RHS 162 - Insurance Robots-as-a-Service with Quandri.io

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We'll be right back. in a crude laboratory in the basement of his home hello everyone and welcome back to the show today we have a tremendous episode for you an absolutely tremendous episode we are bringing back a topic that was originally touched on in the episode where I talked to

Jeff Roy, which we did about two months ago. So if you want to go back in the archives, you can find that episode.
Jeff Roy is a tremendous guest. I'm an enormous fan and he's an awesome friend.
And we got into what's being called digital workers or basically bots that use machine learning and algorithms to get into your agency management system and do highly repetitive tasks. And Jeff talked a little bit about the technology.
We talked a lot about other stuff that was going on. And he brought up the company that he's using for this particular work, which is Quandary.
You can find them at Quandary, Q-U-A-N-D-R-I.io, Quandary.io. And this idea fascinates me.
It fascinates me for a whole bunch of reasons. And I wanted to have the founders of Quandary on, Jackson and Jameson.
And I'm going to butcher the pronunciation of their last name. We'll say Fregeau, F-R-E-G-E-A-U.
They are from Canada. They are the founders of Quandary.
And I wanted to have them on to talk about exactly what are digital workers? What is this tool? And they basically break down what are bots, what are kind of robots as a service. And then ultimately, what our topic is, what are insurance robots as a service, and what does that mean for us as insurance professionals, as agency owners, as producers? What are some of the tasks that these bots are able to do today? Where are they going in the future? And what efficiencies are they bringing to the table? And what I think is particularly interesting about digital workers in particular is that I think they were going to have a major impact on the virtual assistant industry in so much as what we are currently paying virtual assistants to do today in terms of repetitive tasks.
Many of those, not those that require decision-making because these bots are not making decisions, but for those repetitive tasks that don't require decision- making, why would you have a human doing those when a bot, after being properly trained and they describe what that means, can be even more effective, more accurate? It's really, really interesting stuff. I think you're going to love this episode.
It's certainly something to get your brain going on building this idea of a human optimized agency. Before we get there, I want to give two quick shout-outs.

One is to the brand new community resource that I created called Finding Peak.

You can go to findingpeak.com.

It's a sub-stack.

You can subscribe for free.

You get articles every Friday.

And what we did was we added an additional feature where every Tuesday for paid subscribers, and paid subscribers are seven bucks a month after the fees that Substack takes that basically make nothing. It's not about making money.
It's about buy-in from you guys. But for paid subscribers, every Tuesday, we're doing a deep dive video post, which allows our paid subscribers to dig into topics, which allows me to dig into topics and really break them down through videos, video walkthroughs of different tasks we're doing, of different tools that we use, breaking down different concepts, answering subscriber questions, following up on comments that paid subscribers make, and basically using this as a resource to deliver to those interested, to those looking for the next level in their business, what it means to be a digital agent.
And that's really what Finding Peak is all about, is building peak performance into your business, both physically, emotionally, from a process standpoint, from a tool standpoint, from a leadership standpoint. What does it mean to get to peak in your business? So we're searching for peak.
We're in the subtext of finding peak is in pursuit of peak performance in business life and insurance. And my friends, this is something that I've had on my brain for a long time.
I kind of launched finding peak a few years ago as a brand. It didn't last very long.
I wasn't ready. I'm ready today because I want to help those who are interested in building a digital agency, who are interested in peak performance, who want to figure out how to optimize their agency to be exactly what they want it to be.
I want to help them get there. And so I say go check out findingpeak.com.
That's my big ask is if you love this podcast, if you like following me on LinkedIn and the different things that I do, I'm going to be very tactical, very deliberate, very consistent at Finding Peak and I think you're going to get a lot of value out of it. I also want to give a big shout out to the parent company of Rogue Risk and that's SIA.
SIA has been a tremendous partner for us. They are not asking me to do this read for them, so I don't want you to think that part of my deal is I had to do reads for them.
This podcast is my own still. SIA doesn't own this podcast.
I can do whatever I want with it, but I have to tell you that you never know what you're going to get when you're acquired. I had no idea no idea.
You know, I've obviously trusted and think

very highly of Matt Maciello personally. And then once I got to know his team and the rest of the leadership team, I thought very highly of them and was very impressed, but you never know.
And I can tell you that joining SIA, being part of this very large 40 plus year old network, and watching the wheels turn as they adapt to the changing environment and adopt new tools and processes to help their network agencies become successful. Guys, if you are an SIA member, I think it's time to open up your ears a little bit and start taking notice of what's going on because I think there's some incredible stuff happening.
And if you're looking to optimize the revenue in your agency, guys, I, you know, I know that for a long time, there's been, you know, mixed emotions around all aggregators, not just SIA. But I can tell you that a lot of the things that are happening inside the SIA ecosystem are very, very exciting.
So I wanted to do that read for our parent company, the company that's allowed me to really realize the vision of Rogue Risk. I couldn't have done it on my own.
And it was only through my partnership with SIA that we're going to be able to execute our vision of the no ceiling insurance career of being a national, small commercial insurance brand, competing against some of the other biggest brands in the country. You know, they make that possible.
So I want to give a big shout out to them. So with that, I will be quiet.
In terms of this intro, I will stop the intro. We can get on to this absolutely tremendous conversation that you are going to love with Jackson and Jameson from Quandary.ary.io here we go hey what's going up guys hey uh sorry i'm just switching my headphones here hey run hey what's up how are you doing i'm doing very well i appreciate you guys jumping on here with me yeah it's great to uh great to meet you yeah i've heard a lot about you from uh from jeff and some of the other guys we know in the industry, so nice to finally see you.
Oh, nice. Wow, that's good.
And Jeff's got great things to say about you guys and the work you're doing and all that. And I don't know if your ears were ringing or not, but he was on the podcast about two months ago and was talking a little bit about what you guys have going on.
And, you know, I said, Hey, you know, at the, at the end of the show, I said, Hey, can you connect with you guys? I'd love to take a deep dive and take a deep dive and just get to know you guys a little better and share what's going on and ask some deeper questions. Cause obviously he understands the technology a little bit, but he's still just a user and, you know, and gave us some good use cases.
But I think, you know, I think that, you know, a lot of people have questions about, I mean, staffing is obviously a huge issue that leads you to things like, how do we find staff that can, and methods of servicing and whatever that can reduce costs. So people look to VAs, VAs are great, but also a lot of work.
And then, you know, you look to something like what you guys are doing and some of the, you know, even the basic functionality of what you're doing, you know, and your mind starts to spin around ways that you can be more efficient and effective while keeping costs manageable and consistent. And that's very exciting.
So, you know, to kind of kick things off, love for maybe, well, Jackson, we start with you and then James, we'll move to you just so you guys don't talk over each other. Maybe just a little history, you know, you don't have to give us since birth, but just kind of genesis of you guys, your origin story and of Quandary.
Sure. Yeah, I'll start with my background and kind of go into Quandary and then Jameson can give his as well.
I originally did a finance degree. I was always really interested in entrepreneurship and business itself, but didn't really see that as something that you went to school for.
That didn't make a lot of sense. And I really like math.
So I ended up doing a finance degree. And during my finance degree was getting pulled in the direction of entrepreneurship.
So after school, instead of going the finance route, going into corporate finance or banking or something like that, ended up working for a startup right after school. Had a great, great boss there, a really good mentor.
And I was the third employee there and basically was the assistant to the CEO. I started doing basically everything he didn't want to do.
I was writing policies like, you know, SOP, standard operating procedures. I was booking travel.
I was paying bills. I was paying taxes.
I was helping to hire. I was basically doing everything under the sun and learning how to build a business.
So he was a really great mentor to teach me how to actually do this. And I really looked at that as an opportunity to learn how to build a business myself.
And it was actually at the tail end of that business that we had the idea for what ended up becoming Quandary. I had become the COO at that business after a couple of years, and I was managing a team

about 30, 35 people.

And a segment of that team was doing high volume, repetitive work.

And as everybody probably can attest to, that work is boring.

It's hard to staff.

It's hard to manage.

There's a lot of turnover, so it's hard to train for.

And there's a lot of mistakes that happen in that work because people are just doing

the same thing over and over.

So naturally, we were looking for technology to put in place to automate that work instead of having people do it. And the system that we were working in didn't have very good APIs.
There wasn't really much we could do in the back end. So we were looking for solutions to work over the user interface or over that software platform itself.
So I came across bots as a potential solution for that. Learned the technology.
This was right at the beginning of COVID. So I had some extra time.
Learned the technology, built a few bots to solve that problem in that business. And Jameson and I had been throwing around different business ideas for the last couple of years.
And this seemed like it had legs. There was nobody offering robots as a service to any businesses that we could see in the US or Canada, especially to SMBs.
And we saw an opportunity for businesses to really benefit from this kind of technology, technology that can save time, repurpose staff away from these high volume repetitive tasks. So we started Quandary with the mission to free the workforce essentially from this high volume repetitive work and allow people to focus on what people do really well and robots to focus on what robots do really well, which is that high volume data processing.
So we started Quandary to solve that problem initially in industries that had high volume repetitive operations. So healthcare, legal, finance and accounting, retail, and insurance were the first couple of industries that we worked in.
And after working with our first couple of insurance agencies, a little less than two years ago, we realized pretty quickly how massive of a pain point there is in the insurance industry for this high volume kind of work and that it's not going away anytime soon at all. There's a lot of structural reasons in the industry why that's not going away.
And it was something that there was almost like a need to solve. Like we were seeing a demand for this, like people were demanding this technology, whereas in other industries, it was it was helpful.
But in insurance, people really needed it. So we pivoted the company at that point to focus on insurance, specifically on insurance brokers and agents and have been have been basically going deeper and deeper into the distribution channel of insurance ever since.
And that's now all we do is build, build robots for insurance agents and insurance brokers. Tremendous.
I got a bunch of questions there, but James, I'm interested in you. Yeah, of course.
First, thanks for, thanks for having us on. Jackson told his story of going into and learning about startups from the business side of startups, and then I took the other route.
So for tech startup, there's kind of two different sides to it. The other side being the technology side.
So I did a computer engineering degree. And one of my first actual roles as an engineer, as a computer engineer, was at a startup that was building robots in a sense.
So it had similarities, but these were actually boats to go out into the ocean autonomous boats that were solar powered to do scientific research essentially and gather all this data and so I was a small team I started when there was just the two founders and myself and one other employee and immediately fell in love with the startup environment. Just very quickly realized how much you can get done and how much of an impact you as the person working for a startup can have on the company itself, but also how much of an impact that startup can have on you as an employee and just give you so much opportunity to learn and grow.
And so I was there for a while and just building all sorts of systems, all sorts of prototypes for this company. And after that was right around the time that Jackson and I were bouncing around business ideas.
I mean, we've been doing this for quite a few years and there was a few that we thought we wanted to pursue and then stopped after a little while because generally we thought for most of those that they weren't really as big of an impact as we wanted to have. So when we first started looking into robotic process automation, bots and automation in general, I think we, after working with the company that Jackson worked for, and then a few other companies after that, we quickly realized just how massive this impact this can have on so many different industries.

Like Jackson was saying, that led us to starting Quandary and kicking it off and working across all sorts of different industries. And I think one of the key points about when we first started

working with insurance brokers and agents that Jackson touched on is that all of these other

industries that we had worked with, they had a pain point around high volume, repetitive processes that were taking up labor. But the pain in the insurance industry was probably about 10x what we were seeing in all of these other industries.
And it was just a very immediate need. So that led us to pretty quickly pivot and focus solely on the insurance industry, because that's where the biggest need is and was.
And we haven't looked back since. So thank you for that, guys.
That's awesome. And it's a cool journey that you have someone coming at the business side, someone coming at the tech side.
I know having founded Rogue Risk a little over two years ago, if I had had a technical or more technical founder alongside of me, I think we would be a lot further along than we are today. I love technology and I'm a highly functioning user of technology, but I cannot create technology.
And I'm not super good at taking what I would like something to do and turn it into something that someone could actually build on, you know, which is a skill, right? That skill of, you know, translating a user's feedback or what a user wants and translating that into, you know, a document or a series of tasks or whatever that you would need a system to do. I'm not that that's that's a whole nother skill.
So I think that's really cool. You guys have that between you.
My first question in general, and some of these are going to start fairly remedial, but I think it's important in setting the stage for the audience like one just so everyone's clear they're not talking about actual robots like you see in like star wars or something like that like that's when they say robots that's i'm assuming that's not what you're talking about but no that's that's not what you're talking about so so what is common common question common question though but no not yet not yet not yet see, I can see some of the audience going. So there's this mechanized humanoid going to come into my office and start banging on my keywords now.
No. So, so when you say bots, like what, what, what does that mean? Like, you know, we hear, you see on social media here on the news bots, or, you know, if you're into cyber insurance, then bots are a big part of what the hell is a bot what is it when you say that what does it mean like what what is it actually that that uh a neophyte could could grab onto yeah good good question maybe before i jump to that one thing i'd like to call out is that uh these mustaches are not a fashion statement quandary is doing a movember fundraiser gotcha and james and i are having a competition just so just just know, Ryan.
Okay. I liked them though.
I'll tell you. I was a little, I mean, mine's a little further down my face, but I do like it.
Yeah. Thank you.
Yeah. In terms of a bot, basically why we call it a bot and what exactly that means is that this robot or this program, this piece of software is going to interact with your software in a similar way that you do.
So they're going to get a username and password. They're going to access your system that way.
They're going to click buttons on the screen. They're going to read text off the screen.
They're going to interact with that system in the same way that you do. They're just going to do it a lot faster.
They're going to make less mistakes and they can run 24-7. But's why we call it a robot is because a lot of these systems don't have that backend access.
You can't automate things through an API or through a backdoor. So you need to go through the front door.
So you have a robot that emulates what that person is doing. Gotcha.
Now, do you, if you are working inside a system that has a API with capabilities, can you set it up that way too? Does it have to go in through the front door or can you systems plug in through an API connection as well? Yeah, plug in. So if there is an API, we'll use the API.
That's actually easier for us to do. So we'll interact with it that way.
But if it doesn't, then we'll use the username and password method. Sweet.
So what are the processes? I think this is the second the biggest question I got after Jeff's interview was, he gave some kind of high level examples of what these robots could do. But I think, you know, I think it would really help people if in as broad a stroke as you could help them better understand like exactly what kinds of stuff are they capable of doing?

Because, you know, I think some people basically assume it's almost nothing. Other people have the assumption that it's everything that a human could possibly do.
And we always know the truth is somewhere in the middle. So, you know, kind of break down, like what are like kind of the general functionality features? And then maybe what are one or two actual procedures that you've seen people using this for just to kind of allow people to visualize what you're talking about and what they're capable of? Sure.
Sure. Yeah.
So at a high level, looking at any process that is rules-based, high volume and repetitive rules-based, meaning you can break it down to an, if this happens, do this, if this happens, do this. It's high volume, it's repetitive, and it's all digital inputs, meaning that what that bot is actually reading is some kind of electronic digital input like a number in a spreadsheet, number in a software platform, a PDF document, not a written signature or something that somebody has put in by hand.
So at a high level, those are the types of processes that you can automate. In terms of actual specifics that we're doing right now for different agents, the way that we approach this is by having productized robots.
So basically a robot that is pre-built, that's already solving the problem for an agent that they can then deploy into their system and do a little bit of configuration on there and to make it fit for their operations, but not something they need to build from scratch or work with us to build from scratch. So I can quickly run through what those three products are.
They all automate different aspects of an agent's operations. The first one helps with the daily download process.
So the EDI process that happens on a daily basis,

all of that, all of those clicks that need to happen, and then all of the matching that needs

to happen afterwards. So all of the policies that don't end up routed to the correct account,

all the edocs that don't end up routed to the correct account, but can identify those, figure

out where they need to go, send them to the correct account so that a person doesn't need

to do that every single day in the morning before the brokerage starts for the day. So that's the first one.
The second one, we call the e-doc executive, and that will focus on categorizing, storing, and naming documents. So basically, when those e-docs downloads, the actual policy documents, opening them up, figuring out what they are, naming them something contextual.
So for internal standardization, they're named properly. And when those policies get sent out to customers or accessed through a customer portal, they're named something like personal auto, 2015 Ford, $1,483, not whatever they come down through a carrier safe standard naming conventions across your business.
So that's a second application of it. And the third, and I think our most interesting one is the renewal reviewer.
So what this robot will do is look at last year's policy. It will look at this year's policy.
It will extract all of the data out of both of those policies. And what it will do is prepare a report and give that report back to you.
And what that report is, is a comparison from last year to this year, showing you any major changes. So premium changes, things that have been pulled off, things that have been added on discounts, coverages, surcharges, that sort of thing.
And then it will also compare this year's policy to a rule set. So let's say you want every policy to have sewer backup coverage, or you want to know every premium increase over 20%, things like that.
Or, you know, for example, every premium increase over 20% over two years or over three years. The robot can look at things like that, give that back to your team in a report so that they can scan that in 20 or 30 seconds instead of somebody spending 20 or 30 minutes preparing that report.
So what it allows their team to do is only focus on the ones that are most important, where there's a risk of churn, a risk of that person not retaining, or where there's upsell or cross-sell opportunities. So you can focus on the 20 or 30% that are really important, and the robot can handle the rest of them.
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That's really interesting. What about automated rating? What about like, can you take like if a renewal comes in, again, you probably get this shit all the time, but that's all right.
We're on a show. So I have your attention.

And, you know, the idea of in that renewal review, you get that this renewal came in 20 percent over.

Is there, you know, can you ultimately get or is the goal to get to where that robot could then actually re-rate that business through a platform, depending on who you use, some sort of rating platform could then take the information, plug it in and re-rate it, come back and say, hey, your Hartford's renewal is a 20% increase this year, but Travelers is actually would only be an 11% increase. This might be a good place to move it.
Like, is that, is that level of sophistication possible over time? Does it take, is it, is it teaching the system or is it just as sophisticated as you want to get? You can, if the, if everything's in place. Yeah.
So that's, that's absolutely part of the goal. There's, it's very possible to get to that level of sophistication.
What it takes is time and training, right? We need to train these robots to be smarter, to be more intelligent, to be able to process more information and more complex and more nuanced information. But that is something that we're looking at pretty seriously is being able to add that onto the renewal process.
And ultimately, where we want to go with this is really being able to use a robot for all your backend processing, high volume, repetitive activities and have people focused on what people are really important for making decisions, talking to customers, upselling, evaluating risk, um, and allowing all that data processing to happen in the background through a, through a robot. Yeah, we, we, um, one of the first things I wrote down when I was doing the business plan for Rogue was the term human optimized.
And everything that we do at Rogue is pushing towards. I mean, obviously, we're only two and a half years old, so we're still in very much in startup mode too.
and you know what you're describing and what you're saying actually fits very much in with what our kind of core philosophy is is that humans should be used in the activities and in the touch points where they actually where they add the most value and they should be removed or have their role greatly reduced in the places that don't have value you know and one of the things you know it's why we're always looking and trying to find more automated ways of like producing COIs, right?

Like I know a lot of agencies and hopefully over time this will change, but a lot of agencies still see one of their primary value mechanisms being the production of certificates of insurance, which was at one time a very valuable thing that we did. Today, it's kind of like the barrier to entry.
It's kind of like the bar. Like if you can't rapidly pump out with limited friction, a certificate of insurance, they're almost immediately looking for someone else, right? Especially contractors or anyone who has to do more than a few certificates a year.
And it's like, how do you, you know, again, how you do it is kind of a rhetorical question.

But the question we always ask ourselves with processes such as these are, where are the places that like you producing a COI today as an independent insurance agency adds zero value to your customer? It's just an expectation. So if you can remove the human or reduce the human's touch as much as possible from a process like that, then,

then you're giving the customer what they, what they want, but you're,

but it's not something that adds value.

So don't spend your people's most valuable resource,

their time on that activity, because it just, it's, it's just a bar.

It's just the bar. It's, it's what, it's an expectation.

Right. Yeah.

Spend quite a bit of time just exploring. As you can imagine, and as you know, I'm sure there are just countless of these processes that are just necessary for doing the business that you do.
And so we spend quite a bit of time just exploring all of these processes and what has the most value for building a bot around. And those three that Jackson mentioned are essentially, that's our starting point of the processes in these BMSs that we think add a lot of value to the barricades and the agencies, but very much so are constantly looking and evaluating at what the next productized bot will be because there's an almost infinite number of processes that can be automated through these bots.
Yeah. And I think to your point, what you were mentioning there, Ryan, what this is all about is optimizing the person, right? getting people away from the work that is really important, like issuing a COI or doing a renewal review.
Like it's important. It has to happen.
It adds a lot of value to the brokerage, but there's no additional value of having a person do that, right? It's not like talking to a customer. It's not like trying to sell somebody.
It's not like you need all of that insurance knowledge to evaluate a risk profile. It just needs to happen.
And that's really where you can put a bot in place to optimize people to focus on what people are really good at. Yeah.
I mean, while you were doing those first three, which are an awesome place to start. And I also, you know, I wrote down on the side, I was like automated rating, accounting, COIs, like data gaps.
Like I you, you actually may be working with him. I'm not sure, but, uh, Paradiso, uh, Chris Paradiso, a buddy of mine, buddy Jeff's, um, I know at one time he had three virtual assistants, um, that he was working with who were overseas, basically working on data gaps, right.
Plowing through records, making sure that there were 17 pieces of information he wanted to have on every account. And I mean, now you're paying three humans who make mistakes, right? And not that a bot can't make a mistake, but obviously the point is like, it probably won't make that mistake twice, right? If it makes it once, then it learns.
And then the second time it won't make it humans can, as we all know, we'll make mistakes multiple times the same mistake. And it's frustrating.
That is it, it happens. You know, that's a lot of cost.
It's a lot of training. It's then having to come behind those three humans and then Q QA their work, you know, on a regular basis.
And, you know, something like that, I'm assuming would be fairly easy for you guys to do is just ping, ping every record in the database and make sure that all 17 specific pieces of data are captured. And if they're not, you know, either calling them out or, you know, maybe even firing off an email or something like, like that kind of stuff to me, it just, it makes so much sense.
Like that, that to me, like that, those kinds of tasks are the tasks where like in most agencies, this stuff isn't even getting done. And it's why, you know, you, you have all these inconsistent phone records, inconsistent email records.
You, you don't have the F E I N for 40% of your commercial clients. You know what I mean? Like, like, and then when you, in those moments, and this is the thing that I think about, or this is the thing that I think a lot of agency owners don't consider is, okay, does it matter that I don't have Jim Jones bakery that I don't have, you know, the Jones bakery F E I N number? Well, 99.9% of the time, it doesn't matter.
But then when I go to quote their workers comp for them and I don't have it, well, now I got to call them and they don't pick up. And then I email them and they don't get back to me three days later.
And then now you've just wait, you know, and then you do that for hundreds or thousands of clients. Now, all of a sudden you're, you're wasting so much time in a year.
And that's where, you know, these types of tools, like what you're building at Quandary, like I just see them as, as it's a natural next evolution. Um, you know, humans make decisions, robots, automation, bots, whatever, they do all the other stuff.
They do all the process, the, the, the, the grunt work, but the humans, we still need the humans for the decisions and the relationship and that kind of stuff. But man, there's so much that can be taken out of the hands that just doesn't, it's just busy work today.
And it's not actually valuable. And you're paying your most expensive people to do things that don't add value to your clients.
And that should be scary to a lot of agencies. Yeah.
And it makes, it makes people feel productive too, doing that busy work, right? Like I think we've all had this, like you end up answering emails for four hours in a day or whatever that thing might be. And you feel like you've been really productive, but have you actually moved the needle, right? Have you actually brought a lot of value to the business? If you remove a lot of that busy work, maybe people like they don't need to have their nose to the grindstone for eight hours a day, but they can produce way more value because they're focused on much higher leverage activities.
Yeah, right. You can create a lot more with the time that you're putting in if you don't need to do that low value busy work.
Yeah. So let's say, you know, I'm an independent agent, and I'm listening to this.
And I'm like, you know, geez, that the daily downloads and the just just in the edox thing. Right.
Just taking the that would be amazing for me. Like, what is what is an engagement with you guys look like? Like, how do you get started? And, you know, this doesn't have to be a hardcore sales pitch, but obviously I want you to talk about what you do.
Like, you know, is there you know, is this like a, you know, I know people are going to have, well, maybe, you know, does my AMS? How does, you know, how does this work? Uh, you know, how much time is it like, what does it take to get involved in this? And then, um, you know, maybe talk a little bit about like the, the buildup process. Cause I know, you know, we'll give a human a thousand mistakes, but if something automated makes one mistake, it doesn't work.
And it's all sham and, you know, and, you know, we should go back to, you know, the days of, you know, the horse drawn carriage and passing notes hand to hand, you know, so it's just, it's funny how that works, how automated systems get one mistake and humans get a thousand mistakes. But so talk to me a little bit about like onboarding, getting, you know, what it would look like to, to, to figure these things out.
And then how do you deal with some of the early, early, you know, the, the, the get off the ground stage? Yeah. Yeah, absolutely.
And you're, you're right. Like a bot at 97% is almost worse than a person at 85%.
Yeah. Because you're, we're more comfortable with people making mistakes, right? We're, we're a person.
So we're okay with that. But typically, once these bots are up and running, they're as close to 100% accurate, as they can be, we always err on the side of a bot not processing something, if it's not 100% sure of what it's actually processing.
So the error rate is very, very, very low. What you'll see is like a bot might process 90% of edocs, for instance, and it will leave those remaining 10% for a person to do.
So it won't make errors on those 10%. It will just not process them.
In terms of getting started, depends on the product. So it depends on what robot you're implementing.
Some are easier to implement. Some are a little bit more complex.
On the easier side is our EDOC executive. We can get that up and running in under 30 days.
For our renewal reviewer, that one takes a little bit longer. It's about two to two and a half months to train that bot on your data set and get that deployed.
In terms of what that process actually looks like, we have a discussion, obviously figure out what processes interest this person and look at their processes internally. So look at how they're running a renewal review, get a really good understanding of that.
See if there are differences from how the product works to how it actually runs. Go through a design phase where they get to configure the bot to themselves.
So using like the renewal reviewer as an example, that robot will look for about 30 or 40 data points on every type of policy. And that's going to get pulled off of every policy automatically.
You as an agent, you get to determine which of those data points are actually important to you. So which ones do you want to be made aware of? Which ones do you want in a report? Which ones do you want to be flagged for your team? So that design phase and that configuration happens in front.
That usually takes a couple of weeks. After that, we're just training your bot in an environment running on your data set while our engineering team is watching everything that it's doing.
So building in new rules, building in exceptions, making sure that by the time it gets to deployment, that it's working perfectly. Your team is going to be reviewing some of those outputs during the deployment phase.
And then once we actually deploy it and it's running autonomously at that one to two month mark, then it'll be processing everything in your system at a, usually at a rate of 90 to 95% of items processed and at an accuracy rate of 97, 98, 99%. One follow-up question in there just before you keep going um so so if i heard you right you you could actually take the that the physical downloaded document that that our attachment that comes along with the download and actually the bot can scan it and pull piece of information off of it and maybe compare that against what the actual downloaded information was and fill in any gaps that maybe the download didn't pull.
It can actually pull it off the dock and bring it over. Is that the way I heard that? Yeah, that's correct.
So it can run off the application data, so the EDI data in the AMS system and or the actual policy data. So we've got we've got machine learning models internally to extract all of the data from the actual PDF documents themselves.
So although the EDI is getting better, it's still not 100 percent accurate. And oftentimes we still have people looking at the policy documents, even if the EDI is 90 percent accurate, because there's those one or two things that are really important.
So you can scan the data off of the actual policy document off the PDF attachment and reconcile that that's in the system. That's awesome.
Yeah, that PDF document is generally the best source of truth for all of this data. So pulling the data off those PDFs is a pretty important functionality from those bots to know that the data they're interacting with is actually the right data.
Yeah. I mean, it takes us to the, you know, it brings up the whole clean data thing, right? I mean, you think about how much information, you know, and we find this a lot, you know, when you're prospecting and you're filling in information based on what they're saying or what's happening, and then you turn that prospect into a client.
Well, sometimes in that process, the data actually changes, right? You go from like, hey, yeah, I make $4 million in revenue to, you know, really I make, you know, $3,675,000. And that's what the policy is actually rated off of.
And if you still have the 4 million in when you were just prospecting and putting information in, well, now you have a mismatch in data, your policy is rated on the actual PDF at, you know, 3.6 million, but you have 4 million in your management system. Now, if you try to go re-rate on a rewrite or whatever, and now there's this mishmash of data and the ability to say like, okay, the PDF is source of truth, you know, match that up against what's actually in your agency management system and see where there's discrepancies and then fill in any missing data.
You know, that whole argument around clean data, that's, you know, this, this to me, you know, and looking at where business is going, um, having, you know, I'm doing air quotes, although no one can see me clean data. Um, this feels like a major, major, uh, part of success moving forward.
A major, a major aspect of a successful agency is, is, is as complete a data set as you can have, you know, relatively speaking and having that be accurate and, and clean is just, otherwise what can you do? You can't do these automations if everything's a mess. Yeah.
And ultimately like you hear it all the time, it's garbage in, garbage out. Right.
And what we found from, from working with agencies, like agencies assets are our data. All they do all day is process information.
You get information from carriers, you process that information, you give that information back to a customer. The core competency, one core competency is really processing data.
And in order to do that as effectively as possible, you need that data to be as accurate as you can. Yeah.
So during that, how do you, and again, this question might be a little remedial, but there's a lot of non-technical people like me out there. So we implement the daily download bot, right? So we, boom, we decide based on what we need.
That's what we need. Okay.
We implement it. That buildup phase where there's going to be some learning, there's going to be some error.
What does that look like? How does it work? Because what I'm trying to do is take the scary out of this for people so that, you know, if this is something they're interested in, they'll start to consider options like Quandary or, you know, someone else if that's what they want to do. But when they start to consider this option, the scary parts for me are one, I have no idea what this is, right? It seems something very ethereal.
Okay, we talked about that. It's a robot.
It's a piece of code. Acts very much like a human except for, you know, it can do these repeatable tasks.
You gave us the rules-based, high-volume, repetitive, digital. That makes sense to me.
Okay. I can kind of feel what that is.
And downloads are definitely a pain point for me. Okay.
I can see how this could really help me. I think the next question a lot of people have is, well, is this going to be a nightmare once I get it going? Am I all of a sudden going to have more work for a period of time? Or am I now going to have to task an entire employee to this to make sure it's running correctly? Like, what is that buildup time to say acceptable accuracy? You know, nine, you said 99% plus accuracy.
What is the, what is the buildup time and what does the work look like during that period for the agency themselves? Are they getting a lot of call out reports or how does all that work? Yeah. So a few, a few different things.
So when we first go to actually scope this and put a bot in place, we'll work with with an agency's team. And usually the person is always one person who knows that process the best, right? It's usually the person actually doing that process.
So we work really closely with them to understand specifically their process, how it might differ from another agency, what other exceptions or differences they might have, and understand all of that before we actually put the piece of software in place. So we spend a lot of time upfront with our team.
Once we do that, we do a phase of what's called supervised deployment. So supervised deployment is when we put a robot

into your system, we first show you what that robot is doing so you can actually see the robot running. And once we do that, your bot is going to be processing data, but it's not actually going to be making any changes in your live system.
So it'll be processing data, one of our engineers will be watching everything that it's doing and the outputs that come out of that, we ask the agency's team to review all of those outputs during that supervised deployment period. We do that for, it depends on the robot, anywhere from one week to potentially three weeks, depending on how complex the data set is.
But your team's reviewing all of those outputs. So by the time it moves to an autonomous deployment, you've reviewed all of that.
Your team is really confident with the results. All those results are exactly what they need to be.
And we're only deploying it once we have sign off from our engineering team, our QA team, and also your team that those outputs are exactly what you want. End to end, it usually takes, we recommend people set aside about one, one entire day of work.

So about eight hours in order to validate the outputs and work with us on the upfront process.

So it does require somebody on your team to work with us on it, but it's not extremely laborious.

It doesn't take a lot of time in order to do that because we take a lot of that work on our side.

On an ongoing basis, we have internal spot checks on our side. So we have QA testing that we run to make sure the bot is doing what it's supposed to be doing.
And there's a daily report that comes out from the robot, usually to the person who is responsible for this process previously. So once that robot is done, the run for the day, it will send out a report saying, you know, hey, Ryan, finished your download today, I was effectively able to process 95% of all of these items.
Here are the remaining 5% that still need to be managed by a human. Person goes in, looks at that remaining 5%, they process those quickly, and then do that on a daily basis.
So it still does require a person to be involved for a very small percentage of them, but a person doesn't need to monitor it. They don't need to start it.
They don't need to stop it. It's going to run at a set time process, all that information every day and just notify your team.
Let's complete that. You know, I know you guys are just getting started.
So you probably, you know, I'm assuming you don't have hundreds of deployments out there, but I'm sure when you're talking to people, you know, let me back up. The guy that launched QQ Solutions, which is now owned by Vertifor, his name was Mark.
And I remember, you know, this is probably a decade ago. He was doing the rounds.
And when I was working at trustedchoice.com, I got to know him pretty well and a couple of his people. He was a good guy.
And one of the things that he said, I asked him, i asked him a question like when i was looking at qq because at the time you know qq hasn't really moved forward very much i mean they've done some some some improvements recently which is which is good to see but like for a while it didn't really move forward but at the time when qq was first launched it was like revolutionary it was like you were looking at the future of agency management systems it was slick fast you, it made sense visually at the time and all that. And I remember looking at it going, Mark, like how come you are not in every agency under 20 people in the country? Like if I just look at this, it just immediately makes sense to me, all these things.
And he said, he goes, he goes, when we first started selling this, one of our pitches was, um, that you could never have to hire another CSR or this would replace one or two of your CSRs. Like he had something like that, like basically like, and he said the reason he goes, we struggled at the beginning because the people whose jobs would be in jeopardy because these tasks, you know, certain tasks wouldn't need to be done, certain like connections and whatever.
He goes, they would immediately find every reason why this thing was going to blow up and wasn't going to work. And he's like, and, you know, we had to work around and find ways to say like, no, we're not recommending anyone to get fired or whatever there's, you know, that kind of thing.
So I'm sure you guys run into something similar where, you know, if I'm the person who does all

these tasks and that's my job, and now all of a sudden, you know, there's these bots that are

going to quasi replace me. Do you get pushback? Do you find internal people look at this and start

questioning it? Or are you not dealing with them? Or you just haven't experienced that yet? Because

I can see that in your future as you start start to gain traction, and more and more people know what's coming when the boss is meeting with the digital worker team, you know what I mean? Like, I can see them getting a little bristly. Yeah, we definitely we definitely have dealt with that on a on a smaller scale.
Find that typically we'll go go into these organizations. And usually it's that it's that person, right? The subject matter expert, the person doing the downloads or doing the renewal reviews that has a lot of doubt as to whether this robot can actually do the process.
We actually, we usually spend time with that person upfront, really showing them what the bots can do, how they work, how we train them and how we work with their team to make sure we get it exactly right for their organization. And ultimately, we need to build trust, not just with the person who's buying the software, making the decision, but we need to build trust with the rest of the organization because they're going to be the ones using it.
They're going to be the one actually interacting with the bot. So we spend time upfront to show them how these bots work and explain how we get it exactly right for that organization.
I think the second part to the question is the kind of replacing part of it, right? And what we've found is that across brokerages or agencies all across Canada, all across the US, and really across every other industry, people can't hire enough staff. Like it doesn't really matter what business you're in, except tech right now, which is which is laying people off, but you cannot hire enough qualified and trained staff and retain them on an ongoing basis.
People need more labor to continue to maintain their business and continue to grow this. And what these robots allow you to do is take staff who've been with you for a long time, who are well-trained, who know your business, who know insurance, and you can put them on much higher value processes instead of needing to hire other people and put them in your business.
So a bot eliminates the need to hire and train new people, but we've never seen somebody actually lay somebody off because they probably have four or five open positions in their, in their agency, if not more. Yeah.
Yeah. I, I, I think that's, that's a great way of getting after it.
So what is the, you know, beyond, beyond building, you know, productized robots for, for additional process inside the agency, which is a huge task. So I'm not diminishing that particular task.
What's on the future for you guys? I mean, what excites you beyond? I mean, obviously, you've figured something out. And I think, you know, to me, I think, like I said before, not to knock virtual assistants, I think virtual assistants are great.
This to me very much feels much more like the future of how these tasks are handled. I think virtual assistants will always be a part.
But outside of like the standard licensed American service or sales professional inside an agency, this feels much more like the future of automated work in a human optimized agency than more humans that work for cheaper in other parts of the world. While I think they're very valuable.
So that's not to knock virtual assistants. This just feels like the jump to me.
So in that vein, thinking not maybe like the next year or two, as you build out more of these specific processes, like, where do you see a tool, a service, the technology, the, the, the gathered

machine learning that you're, that you're putting together these different algorithms

and stuff.

Like, where do you see this ultimately going?

Like when you're, you got a couple of pops in you and you're kind of sitting there fantasizing

about what, what this can do in the future.

Like, what does that look like?

If you're, if you're willing to share, like, what are some of the crazy ideas, the crazy problems you think you could solve with what you're building here? Jameson, you want to take this one? Yeah, I think it's something that's becoming more apparent every day as we do this, is that a of people myself included you think of you think of a future and you have you have robots walking around with people every day doing tasks and just living within living within society um but what we've seen as everybody knows in the last couple of years is just this huge shift to everything being online work being online like all of the work i do at quandary is all done from my computer for the most part and so i think what's becoming more apparent is that there is a future where those two things merge uh where there are robots that are doing certain things within this society we live in. But what's much closer than having that be in the physical world is having it be in the digital world.
And so for me, for us, I think what is really exciting is just pushing the limits of what we ourselves and what everybody else thinks that these robots can do. There is so much within the insurance industry alone that we are going to sit and focus on for quite a while because there's so much value to add.
But we want to just continually push

both what processes the arts are working on,

but also just the complexity of what these bots can do.

And ultimately, we want to push into many other areas.

Healthcare is an example where this can have

a lot of really massive impacts on people's everyday lives

and essentially push the boundaries of what these bots can do. Yeah.
So, well, cool. I think that's really good.
I think that, like I said, I think this is the jump. I think that, like I said, I think that other humans outside the country who just work for less money is a good way to add scale to your workforce for a lower price point.
But to really optimize your business, to systematize your business and to put it into that next gear, which allows the people you have to be as efficient and effective as they can be. It seems like a tool like a bot that's able to learn over time through ML or machine learning is going to be way more effective, way more easy to manage, control, make adjustments when necessary.
Ultimately, the hope is you don't have to QA it as much that you, you know, you have the ability, you know, once it's kind of humming, you can not necessarily set and forget it, but, but, but really give it the limited brain cycles that you would hope some of these process would have. So I think the work you're doing is awesome guys.
I think it's, um, I wanted to have you on cause I wanted to just shed more light on, on this particular topic. These, these digital, these digital, uh, digital workers is how you, is how you guys explain them.
I think that, um, you know, I've talked about VAs a lot on, on the podcast. And I, and like I said, I still believe in VAs that's, that's not a knock on VAs in any way, but to me that the true optimization is going to come from jumps like this, jumping past cheaper humans to technology that helps you, that helps augment what you do and to do it faster and more effectively.
And all in the agency management systems being a bugaboo for most of the people who are listening to this show, their agency is in some way, and the data in that agency management system is in some way a bugaboo for them. It seems like what you guys are doing seems like something that people should consider.
So if that's the case, how do people get at you guys? Is it LinkedIn? Is it the website? What's the best way to reach out? Are you taking new customers is probably a good question to ask. what's going on? How do people take the next step if they're interested? Yeah, so we're definitely taking new customers.
We do have a bit of a wait list right now, because we have a lot of customers that want to sign up. So we're building up to be able to handle all of that.
So it does take a couple of months for us to be able to kick off a new customer. But if anybody's interested, they can go to our website, they can book a meeting directly with me off of our website,

or send me a message on LinkedIn. And we can get in touch.
Yeah, talk to people as soon as possible.

Yeah. And that website is qandri.io.
So quandry.io. I'll have the links in the show notes show notes if you're listening on the show notes or if you just go to my website and you forget it um or just type in uh q u a n d r i insurance and it comes up too i think that's what i typed in to find your find your website um but you know our good friend jeff roy is using you guys um a couple other, uh, friends of the show are at least in talks, if not current customers are moving forward in the process.
Um, I'm excited about what you guys are doing and hopefully, uh, we can come back in the future and talk about all the cool, crazy shit you guys are working on again. Yeah.
Awesome. Ryan.
Thanks. Thanks a lot for having us.
It was really nice to meet you. I enjoyed the conversation today.
Yeah, thanks so much.

Yeah, awesome being on the show.

Thanks a lot, Ryan.

Great to meet you.

Thanks, guys.

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