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See more at Switchfly.com
Welcome to Travel Buddy,
presented by Switchfly.
In this podcast, we talk about all
things travel, rewards, and loyalty.
Let's get to it.
Brandon Giella: Hello and welcome
back to another episode of The Travel
Buddy podcast presented by Switch Fly.
I have with me as always the wonderful
Rachel Satow and Ian Andersen.
Welcome back to the show, and we also
have a special guest back for his
second time, Rob Neat Gman, who is
basically head of AI at Switch Fly.
you know all the things that there
is to know about tech and ai, and
loyalty programs at Switch Fly
and all that, and you're leading,
engineering teams in that regard.
So tell us a little bit about what
you guys are doing at Switch Fly.
Ravneet: first of all, Brandon.
Thanks for having me again.
pleasure to be here.
my title is Head of Data
Science and Machine Learning.
So essentially I hit two things.
one is.
try to drive data driven
decisions across the company.
and the other side is AI or more machine
learning, wherein I try to help with,
improving efficiencies or operations
side of things using, machine learning
as well as customer facing side,
more personalizing the whole shopping
experience to the degree possible.
Brandon Giella: Amazing.
Ravneet: That's, that, that's
at a high level, what I do.
Brandon Giella: So when it comes to
to
Ian Andersen: it's also
an extremely understated,
Brandon Giella: what I'm getting at.
Ian Andersen: explan.
Yeah.
Yeah.
Brandon Giella: Yes, exactly.
yeah.
So
when it comes to ai, you
are, you are one that is.
Literally thinking at a very macro level
and a very micro level where we're seeing
these trends, we're seeing things develop
across the way that companies are using
ai, particularly in the travel industry,
particularly for loyalty programs.
And then you're actually implementing
those models and those teams and building
product to serve those kinds of trends.
So that's what I want to get
across because generative AI
has been a huge topic trend.
For years.
It's a buzzword.
essentially everybody's an AI company
at this point, but you guys at
Switch Fly are actually doing pretty
amazing things with AI and the way
that you're rolling that into the
product and the customer experience.
And so since AI is such a buzzword, I
wanted to get really practical, like what
are the ways that you guys are actually
incorporating AI into your product?
How's that impacting customers?
How's that impacting travel
loyalty programs in general?
And then we'll talk a little bit
like future state and trends.
So first question, Rodney, what are the
ways that you guys are actually using
AI at Switchfly in real tangible ways
where you're bringing in these LLMs into
the product, into the customer journey?
What does that look like?
What, are things are you
guys developing there?
Ravneet: Sure.
I think ever since, l LMS became very
popular, my, my take has been, there
are three ways in which Gene AI helps.
One is productivity improvements, which
applies to every single, or almost
every single person who uses a computer.
And second way is more.
second and third are more time to market.
So if, I were to build a traditional
or machine learning model using
traditional approach, the lifecycle is
find the data, label it, build a model,
deploy it, where gen ai, that labeling
exercise can be done very quickly.
and so labeling and automation, The, time
to market that has sped up quite a bit.
And the last use case I like to think
of Gene AI is, more prompting or prompt
engineering, wherein the model
itself is the large language
models itself are so great.
You can give it some specific
instructions, and sometimes those
instructions could be 50 to a hundred
lines long or maybe even longer, but.
Really complex instructions and it
can do specific tasks for you, or you
can fine tune a large language model
to solve a specific problem for you.
so at a high level, three, three things.
And,
of course when I talk about the first
one, productivity improvement, if I
were to think from engineering point
of view, it helps us write code faster.
it helps us automate things like
say, say writing unit test cases.
if, I were a finance person, it can help
me do some data analysis and forecasting,
I could, upload a small Excel and ask
it questions on, on on predicting future
trends or identify some gaps in the data.
O one of my favorite ones on
productivity is, almost having a
PhD level expert available to you
that you can ask any questions and I
like to almost brainstorm ideas and try
to use that special specialist lens to
examine ideas from different perspectives.
Brandon Giella: Hmm.
Ravneet: But I think overall, I
think productivity improvement
is, valid across, any industry.
not just travel, but those are high level
use cases where we use, GenAI every day.
Brandon Giella: Awesome.
That's helpful.
Ian Andersen: Brandon, something I was
thinking about when we were getting
ready for this, I was, I went back
and looked at the podcast we did a
year ago, last July, with NY and,
Brandon Giella: This is episode 13, by
the way, last July, and we, it is called
AI and Machine learning and Travel.
So go take a listen to that.
Ian Andersen: and it's,
really fascinating to see,
I think at the time you and I
especially, we were still struggling
to get our heads around what exactly,
this kind of new AI world is like
and what it can be used for.
rev need, I think had, clearly a much,
much firmer, hold on, what it's been.
And I think over the last year, what's.
Really dawned on me is that I was
so much in a, thinking about it from
the user perspective, from the end
user perspective, what can I go do
in chat GPT or Claude or whatever.
Where I didn't fully grasp what
it, the ramifications on the sort
of enterprise side of things.
and I think that's where RevNet
has been, really focused on is that
before it even gets to the end user.
there's so many different
touch points that
AI can help speed things up and, sharpen,
sharpen the, accuracy and, productivity.
So is that, fair?
Neet?
Am I, starting to finally grasp
where you might've been 10 years ago
with this understanding in there?
Ravneet: I, I, think, fairly accurate.
I think when we look at the whole
business lifecycle, there are just
so many pieces that go together.
what Gene doesn't solve yet is
knowing where those pieces are
and building that, data
integration pipeline.
So that's still a manual task.
hopefully in, maybe not near
future, but some state and future.
those large language models
would be smart enough to.
And know how, to find
the right information.
but yeah, starting from operations
all the way to user experience,
there is still that, dirty data
problem that needs to be solved.
Finding the right data set,
giving it to the right model
and surfacing it to the user.
Ian Andersen: Because it, really is
just garbage and garbage out, right?
Ravneet: yeah.
De definitely, o over the past year, or
two models have definitely become faster.
They're, they have become more accurate.
they've become cheaper,
fewer hallucinations.
But some of the problems, when we talk
about production grade applications,
we don't want someone to think
they're booking a certain hotel.
And, but they're actually booking
a hotel across the street.
so
the accuracy
Brandon Giella: worst.
Ravneet: for sure.
so accuracy is, or importance of
a production grade application.
I think user impact is paramount and
that's what drives what features we feel
confident about rolling out to users.
Brandon Giella: So what I'm hearing is
AI is not going to immediately eliminate
all jobs for white collar workers.
fairly accurate?
Ian Andersen: Knock on
Ravneet: I, do, think it
is a few steps away, until.
Brandon Giella: Okay.
Ian Andersen: we talked a lot last
time too about, and you've helped us
out with, we've written some, blog
articles and some other, stuff on.
Like user data within the system
and privacy and, transparency
of how we're using ai.
one, one question I've had, and, Rachel,
I'm sorry I'm totally monopolizing
all this, but one question I've is,
regulation globally still
seems to be an issue, right?
Everybody's struggling with where
this is going and how do we.
Put guardrails around it and how are you
thinking about the privacy piece and the
transparency and that aspect of it when
so much of that is still up in the air?
Ravneet: For sure.
I think at the heart of all we build
at Switch fly is, I try to put privacy
and explainability first, as in.
Any feature we build.
So for example, we have this feature
called AI Destination Recommender,
where a person can say, find me
cities that have a beach, or cities
known for culture and history.
When we make a recommendation,
we give an explanation of why
we are recommending something.
if it's the first ranked city, why is
that better than the second or third?
So we provide explanations.
similarly if, explainability goes
beyond Gen ai, so any recommendation
to a user, even things like, we have
this algorithm that finds hotel deals.
When we preference a deal,
we explain it to the user.
This is a price drop that has
happened for the same check and
checkout dates in the last 30 days.
This is the
lowest price, which is
why we are recommending.
Something.
So that explanation goes a long way
in building trust and having people
keep coming back to the platform.
As we have seen through data,
about 20% of our users for
certain clients are repeat users.
So we want people to have trust
in the system, in not just the
data, but but a system that values
their privacy and preferences in.
In an inclusive way.
Rachel Satow: on that note, from a
marketer standpoint, we think about things
like GDPR CAN-SPAM etc all the time.
And I think when we are talking more
about the, software side of things and the
engineering applications there, we have to
be cognizant that there's a very fine line
between being helpful and being creepy.
and for us, when we think about that
user-first interface or that user-first
mentality, that means being like super
context aware and to Raven's point.
There's, being very transparent and
explaining why certain things are
being served or being, very upfront
from a marketing perspective, from
Ian and, my side pers we need to
make sure that we are being open with
sharing how we are using GenAI or
how certain features function, and.
We want to ensure and instill
that it's based on behavior.
this is something that we're learning
from users actually utilizing the
platform and not just from overall
surveillance or, that creepy side.
Ravneet: For sure.
We, don't tap into any data brokers to
get additional insights about users.
it's.
the, data we use, our systems are
fully compliant, G-D-P-R-C-C-P-A,
and and we look at user behavior
in an anon anonymized way.
So if a lot of people click on certain
hotels, maybe that is a popular hotel
for a particular season in that city.
So worth, recommending that to
someone we know nothing about.
Ian Andersen: So does this, get
into where the line between.
AI and machine learning is as far as
ai, the way I understand it, please,
tell me if I'm wrong, is that, you
have these large language models that
synthesize just ungodly amounts of
data to, then, come up with, variations
on whatever the, prompting is.
But machine learning
is the system actively.
Learning about you, And where you're
clicking and what you're doing.
I know those terms are used
so interchangeably, but, there
really is a difference, right?
Ravneet: For sure.
yeah.
I think in the past few years now,
gen AI has become equivalent to
AI and machine learning is ai.
So at a high level, the way I describe is.
AI is this superset or umbrella,
that, that includes systems or
processes that are intelligent.
They could be rule based, with simple
if the else conditions, if the weather
is sunny, it's not going to rain.
even those simple, ideas.
So AI encompasses all of that, but
machine learning is a subset within.
AI that becomes more tied to pattern
matching or pattern recognition using
a large data set or a data set that
it can draw those patterns from.
And it uses mathematics and statistics
to draw or identify those patterns.
gen AI or
or large language models is a
subset of machine learning itself.
It's not a branch as
such, but it's a type of.
Machine learning, in a way.
and a lot of it is based on transformers.
If we were to get technical, that
came out in 2017, of course a lot
more advances have happened since.
And, and now we use LLM Gen AI Machine
learning and AI interchangeably.
But there is some technical
difference when we.
When we get down to the nuts and bolts.
Ian Andersen: I got, I was,
reading something about.
What, always strikes me, and I, know
I, I specifically remember us talking
about this last time of like how old
this, like the technology or the,
at least the sort of mathematics and
theories behind the technology is
that there was, a guy that built a.
very simple computer in like 1952
that taught itself to play checkers,
just by repeating and learning the rules
and of, those before my parents were born
and we were getting into machine learning.
and is it just that like we
have now finally come to.
This point with, computer
processing that we can, that,
that we've seen this explosion.
Is that kind of what's going on?
Ravneet: yeah, you're exactly right.
a lot of the systems and algorithms used
today are very old, and for, the longest
time, there wasn't either enough data or.
Enough compute to apply those old
techniques on a larger scale to make them
seem intelligent enough or super human.
level intelligence.
I remember there is this researcher
that I don't know how long she spent,
I, vaguely recall about two or three
years of effort in labeling images.
the data set is called ImageNet, which
is millions of labeled images, whether
an image has a, human or a cat or a
dog, and like labeled to the details.
And,
her effort was creating that label
dataset, but it took 20 years from
the point she created that dataset to.
Someone using a lot of compute
and efficient, systems to
create a image recognition model
that
could identify images
better than humans could.
So, it, techniques are old.
and of course, now, I don't know if
you guys have heard this, that we are
running out of data again in that,
When someone talks about using all
the internet to train the data, a
lot goes into what I just said, but
let's say all that data is utilized.
So then where do G PT five or the next
version of the model get the data from?
So there is this side of
generate synthetic data, but also
can we improve,
Ian Andersen: That, seems really scary.
It just
Brandon Giella: Yeah.
That's
wild
Ian Andersen: gonna make fake data
just to keep this thing going.
Yeah.
Brandon Giella: that's crazy.
Ravneet: Yeah.
because I think one limitation is
at least the data that's available
in the public domain has already
been consumed by these companies,
or, the companies that lead
frontier research models.
and so either it's synthetic data
or enterprises letting their data
be used in some controlled way.
but at cer certain point, it
becomes efficiency of algorithms
and being able to work with less
data, then looking for more data.
Ian Andersen: So I wonder, It makes
me think of something that's been
on the periphery of the news lately
as far as, where AI is going.
You hear so much about the power
requirements and obviously if it continues
to ramp, in this exponential pattern that
it has been the past few years, the power
requirements are gonna pretty quickly.
Overtake over, overcome what we, can do.
so there's been talk of in
the future of, is generative
AI gonna be more generalized?
And we'll see that kind of the ag,
the what is the a GI will it become
more specialized and focused, to have
kind of fewer power requirements.
where do you see that going?
Ravneet: I, I personally see it, it
has to be a balance at some point.
As in,
we can only, or mankind or humankind
can only produce so much energy given
the infrastructure that's available.
So at some point it's a
balance of what's the energy.
Available.
And then do we make our processors
or chips faster, at the same time
reduce their energy consumption?
and then comes the next aspect,
which I hope to see in my lifetime,
artificial general intelligence.
A lot of research is
going on in that space.
I don't know if you you've seen,
one of the documentaries from,
Google DeepMind researcher DE has,
he, won the Nobel Prize recently,
for solving the protein, structured
problem or protein folding problem.
Brandon Giella: Hmm.
Ravneet: So, there are a few labs across
the world that are working towards a GI.
But it's, it's hard to, assess whether
is it a compute problem for now or
given enough compute, does it become a
data problem or does it become just the
algorithmic efficiency or power problem?
I think those are just
different parameters these
research labs are playing with.
Brandon Giella: think we can figure
that out on this call, right?
Ian Andersen: yeah, We
Brandon Giella: is.
Ian Andersen: Yeah, for
Rachel Satow: I was about to say,
this is all barring a third party
extraterrestrial tesser act coming into
play to power.
Brandon Giella: Yeah, I was saying,
I was thinking in my mind like, I'm
really excited to build my cabin in
the woods, powered by, some solar and
I have one lamp, and I just read books.
no, I wanna, I wanna shift gears a
little bit and think more, Practically
in terms of there's a lot of research
going on, there's a lot of trends.
There's energy usage.
There's privacy and data and, regulation,
compliance around these issues.
But I also wanna mention and
bring to bear that these things
actually do these systems that
people using them, this whole, this
whole world we're talking about.
It does impact business in real ways,
and it impacts and drives conversions for
loyalty programs and things like that.
And so I'm curious from the data
that you've seen, and maybe even
anecdotally, things you've heard
or things that you've read.
What are some ways that you're seeing
AI have a tangible business impact,
particularly in the travel industry?
even if it's just your own data, you're
seeing, X percent increase in conversions
and this and that on the platforms.
Is there anything like that you can
give us some insight into, like how,
these big abstract concepts or what
feels to a novice like myself, an
abstract concept, but now I want to
implement it or I want to invest in it
and I wanna make a business decision?
On these kind of concepts, like how
do I even begin to think about that?
What are, you seeing to
help me make that decision?
Ravneet: I, I'm not a business first
person, but my perspective is, those
business problems are still the same.
People want
to find the right information in
as few steps as possible and be
able to trust that it's reliable.
So now
the workflow that we have behind
it, we can surface new features.
for example, if you look at a hotel
search results page where people, or
I, would look for a hotel, I would
put filters on a certain star rating.
I would put filters on certain
prices or certain amenities,
to, find exactly the hotels.
That I'm inter, that I'm interested
in, and then I may review those details
manually, but surfacing features, like
one of the things we are working on is
give a natural language, text option
to user where they can ask questions
or type some text and find hotels
that match exactly that criteria.
For example, if a person is interested
in laundry or laundromat services.
I haven't seen,
plat other platforms offer or any
platform offer a amenity filter
saying you can check a box and
it would give you hotels
that have that service.
Brandon Giella: Mm-hmm.
Ravneet: but now with Gen ai, we could
create this natural language text feature.
A person can say that and
find exactly the hotels,
that match their criteria.
So it basically makes.
Unstructured data or natural
language text, searchable in more
variations than we can imagine.
Brandon Giella: So for example.
Let's say I'm planning a trip to Paris.
We talk about Paris on this show a lot.
I like Paris.
but let's say I'm going to Paris
and I'm thinking, okay, and I'm
typing into a chat bot maybe.
And I'm just saying Hey, I'm looking for
something in the first Aron des month and
I want to be near a restaurant like this.
And I have this in mind
for the actual hotel.
I wanna make sure there's a pool.
I wanna keep it under this, price point.
I'm gonna be there for five days.
You can, say all those
things in like a chat.
it would surface what it's,
interpreting that information.
It doesn't have to be like a dropdown.
Filters, is what I'm hearing.
Is that, kind of how it works?
Ravneet: I, I, yeah, exactly.
So we are not making that big a shift
yet where users, Just interact with the
bot to make their booking, just yet.
But we want to keep some consistency in
how people were using, or entering a
destination dates, hitting a search,
and then looking at the results.
But now they can interact with the
results using a natural language text box.
but hopefully in the future as
we see users get more comfortable
with using natural language text.
We could shift to either a voice activated
or, text-based, chat bot
Brandon Giella: Yeah.
That's awesome.
I like that.
That's cool.
Are you seeing any impact on like loyalty
programs specifically and how they might
be interacting with these platforms?
Ravneet: for sure.
I think, I think, overall, if, you
look at again, the same business
metrics, That we measured before.
so in terms of customer engagement, we
do see the moment we make our platform
better, either even in if it were
give such results to users quicker.
We see people doing more searches,
we see people engaging with
more content and almost it is a
statistically significant difference,
for all the AB testing we have done.
And, so we do see improvements in.
Conversion.
We do see improvements in search
engagement rates, the number
of hotels, or average number of
hotels a person clicks, in a day.
Brandon Giella: So going back to
the top of the conversation where
you were like, there's productivity
improvements and there's obviously
like technology improvements.
You're really seeing in hard statistical
data, those kind of productivity
improvements on the platform.
And the way that AI is filtering
and searching and labeling,
there's real business impact.
More conversions, more clicks, more
hotels searched That's pretty amazing.
Rachel Satow: Yeah.
And just to chime in here, Revit since
we chatted about this at earlier this
year, with the launch of Neighborhood
Insights, which is powered by our models.
The, quick stats that we had chatted
about was an increase in, users at
a 4% increase in users searching.
So going back to what Nee had
mentioned, we're seeing more activity,
and engagement with actual search.
then, from those viewing
destination pages, and then a 1.6%
rise in total conversion.
So actual booking from
those destination pages,
all with the implementation
of, of our models.
Brandon Giella: That's amazing.
Ravneet: yeah, that, that was
a cool feature as well where,
I haven't seen any other
platform do that yet.
Wherein, when a person starts exploring
a city, I think one of the first
questions I ask myself is, which
neighborhood should I stay in that
city?
Brandon Giella: Yeah.
Ravneet: because cities could, or
different neighborhoods have different
vibes, and so we created this feature
where we could make neighborhood
based recommendations within a city.
And explain why we are recommending
a certain neighborhood.
For example, a neighborhood
is known for its monuments and
history, so we recommend that
neighborhood and hotels under or
within that neighborhood, and that
led to some statistically significant
improvements across the board.
Brandon Giella: That's
Rachel Satow: And just, go ahead again.
Ian Andersen: Oh, no, I was
looking at, I cannot, I, just
looked up, because I couldn't
remember the exact statistics, but.
I cannot believe this was two years
ago that we were talking about this,
the similar hotels, thing you, you
put together over two years ago now.
and even, just the, short time we
were doing the, use case, examples,
we saw 20% increase on, conversion
rates, on booking rates,
was that 30% increase on, searches?
just,
it was, startling how drastic it
was in such a short period of time.
this was a matter of weeks
that, that we looked at this, it
wasn't, a whole giant data set.
So I can't imagine it's just
gotten even, better since.
Rachel Satow: Yeah.
Anecdotally, the neighborhood
insights has become, quickly
become one of my favorite features.
so I'm actually planning a trip
to New York in January to see
a Broadway show, and I grew up.
hours from the city.
So I am familiar enough, but
it's obviously changed since
I've moved out of state.
And, I found myself like really leaning
into neighborhood insights to try and
refresh what may have changed since I've
been gone, and to try and find like the
perfect hotel spot for, me and a couple
of friends to go see with this show
because, There are some people who wanna
stay in the middle of Times Square, and
there are some people who wanna stay
outside of all of the hustle and bustle.
so I found myself like leaning
into that feature quite a bit.
Brandon Giella: That's great.
No, I love that feature because it's
almost like vibe check, okay, I'm going
to, let's, I'm just picking Paris again,
but say we're gonna Paris and I want
to, I want a quieter neighborhood, or
I want a neighborhood of more shops,
or I want a neighborhood that's got
a lot of, let's say Michelin star
restaurants, or a lot of museums.
Like, where do I, go?
And to be able To To put
some language to that.
'cause if I'm not that familiar
with the city especially,
that's such a fantastic feature.
Ian Andersen: And, one thing to
highlight of just bringing it back
to how quickly this all moves is,
a couple years ago when we were
really first talking about.
Ai, getting integrated in with switch,
it required a little bit more from the
user as far as, specificity, right?
Like the similar hotels feature.
It.
you had to pick a hotel before you got
some sort of specificity, right?
and it's just gotten.
More and more, I don't know,
what's the word I'm looking for?
It's, predictive as far as leading
you rather than, being reactive.
It's being more proactive.
it is maybe a subtle distinction, when
you're looking at it side by side.
But as far as usability, that's, it's.
It's, game changing, right?
Being able to go in and not have
to explain necessarily what I'm
looking for entirely to give very
subtle kind of interactions and let
the, program really tease that out
of you, and get you to that point.
I think's a big deal.
Ravneet: Yep, for sure.
I think, more than making huge changes
that changes or gives a new look and
feel to how people search, it's these,
consistency of the features
that people are used to.
How can we do some minor adjustments?
A person has already.
I chosen a hotel that they're reading
about, so why not recommend some
other similar hotels so the person
doesn't have to go back in the
shopping flow and find another hotel.
So it's these smaller incremental
improvements that are making a big
difference in delivering value.
Ian Andersen: That, that makes sense.
Just from a, a, i, don't think I've
thought about that, but the, If you
say you booked on Switch fly a month
ago and then you came back and it's
completely different, no matter all of
the like upgraded functionality, it's
probably not as impactful than if you
come back and it's pretty similar, but
there have been some subtle upgrades
and you're like, oh, hey, last time I
couldn't do this, or last time, whatever.
Like it, even if it's not as
like broad and sweeping, it
probably is a little more.
Impactful per user.
Ravneet: Yep.
Yep.
and which is why when I was talking
about the natural language search.
We want to keep existing UI or UX
and slowly move users to, a natural
language search, but at the same time
learn about, sometimes I wonder the
way people use a platform, it's more
of a symptom of how the platform is
designed, which is consistent with
every other platform before we start
thinking of a personalized concierge.
'cause practically how many people.
use a concierge today.
and the other part of that is, the
data that they're willing to share.
So it's a, trade off and, we want
to make sure we take the baby steps
and, deliver value where users see.
Ian Andersen: Which will get them more
comfortable with sharing more data.
Like it just is a natural kind of
progression that Yeah, it feels right.
Brandon Giella: To me, the listening to
you guys talk about this, it's, Rodney,
you use this language, a moment ago
where it's like changing the relationship
to how we're searching for travel.
And it makes me think of, I've had
this thought on my head, Ian, as a
history nerd, you'll appreciate this,
but it's like the, printing press,
how it changed, the relationship.
We had to text and information and,
then, The, our relationship between
church and state and education and all
kinds of different things that we won't
get into, but literally everything.
but what I find fascinating is that's
where we're going, is more closely
to how the human mind works, which
obviously is the underpinning of a,
large language model and that it's
mapping a neural network between our
brains and information, things like that.
But what's interesting is Ian, you
mentioned like you, when you're
searching for something, you just
are, you don't know exactly what
you're looking for, and it's the same
kind of thing when you are speaking.
As I'm speaking now, I don't know exactly
what I'm thinking, but I'm, searching it
out as I'm speaking and a conversation, a
dialectic or writing itself is thinking.
And so I love that you guys
are, this is very meta.
But you're just going in this direction
of like how people are interacting with
travel itself and this kind of they're
just like vibing their way into the trip
that they want and using AI to get there.
Is that, ki, am I directionally correct
in how you guys are thinking about this?
I.
Rachel Satow: Yeah.
this was something that,
Reni you and I had talked about way back,
but it was really about the consolidation
of the effort of planning your journey.
your.
The way we search.
so.
a lot of the intention behind some
of the features that are going into
the Switch fly platform are with the
end goal of making it simpler and re
removing friction points so that you're
not having to go to five different other
websites to find out this information.
And you can all do it all within.
One platform that you can
then eventually book from.
So it reni, correct me if I'm
wrong, but yes, it is, all with that
goal of being able to, streamline
the whole traveler journey.
Ravneet: that, that's, that's on point.
Rachel, I think overall there
are so many travel platforms,
consumer facing or reward.
Loyalty, rewards focused, and
each, external platform has
their own special features.
So rather than having people do research
and discovery outside and come to
our platform just to make a booking,
we have these features where we want
people to be able to stay within our
platform right from the point they
where they decide to travel and.
Find the right destination, the right
neighborhood, the right hotel activities,
or whatever the itinerary looks like,
and be able to do it in one single place.
Brandon Giella: That's cool.
I love that.
Okay.
Last question for you, just in
the last two minutes or so, what
are, ways that you're thinking
about the future of AI and travel?
So we talked a little bit about
vibing your way maybe into, travel
and the way that you're searching,
but what are some other maybe
technologies or trends that you're
paying attention to that will really
impact your work in the next 12 months?
Because we can't predict
any further than that.
Like literally every month AI is totally
different, is what it feels like.
But So the next 12 months though,
where are you guys pointing your ship?
Ravneet: A big focus is, we
have done, some experiments
over the last six to 12 months.
A lot of it is focused on
operationalizing those.
our Northstar is our business metrics.
so what drives conversion?
What drives customer engagement and
the features, that help move that
Northstar, we are focused on that.
so a lot of experimentation, AB testing,
new features and making decisions.
How far do we want to go on a
certain feature, where it drives.
Business value, but at the same
time, it's useful for the users.
so essentially that's the driver.
And then tying it back to gen ai,
more productivity improvements.
how do we, improve the
quality of our deliverables?
How do we identify issues earlier?
How do we maybe automate code
reviews or write better test cases?
so.
a lot of productivity improvements
and business value driving features,
both for users and the business.
Brandon Giella: That's great.
That's a great place to start.
Ian Andersen: So I do have
one final one, Brandon.
Brandon Giella: Okay.
Ian Andersen: it's triggered by your, meta
wanderings is, ny from a, data scientist
perspective, what is intelligence?
Is there a way for a machine to be
intelligent the same way a human is?
Ravneet: Oh,
Brandon Giella: in 60 seconds, go.
Ian Andersen: I want bullet points.
Ravneet: that's a great question.
I personally like to think of, when I
see my 3-year-old son, since he was born.
I'm seeing him more like a
machine in training for that time
in that
he, ma he makes an action or
says something, and then my wife
and I, we nudge him in a certain
direction supervising him.
And his neural network, which is maybe
far sophisticated than a machine network,
but his neural network is in training.
And so at least in theory, if there
is right dataset, right algorithms and
enough compute, then a machine should
be able to ex exceed human intelligence.
The question is not if it's a
question of when, in my mind, and.
Purely speculative, that when
could be five years from now,
it could be 10, 20, or 30.
but
it's more of a question of when, to me.
Brandon Giella: Scary, hopeful.
I can't tell.
but if, you have any training data on
my eight week old to get him to go to
sleep, I would love to just plug that
one right in 'cause I'm very tired.
No, that's great.
That's a great analogy.
Awesome.
team, thanks so much.
I love being on the show with
you guys 'cause there's so much
wisdom and insight and just fun.
And I enjoy it.
so Ravini, thank you
again for your expertise.
I am, super excited in the way that you
guys are thinking about AI generally,
conceptually abstractly, but then
how it really impacts businesses
and, makes travelers lives better.
that's the whole point.
And so as a, abstract user on
your platform, I appreciate
you guys and what you're doing.
Yeah, but, if you haven't yet, this is to
you listeners, if you haven't yet listened
to episode 13 AI and Machine Learning
with Rodney the first time, please go
back and listen to that because it'll get
you up to speed on the, philosophy or the
approach that, you guys at Switch Fly.
Kind of, just think through AI and
machine learning in general and then this,
obviously the show we talked a little
bit more practicality and kind of future
facing, but, and it's really interesting
to see it just in the last 12 months.
How this kind of technology and
thinking has developed and, how
you guys are approaching it.
So please go listen to that and with
that we'll see you on the next show.
Thanks
Rachel Satow: Thanks Brandon.
Ian Andersen: Thanks Brandon.
Ravneet: Oh, thanks for having me.