Variance AI: Trust & Safety Automation with Autonomous Agents

Mar 31, 2026

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3 min read

YouTube video ID: JF6XIixstmQ

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Variance announced a $21 million Series A round and emerged from three years of stealth development. The company’s core function is to automate content, fraud, and identity reviews at scale for Fortune 500 marketplaces such as GoFundMe, Medium, and Redbubble. By handling risk and compliance tasks automatically, Variance enables platforms to validate user‑generated requests—like a GoFundMe fundraiser—before they go live.

Technical Approach

The platform replaces deterministic, rule‑based systems and human‑in‑the‑loop workflows with autonomous AI agents. These agents can read standard operating procedures (SOPs) and make decisions without relying on specialized classifiers. Data integration is achieved through reverse ETL, APIs, and browser‑based scraping that mimics a human analyst interacting with legacy internal dashboards. Each customer’s environment may involve five to ten disparate systems, producing petabytes of unstructured data that the agents ingest, reason over, and act upon.

A key innovation is the “self‑healing” capability: the AI agents close the feedback loop by materializing new features on the fly, querying data stores, and deciding the next tool call without human intervention. This contrasts with static rules engines that require manual updates whenever fraud patterns evolve.

Operational Philosophy

Variance runs a lean team of 12 people, including five software engineers. Engineers employ AI coding agents to produce the output of a 25‑person engineering effort, a claim summed up in the quote, “I think in terms of software output, we're probably closer to a 25 people team.” Non‑technical staff such as Customer Success can ship features autonomously using the same AI coding tools.

The company deliberately stays “in the shadows.” As one founder put it, “We're building the systems that are often used by the bad guys, but we're building them for the good guys.” By avoiding public marketing of specific use cases, Variance reduces the risk of teaching bad actors how to bypass its detection methods.

Origin & Resilience

Founders Karine and Michael first met on Apple’s centralized fraud engineering team. Their initial customer was IAC, which needed to scale marketing content compliance for brands like Care.com and Ask Media Group. The venture was built before the rise of ChatGPT and has evolved alongside rapid LLM advancements.

The founders have faced significant challenges, including a “bus factor of one” for the CEO and a serious personal injury in July 2024 when the CEO was hit by a truck, breaking his spine and leg and requiring a ten‑day hospitalization. Despite these setbacks, the team’s strong sense of duty and product focus has persisted, underscoring the founders’ resilience.

Mechanisms in Action

In a typical fraud detection workflow, AI agents ingest user identity data, login behavior, device history, and fundraiser details. They compare this information against the platform’s Terms of Service to determine legitimacy, flagging cases such as fake family members created after a high‑profile death.

Data scraping is handled by launching browser instances that interact with internal dashboards exactly as a human analyst would, then extracting and reasoning over the hidden data. This approach enables the system to process “pabytes” of unstructured information across multiple sources for each client.

Takeaways

Variance raised $21 million Series A and emerged from three years of stealth to offer AI‑driven automation for risk, compliance, fraud and content moderation for Fortune 500 marketplaces.
The platform replaces rule‑based, human‑in‑the‑loop workflows with self‑healing autonomous AI agents that can read SOPs, scrape unstructured data from legacy UIs, and make real‑time decisions without specialized classifiers.
By operating “in the shadows,” Variance avoids publicizing detection methods that could teach bad actors, keeping its technology concealed while scaling across petabytes of data from multiple systems per client.
A lean team of 12, including five engineers, leverages AI coding agents to achieve the output of a 25‑person engineering effort, allowing non‑technical staff to ship features autonomously.
Despite personal setbacks—including the CEO’s severe injury in July 2024—the founders’ resilience and deep fraud‑engineering experience from Apple sustain the company’s mission and product focus.

Frequently Asked Questions

How do Variance’s autonomous AI agents replace traditional rule‑based fraud detection?

The agents ingest identity data, login behavior, device history and fundraiser details, then read the platform’s SOPs to evaluate compliance, automatically querying data stores and executing tool calls without human oversight, eliminating the need for static rules or separate classifiers.

Why does Variance operate “in the shadows” rather than marketing its use cases?

Keeping the technology hidden prevents fraudsters from learning the detection methods, ensuring the AI agents remain effective; publicizing specific use cases could inadvertently teach bad actors how to bypass security, so the company stays low‑profile while scaling.

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Helpful resources related to this video

If you want to practice or explore the concepts discussed in the video, these commonly used tools may help.

Books On Building Resilient Startup Teams Recommended

Provides frameworks for managing small, high-output teams and maintaining founder resilience, which is central to the company's operational philosophy.

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Ergonomic Office Chair For Long Hours

Supports the physical health and comfort of founders and engineers working long hours in high-intensity, high-ownership startup environments.

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Books On Artificial Intelligence And Ethics

Explores the broader implications of AI in trust, safety, and decision-making, aligning with the company's mission to automate complex compliance.

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Full Transcript YouTube

So, I'm particularly excited to do this
Founder Fireside because we have a first
for the Founder Fireside series. We are
doing a big announcement on this
podcast. We are announcing that Variance
is coming out of Stealth and announcing
their $21 million series A. And I'm
really excited to be here with Karine,
the co-founder of Variance. Thank you so
much for joining us, Karine. Thank you.
>> So, this is a big day. You're coming out
of stealth after building basically in
stealth for the past 3 years and you
built Variance quietly into this big
company that powers a lot of the
products that everybody knows and uses
in their regular life. Can you tell us
about what Variance is and about the $21
million series A you're announcing?
>> So, Variance is building purpose-built
AI agents for risk and compliance. We
automate content review, fraud reviews,
identity reviews at scale. We're
powering some of the largest companies
in the world's Fortune 500s
marketplaces. We've been working with
GoFundMe, for instance, to review all of
their fundraisers at scale. Some
Fortune50 for verifying all of their
sellers and complex UBO verifications.
And today coming out of stealth
>> and you guys built in stealth for a long
time like 3 years and you have a lot of
customers that you can't even name on
this on this podcast. Why all the
secrecy around this
>> variance usually deals with really
sensitive data and sensitive issues. And
the phrase I like to use is that we're
building the systems that are often used
by the bad guys, but we're building them
for the good guys. So oftent times it's
really hard to market the use cases that
customers are using varants for because
those issues are so sensitive. And if we
were to market those then it may create
more fraud. It may create more abuse. It
may create more bad
>> like your your customers are stuck in
this constant cat and mouse game with
the bad guys and you're their secret
weapon. They don't want anyone to know
what their secret weapon is.
>> Exactly. We're in the shadows. I think
it's it's it's good. It's really
impactful for our employees working at
the company. Really impactful. You get
to see that when you work at Variance,
but I think even far beyond the series A
will always be a company that's a little
bit more in the shadows. And I think
that's that's okay for us. What's cool
about Variance is that even though
probably no one who's watching this
video has ever actually used the product
themselves,
they have used many products that under
the hood are actually powered by what
you've built.
>> And so indirectly they're actually like
using stuff built on top of variants
like a lot of like software
infrastructure kind of products. Could
you maybe tell people about a a blog, a
specific product that they've used
that's or have probably used that's
powered by varants and how it works?
>> One of the customers, GoFundMe is a
platform that you can use in order to
build your own fundraisers. And GoFundMe
is effectively a payments platform. And
GoFundMe actually has some very strict
compliance requirements because they are
liable for facilitating payments to
let's say an organization that they
shouldn't have done so. So variance is
used to verify for instance if a
fundraiser is going to be built for a
military operation for instance or if
you're building a fundraiser for um a
crisis that you were not part of which
would be fraudulent well GoFundMe sort
of has the responsibility to be able to
detect this they're using AI agents so
variance AI agents to conduct those
investigations so make sure that the
money is going to the person that they
say they are and also make sure that the
money is not going to be funneled to
sanctioned countries for instance or be
going to um anything that could be a
compliance risk for them.
>> You were telling me earlier about like
one specific very concrete form of
fraud. What does like typical fraud on
GoFundMe look like?
>> Yes. So um GoFundMe is actually very
crisis driven. So if there is any sort
of large event or any sort of natural
disaster, there's usually going to be a
spike in fundraisers that occur on
GoFundMe. It's really hard for the team
to keep up with how many of these
fundraisers are actually going to be
real fundraisers for that event or
possibly fraudulent. One of the example
that we saw was I think recently there
was for instance the the murder of
Charlie Kirk. There was a spike of
fundraisers for the family of Charlie
Kirk. How do you know who's actually
related to Charlie Kirk and who's just
trying to raise money for their
>> Okay, so like Charlie Kirk dies.
>> Yes. And then what happens is like a
hundred people host a fundraiser on
GoFundMe like claiming to be a family
member of Charlie Kirk.
>> Exactly.
>> And it's mostly just like total
fraudsters who are just hoping that some
people like donate to their Charlie Kirk
fundraiser instead of the real Charlie
Kirk family fundraiser.
>> Exactly.
>> And you're in charge of figuring out
which one is the real Charlie Kirk
family and who are the likeund
fraudsters.
>> Exactly. And there's a lot of behavioral
signals you can use to do so. Um, you
have information on the identity, you
have information on what that account
has done in the past. You also have
information at the fundraiser level, so
the image, the bio, and such. So, the
variance AI agents are going to be using
all of that context and use the GoFundMe
terms of services to basically decide
whether or not that should be allowed on
the platform. That work used to be done
by human analysts and now can be fully
automated in a much more consistent
manner. Every person who signs up to do
a GoFundMe fundraiser, they don't
realize it, but actually their request
is being validated by variance of
software before it's allowed to go live.
>> Yes, exactly.
>> What are other like products that people
use that also are powered by variants
and they don't even realize it?
>> So, the scale has been um impressive the
number of use cases that our agents can
be used for. So we have been running
complex identity reviews for
marketplaces, gig economy platforms. So
when you sign up to actually be for
instance a delivery driver, your
identity needs to be verified based on
selfies, based on your driver's license.
All of that data is then going to be
reasoned on by our AI agents and then
it's also going to be validated based on
the company's standard operating
procedures. That's a good example of how
variance is used. were also used for
complex what we call KYB verifications.
So if you sign up to do any sort of
business online whether it's for a
marketplace or also to with a financial
institution they have the compliance
requirement to verify that you are
actually linked to the business you say
you you own. So a good example is for
example I sign up to say I'm going to be
doing business with varants. Well, the
legal name of my company is Decoy
Technologies and it is tied to Korean
Mulada. That's a simple example, but
building that graph at scale is really
hard. And oftent times you're going to
see company have multiple shell
companies be tied to multiple different
agents and different other identities.
And within that really large graph, you
expand the area of risk for the company
where one of these nodes could be in a
sanctioned country. One of these nodes
could have adverse media on them and
possibly have been to court for money
laundering and companies are required to
conduct these investigations at scale
and at the moment it's entirely manual.
>> Do you license data from a bunch of
different sources? Do you like scrape
all these government websites? Yes, but
to take a step back, I think it's really
elegant how AI agents are built at the
moment. There's really only three
building blocks that you need. So you
have the compliance documents, the
standard operating procedure. So what
the company deems as necessary to verify
at the on boarding level or at any other
parts of the life cycle of that entity.
Once we have those compliance documents,
then the AI agent can do its work using
tools that we built and data internal or
external. Those are the only building
blocks you need to automate complex KYC,
complex KYB, complex content review. The
question you're asking about data is
interesting. Usually, it's going to be a
split between internal customer data.
So, variance is really good at
connecting to all data sources, pooling
unstructured data into our own data
stores. And the second one is external
data. So we do have access to over
hundreds business registries across the
world which makes us international and
then our agents also have access to the
open web. So a lot of unstructured data
is found directly on the web. I actually
think interestingly enough that access
to the web was one of the final nodes
that made this whole problem really hard
to automate.
>> Yes. Because like the human analyst, a
big part of what they would do is they
would go and they would like Google for
names and then they would like look at
what comes up and apply some judgment
like ah does this seem fishy or not.
>> Exactly.
>> Yeah.
>> When you think of a large graph of
abuse.
>> Yeah.
>> If one of these nodes, one of these
intelligence signal is found on the
unstructured or the open web, then
without having a human agent Google on
the web, you can't even trace back the
whole graph of abuse. What are some
other interesting technical challenges
that you guys have encountered and had
to solve in the course of making this
work
>> at scale? Um the data problem was was
really the core hardest technical
challenge. Usually with customers what
we will do is that we'll pull in pabytes
of data that are coming from vastly
different sources and all of that data
is unstructured. Doesn't have a schema.
unstructured, isn't it? Don't you just
like pull it like right do you pull like
out of their like relational database?
>> I I wish it was this this simple, but
usually I mean I'll give you a very
concrete example. So for instance, if
you need to verify a fundraiser, I'll
pick the fundraiser example. Okay,
>> usually the data is going to be
scattered around the user identity data.
So, you need to have information on the
user. You need to have information on
all of their login behaviors, the
devices that they've had, the PII that
they've onboarded at the beginning.
>> You need to also be able to pull in
information about the business. And then
you need to have also all of the
information on the fundraiser itself and
all the history of that fundraiser. What
has been hard is that often times
whether you're a financial institution
or you're a marketplace that data is
going to be scattered across five to 10
different systems. It's going to be into
different data stores. And one thing
that's been really interesting in that
is that sometimes that data is going to
be hidden behind a UI. So the only way
that the variance AI agents are able to
sort of scoop up that data and reason
over it is to be able to directly scrape
from a UI that was built for a human. So
the data piece and being able to scoop
up all of that data and bring it to
variance was one of the hardest
technical challenges. It's really an
onboarding one.
>> And so do you do you do that? Do you
have agents that interact with like the
customers like internal dashboards and
like pull data out of them?
>> Yes. So at the moment the way to
integrate with variance can be reverse
ETL API but very recently we've been
awarded this third way which is
>> spinning up a browser
>> opening up a really old review tool that
was built for a human
>> pulling that data and then reasoning
over it.
>> I guess it makes sense because like the
work was being done by a human before
and so like the human can use the
dashboard while the agent can use the
same dashboard.
>> Yes. Exactly.
>> Right. you worked on related systems at
Apple like prelims and now you're
building this like deeply agentic
system. Can you talk about how the
technology has evolved and like why it's
possible to solve this problem now?
>> Yeah, definitely. So, I think it's
important to sort of understand how
these problems were solved at scale
before. So, usually companies are going
to have a patchwork of different what I
call deterministic systems. So, they're
going to have rules that say, "Well, if
this transaction is over $1,000, then do
this." Um, they're going to have
classifiers that are really good at
detecting one specific flavor of abuse.
Then they're going to have humans.
>> And humans are really good at
understanding in context all of these
different nodes and signals and then
making a final decision. Mhm.
>> And I think when you think of a fraud
system, the most important feature of a
fraud system is that it needs to evolve
really rapidly and you need to have a
really tight feedback feedback loop. But
when you think of the three nodes I've
mentioned, rules engine, classifiers,
and humans, humans being really slow
oftent times and and a little bit
inconsistent, that feedback loop can
only be so fast. And we never really
felt like you could achieve a
self-healing system that could thrive in
a dynamic environment. And I think fraud
is the most dynamic environment because
you always have adversaries. So Michael
and I were always really, really, really
stubborn about making the system have no
nodes that were inefficient, if that
makes sense. And now you have AI agents
that are able to sort of close the loop
from a reliance and self-healing
standpoint. They're able to materialize
any features that a rules engine would
be able to materialize. You don't need a
classifier anymore because AI agents are
able to read set of standard operating
procedure and reason over an image or
reason over sort of unstructured data
and know that this is possibly
chargeback fraud. you don't need a
specialized classifier for it. Um, and
you don't need human reasoning anymore.
So, you have this fully self-healing
system which at scale is really
transformative and allows companies to
be able to ship faster, open new product
lines cuz they don't have this sort of
bottleneck.
>> Do you have an example of something like
that with one of your customers where
the system was able to do something that
just like no human team of fraud
analysts could ever have been able to
do? Yeah, one customer specifically uh
and I think that fraud pattern came to
be during the elections. Um we had one
customer that is processing a lot of
content and they're also there are
Fortune 500. They're hosting large
communities and they're also fairly
politically exposed and throughout the
elections because our AI agents had
access to the context of entities in
relation to other entities. So, how does
this user fit into all of the other
users that we're looking at? We were
able to detect really complex fraud
rings of especially state sponsored
actors that were pushing one narrative
over. And I don't think this would have
been possible if you had one classifier
in isolation that was looking at one
piece of content after the other. But
because AI agents are able to directly
query our data stores, they're able to
materialize features on the fly and
they're also able to use one step to
reason over what should be the next step
and the next tool call that they make.
We were able to detect much more
sophisticated fraud rings than you would
have been able to do before.
>> There's also like a pretty interesting
implication of variance. like you're
actually able to like detect
misinformation online and like improve
the political discourse. What a what a
like cool impact to have on the world.
>> Yeah, it's been uh it's been high
responsibility. Um but no, it's it's
been really interesting. I think um some
of the abuse vectors we've been able to
detect have had really serious physical
implications. So people that are making
threats online of physical harm have a
plan to do these things at scale. Um.
>> Wow. So you might have actually like
prevented physical violence from
happening in the world by detecting it
early.
>> Yes. Exactly. Yes. So at scale some of
these investigations can lead to finding
things that are really scary and at the
end of the day once it's detected and
investigated by varants it's usually
going to be in the hands of law
enforcement. But seeing that impact at
scale is really interesting. This sounds
like a lot of software to build. How how
big is the team now?
>> So, we're 12. Um, we have five software
engineers. So, we've been we've remained
very very lean. We have
>> five software engineers building all of
this.
>> Yes.
>> Are you hiring more?
>> We're hiring more. So, we're hiring
across the board. Um, we're hiring for
back end. We're hiring as well for front
end, which I think Michael and I hadn't
realized was such an important part of
the product. If I can give you a little
bit more context, I think when Michael
and I first started the company, we were
really stubborn about building a full
endto-end decisioning layer. So, we
thought we needed to get really good at
making really precise decisions, but at
the end of the day, we could be treated
like an API call. We were not right
about that. Um, what we found out is
that because AI agents are able to take
on the simplest part of the workflow, so
they're able to triage 99% of of cases,
that 1% is usually going to be the most
complex cases that need to be reviewed
by a human. And you need a really good
dashboard. You need a really good
investigative visual tool to be able to
make sense of those super complex use
cases.
>> What's it like working at Variance?
What's what's cool about working there?
It's a very strong ownership culture.
Um, with five engineers and we're
processing pabytes of data. We're making
decisions in a fully automated manner
for some of the largest companies in the
world. So, I think just for context, we
have 2x founders on the team. Um,
everyone has a really strong ownership
culture and really feels like they have
agency over every single part of the
company. So, on a day-to-day basis,
we're in person in San Francisco every
day. We're very, very product focused.
Both Michael and I are engineers of
course. So it's a very very high
ownership but also high collaboration
workplace and there's never really a
time where Michael and I sort of dictate
to the team what there is to build. It's
really more going to be we give a
problem to an engineer and then they
just take it and run with it. And at the
end of the day, it's been really
fascinating because I can sort of
proudly say that there's people on the
team that understand certain areas of
the business better than I do. For
instance, Luke on the team was one of
the first engineers at meter and he
understands eval for large language
models better than anyone else on the
team and even I think better than than a
lot of people in the industry. So we
sort of have this culture where we get
to learn a lot from our engineers and
because the team is so small they get to
be a really large part of the success of
variance
>> and I think that's something that's been
possible now that we have coding agents.
>> Yeah. Are you guys AI coding
maximalists?
I would say we we we pretty much are at
this point. Um, everyone on the team is
usually going to be almost like a
manager of their own their own small
team, which is really interesting. I
would say we're five, but I think in
terms of software output, we're probably
closer to a 25 people team. So, every
engineer is going to have three monitors
with their coding agents running. Okay.
>> Um, we still have, you know, good
oversight. We still review all of the
PRs, but I think in terms of output,
everyone is a manager of a small team of
AI agents, which is really interesting.
And I think one other really interesting
anecdote is that our customer success
manager who's entirely non-technical but
interfaces with enterprise customers on
a day-to-day basis now gets to take on
feature requests especially the simple
ones directly give them to cursor a
cursor agent and then directly be able
to ship features in a fully autonomous
manner get back to the customer a few
hours later and say oh it's it's shipped
and she didn't even need to speak to the
engineering team. Wa. That's awesome.
Maybe let's change gears here and talk a
little bit about the the origin story.
How did you and Michael end up starting
this company? How did you end up working
on this problem?
>> So, Michael and I both met, we were
co-workers at Apple. Um, we were both
engineers on the fraud engineering team
and I was a data engineer. Michael was a
machine learning engineer. And it was
really interesting because the team in
and of itself was sort of the broad
engineering as a service team of Apple.
We were providing our services to the
iMessage team, the iCloud team, and we
were this centralized fraud team. And
Michael's machine learning decisions
were then dispatched to the rest of the
organization through my own streaming
jobs.
>> So, we had a very sort of symbiotic
relationship uh from the get-go. We knew
that we worked really well together. I
remember telling Michael, "Oh, well,
what if the right vehicle for this
product was a company?" And I told
Michael, "Oh, we should apply to our
combinator." That was sort of the origin
story. I think it really started from
the product. We really, really wanted to
just see this product exist. Um, and we
wanted to see that problem that we were
solving at Apple be solved in a much
more efficient way, in a much more
self-healing and resilient way.
>> How did you crack it? How did you
convince the first customer to take a
big bet on you? I think your first
customer really believes in the founders
first. They believe in the founders's
ability to be able to solve their
problem because at the end of the day
when you start enterprise, you do have a
version of your product, but it's going
to evolve so much based on your first
customer's requirements.
>> So there was a belief and a trust in
Michael and I that we understood their
problem well enough to then translate
that into a software platform. It really
started with that. And then I think the
second thing that was really important
is that we needed to land on a problem
space which has evolved a lot which was
on fire.
>> It needed to be on fire because what we
found is that if it wasn't on fire then
>> there was no reason to go and trust this
really small startup that had no real
proof points behind them.
>> Who was the first customer and what was
the burning pain point that got them to
be willing to do this?
>> Yeah. So, our first customer was uh a
company called IA. They're a publicly
traded company.
>> IA folks might not have know of IA
because sort of like a parent holding
company, but I'll bet they know a bunch
of the IA brands like understand the IC
brands that everybody knows.
>> Yes. So, I I brands I mean I has
care.com and we were working with Ask
Media Group. Okay. which had a very
large amount of marketing content and
because IC is a large publicly traded
company there was a lot of compliance
requirements around what could go into
their marketing content. So we basically
used the platform that we had built to
review content at scale to then review
their own marketing content. And I think
what was really interesting is that one
that problem was entirely solved using
human agents because those compliance
guidelines are really hard to map to a
traditional classifier. You can't give
advice for legal defense for instance.
It's really hard to map to
>> try writing a regular expression for
that.
>> Exactly. So it was it was
semi-impossible. Also was a very large
team of human agents uh part of a BO
outsourced that was doing this work and
that was basically hurting their growth.
There was less marketing content that
they could put out in the world because
they were not it's really hard to scale
a team of human agents and human
moderators. So they knew sort of had an
intuition that that could be done with
large language models. But we were the
first company to say hey we can actually
do that with large language models. We
were a small startup. I think that
happened maybe
>> this was at the very beginning because
you guys actually started variance
prehat GBT like just a little bit before
chat GBT right.
>> Yeah.
>> And so you were you were right at the
very earliest waves of companies that
were figuring out out how to use LMS to
automate these previously unautomatable
tasks.
>> Yes. Exactly. Um, I think GPT4 came out
during the batch. Yeah.
>> Which was really interesting. And as we
were running this pilot for this first
customer,
sort of OpenAI was coming out with new
models in the middle of the pilot, which
was changing one our cost structure by a
10x factor, but also was changing our
performance quite a lot. So, it was
really interesting to build in this
world that was super dynamic.
>> Okay. So the the the first big battle
was like getting the first customer.
Took eight months to land IAC. You
really did it the hard way because you
went enterprise from the from the very
beginning. Have there been any other
hard like challenges of building this
company.
>> I think starting a company tests you in
a lot of different ways. Um and we have
a really interesting story. You know
after I we got to on board a lot of
great customers in the trust and safety
space. um, Medium, of course, GoFundMe,
Redbubble, and around July 2024 was one
of the times where the company was
growing rapidly. We were on boarding
more and more enterprise use cases. I
think during that month, our revenue was
doubling within the month and then
doubling the month after. It was really
exciting, very step function because
it's enterprises and we had just wrapped
up one of the largest trust and safety
conferences uh called Trustcon in in San
Francisco and I think a couple I want to
say the day after we had worked so hard
for this conference 12 hours a day we
were super tired um I was going back to
the office on a Sunday afternoon um and
I was on the bike lane and a truck hit
me. That was a really really crazy
experience to to go through as a
founder. I mean, the company was doing
well, but at the end of the day, we were
a 10 people team, and the CEO just gets
hit by a truck.
>> How badly were you injured?
>> Um, so I broke my spine, broke my leg,
and I was hospitalized for about 10
days. I couldn't walk for about 10 days.
>> Whoa.
>> For a year and a half, as a founder,
you're moving so fast. You're working
every day. I had never I don't think we
ever took days off. Even if you do,
you're you're still pretty much on. So
for a year and a half, you're working
super hard and then all of a sudden as a
founder, you're in a bed and you can't
move. 3 or 4 days after all of that
happened, Michael came to visit me in
the hospital. And of course, Michael was
super anxious. One for my health, but
also trying to understand what that even
meant, right? Like the
>> the CEO has a bust factor of one. Yeah,
>> we only have engineers and we have me
that's running all the sales and the
customer relationship.
>> You were the it was all founder leled
sales. You were the only person doing
sales.
>> Yes. And and fully the rest of the team
was only engineers. And and Michael came
to visit me and he he brought I don't
know I vividly remember he brought this
Norman Foster book which was the
architect of of Apple Park um as a gift
to me and he was sort of sitting next to
my hospital bed holding the book. Um,
and we were both in silence because we
we didn't even know what to say, right?
It was silence for for a couple of
minutes. And he laughed and said, "Well,
this is going to make a really good
scene in our IPO movie."
>> And I was like, "Yeah, it's a good way
to view it." Um, we laughed about it,
but I think I was probably out of
commission for about a week and then two
or 3 weeks after that. And I think it it
it put a lot of stress on on the
company. There were a m a few moments
where Michael was wondering
if this was the end. He would tell me
and repeat to me the story of Steve
Wnjak who like went through the plane
crash and then left Apple and then um
went to go work at or went back to
Berkeley. So I think there was sort of a
a feeling that maybe this was going to
be the end of the company and maybe we
just needed to sort of part ways. there
was a really deep feeling that um it was
not the end. It's an interesting
challenge. So, sort of like a a hurdle
you need to go over, but it just doesn't
feel like the end at all. It feels like
there's so much more to come. And
I definitely felt that. I know Michael
felt that. And we got to got to walk
again. Now I can walk again. And and and
we got to learn that uh we definitely
need to scale me. so that so that
hopefully this doesn't happen again but
um we definitely yeah need to scale me.
>> One thing that strikes me about your
story is um the extent to which you guys
have had a very strong opinion from the
very beginning about what to build and
who to build it for. Like a lot of
founders come into YC, they have some
initial idea, but like it's just a
hypothesis and then they like pivot
through like many different ideas and
like new models drop and AI changes and
they like might change the thing that
they're working on like several times to
fit with whatever the like cool thing
is. And and you you guys have kind of
been the opposite of that. You came in
like day zero with a very strong opinion
of what to build. you'd seen the problem
firsthand and then the whole company has
just been like that initial hypothesis
playing out. I guess I don't know is is
that perhaps part of why it didn't seem
like it could be over yet because you
like really had to see it through?
>> Yes. And if we go back to why Michael
and I started the company, Michael and I
had a very specific pair of skill sets
in fraud and we understood what the
industry looked like. We had a lot of
issues with what the industry looked
like and how these problems were solved
at scale. But I think from the beginning
we always felt a really strong sense of
duty to put our very specific and quite
rare pair of skill sets to the good of
the industry. Mhm.
>> Um it was almost like a sense of duty
and to us it was never really about
starting a company for any problem sets
which I think some some founders are
really great at doing that don't get me
wrong but we didn't want to just start a
company for any problems. We wanted to
solve that problem
>> and we knew the technology was going to
evolve and and we were so lucky, right?
LMS got so good. Now we have agentic
systems, we have a agent harnesses that
are able to fully solve this problem end
to end. But we really wanted to solve
this problem and this strong sense of
duty I think is what kept Michael and I
going throughout the years and I also
think that's something that resonates
deeply with customers. So when they meet
us, they sort of see these founders that
are really deeply trying to solve the
problem that they're seeing on a
day-to-day basis. Once because they've
seen it before, but also two because
it's something that is doable if you put
enough care into building the right
engineering system in the right ways.
>> All right, I feel like that might be a
great note to end on. Thank you so much,
Karine.
>> Thank you.