportunities for AI to just improve the call processing times. You can also think of us as a co-pilot on the emergency calls. You can imagine some a 911 center is like they're given exactly what to say. But the question is like, do folks say it? They really can't answer that question today. It's all manual. It is you only have people in the back that'll pull out calls, they'll listen to them live, they have a piece of pen and paper, and they'll check the boxes of did they verify the location, did they verify the incident, and they'll get to like two percent of calls.

• 6:44Speaker 0

  Now, with AI, not that hard of a technical problem. It's what was said, what ought to have been said, and then we match them up. And now we get to 100% of calls, quality assured, and then you start to determine trends

• 6:54Speaker 1

  on

• 6:54Speaker 0

  what we're doing well, what we're not doing so well, you can coach those same day.

• 6:58Speaker 1

  When you first realize this opportunity and you went to your customers, what was like how did they receive the idea of like using AI?

• 7:05Speaker 0

  Yeah, it's all been iterative. So the very first product was just transcription and translation. And so I think that was a very on-fire problem. You know, like I said, non-English calls on average have yeah, one to seven minute hold times, right? Depending on the language, right? Some are much faster, Spanish and much faster, but you have hold times.

• 7:22Speaker 1

  Because these are human translators. You have to wait

• 7:24Speaker 0

  to show up. Exactly. They're hitting a button, it transfers in a human interpreter. And if there's not one, they wait until that person's there. And so that was just a very cute need. So we started there. And we thought we that might just be the product, right? Like uh, you know, this is right when ChatGPT came out. We thought the most thoughtful application of AI and emergency setting was as a co-pilot, but you learn next to customers over time, right? So we're now, we're on a rate this year to process over 20 million calls, right? Some of the biggest cities in the in the country. You just start to look at the transcripts side by side customers and say most of this traffic is mundane. Again, you see parking tickets, you see noise complaints, and you're like, oh, we might be able to fully handle that and reduce the burden. And so you start to interject voice. And people really liked it. The way we found quality assurance was uh director in one of our largest cities, he had put keywords on CPR calls.

• 8:12Speaker 0

  And I was like, so he's like, every time a CPR call would come up, he'd get a notification and you can go and watch that call. And I was like, why are you watching CPR? Why is that useful? And he's like, Oh, I'm just trying to do QA and chip in. That's one that we really want to get right. I was like, how do you do QA today? And he took me in the back and he showed me this team of people that had pen and paper. They were listening to calls. And he was like pounding his chest about getting to 3% of calls here. He thought that was amazing. And I was like, dude, just give us the form. Like now we have four, you get to 100%. So it's like it started as just this hey, transcription, translation, this the burden on a call taker is so high. Can we just assist? And then naturally you start stumbling upon all these problems. And I and I still think it's just the beginning.

• 8:53Speaker 1

  Yeah. And I guess one of the interesting things there is like on the one hand, you are replacing the work that people were doing for QA, but on the other hand, there was so much more work that they could have done there that they like simply didn't have the manpower to do. So you can talk about that a little bit, like the the balance between obviously augmenting some of the work as well as you know, taking over some of it end to end.

• 9:13Speaker 0

  Yeah, we get this question all the time in centers. I use two silly examples. Uh and I think they're useful as a frame. I I don't think the AI applications are that different from the past technology applications. Like where are we using technology today? I think there's again two categories. I think we automate work we don't want to do. And then we fill in gaps of things we cannot do. So my two silly examples, I'll walk into centers and have this conversation. And I'll be like, yeah, I probably could wash my clothes by hand. You know, I could like do the whole thing, put them up on the clothespin, but I don't know if I want to spend my time doing that. And so I use a washer and I use the dryer and it it makes a lot of sense. And so I could do it, I don't do it. That's non-emergency calls to me. Like I know you could do it. You might be able to be able to do it better than we could do it, but should you be doing that? Or should you be focused on the core? Cardiac arrest call.

• 9:58Speaker 0

  Should you be focused on the real emergency? And like everybody unanimously is like, yes, I should be focused on the emergency. And so like that's one class. We're using technology for that today. The second silly example I use is I wish I could run 60 miles an hour. That'd be amazing. But uh, you know, I'd be in the Olympics, but uh I can't. And so I jump in a car. And you know, man and machine now get to this outcome way faster, get to my destination way quicker because I'm using technology to do that. And I'd never be able to do that, no matter how much I trained. And so my example to call takers is I'll go into the room and I'll be like, how many of us speak 30 languages? And I haven't seen a single hand. And I joke and say, if I do see a hand, you should pay them double. They're like the best call taker ever. But nobody can do that job. We're not taking anyone's position in that world. We are allowing them to do the job better. And now they don't feel panicked while they're waiting on a line for an interpreter, and they can tell that person urgent, but I have no idea what they're saying.

• 10:49Speaker 0

  I can give you an example. So we um we had a call, is a suburb of really big city, and they just deployed the tool probably two weeks from you know, starting it. And so they're just getting used to it as we remember how to use it. Uh so call comes in, it's in Mandarin, and uh they hit the human interpreter like they normally would, and they just wait because they forget they have prepared. And then they look up and they're like, okay, I have the transcription and they can tell the guy was really urgent. And he was saying, My daughter was shot, my daughter was shot. And they catch it and like, oh my goodness, okay, I can dispatch that. And so they send law enforcement, they send EMS. And by the time that responders got on scene, the interpreter had just gotten on for it, and they save the girl. And their perspective is the girl would have not made it if they waited that entire time. And so you just put yourself in that scenario and be the call taker there and how helpless you would feel just sitting there waiting for the human interpreters.

• 11:37Speaker 0

  And there's millions of examples like that where yeah, you're just empowering them to do better.

• 11:42Speaker 1

  And how do you manage the data sex? Obviously, like this is sensitive data too. And I'm sure your customers care a lot that the data like stays confidential, stays within their system or your system. So, how do you guys manage that data sensitivity?

• 11:53Speaker 0

  One is uh everything's in the US. You know, you would think is fairly common, but is unique in the space. So that's one thing that we stick by. The second is um there's data security policies like CGIS that we adhere to, which is criminal justice information security. And so we adhere to that. And then like your typical, you know, like SOC2 compliance, et cetera. But we don't train on the data. And you know, like the fault question is like why it's because of this. It's all localized.

• 12:21Speaker 1

  Got it. So I guess like having the data set helps you from a product perspective in that you you can better understand what you need to build and such, but you don't actually use the data in the process of building the process.

• 12:30Speaker 0

  Yeah, exactly. It's more that like when we see something happened in the product, those trends you like know quickly are real.

• 12:38Speaker 1

  Well, one of the product things that I do want to latch on to is this voice AI products. Because I think for a long time you guys were a little bit more of a co-pilot assistant when it came to crisis calls. But the idea of launching like a voice AI product that can actually end to end handle a lot of the non-emergency calls coming in. I think it's very innovative in this space. And I'd love to hear how did you guys initially come up with this idea? How does like that product like work in practice? I imagine there's a lot of systems you might need to integrate into, et cetera.

• 13:01Speaker 0

  The same way we found QA or any of these other products. It was just there was a ton of pull. There was pull on the problem. I don't think folks knew what the solution was. But it was clear that uh large part of the traffic was mundane, and you could ask yourself the question does a person need to spend time on this? As we started to work through solutioning, we threw out the idea of like, how would you feel about an agent jumping in and handling the call? And there was real appetite for it. And I think the reason that there is appetite is you know, we went to like where does technology make sense? This was not a people versus bot thing, oftentimes, right? Again, you go to some large agencies in California and the hold times are 40 minutes. So this is like you bought versus nothing. And so we could do much better than that. And then also is like, even if you had zero hold times, you were getting to that outcome by spending a ton, you know, like forced overtime in cities is a very, very common thing where you're pulling people in on holidays, et cetera, to just try to hand answer the call volume.

• 13:56Speaker 0

  And so I think the pain was just so clear, right? And the call volume is only going up and the people are only going down. And so that like general problem, I think people really resonated with. And then when we started launching it early, it really worked. And so it's it's just good at the job. And so we've started to get to a point in some cities where they actually ask for our system, which is exciting. That I think they just know they'll get someone instantly to answer a question practically, it'll take the call. You can have a person in some cities I've selected to do this, monitoring, you know, 10 to 20 calls at once. They can just so there is a UX there, like they can watch the transcript, see what's happening, barge in if they want to. So there is a human in the loop, even though they're not interacting directly with the caller, they can jump in if they want to. Normally, it'll just handle it and it'll go all the way through to the action.

• 14:44Speaker 0

  And that action could equal a call for service. That means integrating it directly into that CAD system, and then a responder would see it, but it's us that's punching in the summary. Or that could be, you know, say there's a parking ticket and you're logging it and you want to track the progress of you saying, No, I didn't do that. We'll send you a link and you can track the progress, but we'll fill it out in the system. So we'll handle it end to end.

• 15:07Speaker 1

  Do customers have appetite to allow AI voice agents to handle 9-1-1 calls, or you think today, like that's a bridge too far.

• 15:14Speaker 0

  I I think what humans are really, really good at is being empathetic to the caller. And I think the only reason we're automating anything is because we believe they are tied up in situations where empathy is not that important. Yeah. Like again, a noise complaint or parking ticket, it's not that important. And that they really should be focused in their time and attention on the cardiac arrest call. That's the only reason we're automating. And so I think AI makes sense as a co-pilot in those situations and freeing up the human to do what they do best, or just offloading things so that they can spend time on that.

• 15:44Speaker 1

  One other thing that I think is really interesting is when people think about like vertical AI applications and prepare it as such an interesting example of one of those. A lot of times there's a lot of education that has to be involved. Like once you deploy the product on site, it takes a while for people to like get used to it. So can you talk a little bit about that? Like, how does the onboarding or implementation process for a customer actually work?

• 16:05Speaker 0

  Some of our products do take a lot of time. Some of them you'd be surprised how quickly people start using them, how quickly people get used to it, right? And so, you know, again, you're just waiting there while you're waiting for the human interpreter. So, like in that scenario, there's not much training needed. They normally put up a separate monitor for us. And the second that that happens, they just look up. We've had centers that have literally signed and the next day are deployed and using it. Um, so that was quickest out of 24 hours. And then there's other products like quality assurance that takes slightly longer. Yeah, often because you have defined workflows and processes that you're using today. It's really more a matter of getting them familiar with the UX and how they can submit feedback. We also will tell them if we check a box and say someone did this versus if they didn't do it, we'll give them like a why behind the AI's decision. And so getting used to, okay, well, should I agree with it here or go against it here are just small things that take time and just change management.

• 16:56Speaker 0

  But often you see people getting going with it relatively quickly.

• 17:00Speaker 1

  And how often are those folks or just people in your go-to-market or implementation org in general? How often are they from the industry versus maybe like from tech writ large?

• 17:09Speaker 0

  No, very often they're from the industry. So you can think of us culturally as like half of the business is just technologists, people that you know come from the valley, et cetera, and or from vast growing startups and product engineering, et cetera. The other half of the business comes from 911 in public safety. It's like quite the group. When

• 17:27Speaker 1

  you bring them together. Yeah, yeah,

• 17:28Speaker 0

  yeah. And uh it's really cool because I think we can just we can solve problems so quickly because a lot of our customers are now on team. And again, we already have a large data set, but now you could just go to somebody and be like, hey, does this make sense?

• 17:40Speaker 1

  Got it. And how do you manage that culture? Because it's a it's a little bit unusual also to have folks who are from an industry that a lot of people in tech aren't familiar with, and then a lot of people who are really cutting-edge technologists.

• 17:50Speaker 0

  I think it's the only way to do it. So, but it is not easy. But I think that to work at prepared, you have to have deep empathy for our customers. So one of the things we do is first 90 days of onboarding, somebody has to go and visit one of the customers. And I think, and it was so we started doing that really early, and it was just so powerful. You can only like really understand what it's like to take a non-English call by seeing that happen and seeing somebody struggle through that. Bringing that in to the company has just, I think, created a culture of empathy for our centers. And so I don't know if you can do it another way.

• 18:24Speaker 1

  What do you think is the role of AI and government and public services?

• 18:29Speaker 0

  I think that you will see, like most industries, you will see the mundane stuff get automated. And uh you're like so we've talked about 311. Like that, I I I go to a lot of cities and that's like the storefront of the city. And we don't do it so well today. Like, I guess a zoomed down point is like I think the like goal of government at the end of the day is somebody needs help help them, right? And you can draw like any big and small thing that we do in government to that idea. Like, okay, I need to get a license, I go to the DMV, you know, like I want a package, they bring it to me, et cetera. Like, and through one or even nine one, that's like the epitome of that. It's in 911. Like, I really need help. Please help me. And we don't do it great today from a technology perspective, right? And so I think what you will see is that in my hope is is that now that's the first place we should be applying this technology.

• 19:16Speaker 0

  But you use the same frame that you use for any other shit. It's like, okay, what is keeping us from doing our best work? Let's take that off. Uh AI could do that entirely. And then the things that I'm really good at, where do I have gaps? And I think you'll see AI jump in there. So, what does that mean for society? That means like, okay, now every city is going to have a line that's manned 24 7 for any requests that you have, and that has downstream impacts for 911. That means anytime you have a real emergency, you know somebody's going to be there and that they're going to be ready because hopefully they've had a break and they're not handling non emergency calls. So I think now every city will be able to do what like a New York City has done. People don't know this. New York City, this is a 2009 stat, so maybe it's even got worse. Spends almost 50 million a year on 311. That's how expensive it is to man a center like this.

• 20:04Speaker 0

  And that's the extreme. But like most cities can't even think about staffing that, where now I think that's attainable. Now I think that's possible.

• 20:12Speaker 1

  So give us a sense of the scale that prepared us at now. So you were found a couple years ago. I think you mentioned like very early on you hit over a thousand centers. What's the rough scale now? And what what's the forward looking trajectory?

• 20:23Speaker 0

  Yep. There's about 6,000 911 centers in the US that man that dispatch I mean have about 72,000 agencies, police fire EMS. We cover six of those 911 centers. And so then a six of responding agencies too can get data from us. In terms of our AI products, we process or on track to process over 20 million calls this year. So some of the largest cities in the US are now on the platform. We're rapidly approaching 150 people, which is great. And then we just closed Series C, which we were excited about. So we've raised roughly 130 ish million in funding.

• 20:54Speaker 1

  And so if you look at like, let's say five to 10 years and you say like prepared was a success or continues to be a success. What does that look like to you?

• 21:01Speaker 0

  I think that you're you're seeing what will be distributed widely in small pockets today. I think in the future 911 operators become more like air traffic controllers. And they'll be able to focus on what they're really good at, just being an empathetic ear to the caller. And everything in the background is just being done. And the entire non-emergency call volume is offloaded. I think every city will have a three-one one line. And again, you'll never have this gap if you call in and you don't speak English. You won't have any critical detail being missed. I think you'll also see the technology, these eight screens condensed to one, hopefully. Just one intelligent system. That

• 21:37Speaker 1

  shows prepared.

• 21:39Speaker 0

  Yes, that'd be great. We could call it that. I think that one, this is a little bit of tangent, but like I think I think one of the things that could be dangerous in our space, like we have kind of a fork in the road right now where I think AI could either be the contributory factor of 20 screens in and double that, or it could bring it down to one. And so why are we trying to build all of these component parts in a single screen is because like we just think for two reasons. One, it won't just be a matter of convenience in the future. Like eight screen is inconvenient. I think I can't think of a better word, but I think it could be dangerous in the future if you don't bring them together because AI is only as good as the data you give it. So if you silo all these pieces of information into eight different systems, it's not gonna have the full context.

• 22:20Speaker 1

  What are you most excited about for the rest of the year?

• 22:22Speaker 0

  I think the thing that that makes it easy for us to get excited is you know, like we recognize there's going like I think right now, this moment is when you'll do your best work, right? Like you don't often realize it, but like when the internet came out, I bet people were like, oh, that's when I had the most impact, and that's happening now with

• 22:38Speaker 1

  AI.

• 22:38Speaker 0

  And so like what I really push our team to think about and any new employee that we would be chatting with is like um be really deliberate about what you work on right now. Because again, it's like it's totally outside of you, this tech shift. And but it's win at your sales. Like you can just make the most impact no matter how hard you work now. And so the cool thing for us is we just kind of stumbled into that inflection while working in mission critical life saving world. And we think that's just such a privilege to really be able to do that. And so I think I'll do my most pack impactful work in the direction of saving lives. And so I'm excited about that.