Exhibit
99.1
Operator
Hello.
Welcome to a March 10, 2026 Versus AI webinar. I would like to read our disclaimer.
This
presentation contains forward looking information and forward looking statements within the meaning of applicable securities legislation,
collectively forward looking statements.
Forward
looking statements can be identified by the use of words such as plans, expects, is expected, budget, scheduled, estimates, forecasts,
predicts, projects, intends, targets, aims, anticipates, or beliefs, or variations, including negative variations of such words and phrases,
or may be identified by statements to the effect that certain actions may, could, should, would, might or will be taken, occur or be
achieved.
Forward
looking statements are based on a number of assumptions of management, which are subject to a variety of known and unknown risks, uncertainties
and other factors, which may cause the actual plans, intentions, activities, results, performance or activities of the company to be
materially different from any future plans, intentions, activities, results, performance or achievements expressed or implied by such
forward looking statements.
Factors
that could affect our actual results include, among others, those that are discussed under the heading Risk Factors in our most recently
filed reports with United States Securities and Exchange Commission, including our annual report on Form 10-Ks, our quarterly reports
on Form 10-Q, and our current reports on Form 8-Ks.
The
forward looking statements contained in this webinar represent management’s best judgment based on information currently available.
No forward looking statement can be guaranteed and actual future results may vary materially.
Accordingly,
you’re advised not to place undue reliance on forward looking statements. The forward looking statements herein are made as of
the date of this presentation only, and the company does not assume any obligation to update or revise them to reflect new information,
estimates or opinions, future events or results, or otherwise, except as required by applicable law.
A
recording and transcript of this call will be available on the company’s website at www.verses.ai. With that, I will turn the stage
over to Interim CEO, David T. Scott.
David
T. Scott
All
right, thank you very much, Rob. Good day, everyone, and thank you for joining us. When I stepped into the role of interim CEO, one of
the commitments that we made was to communicate regularly to our investors and to make sure that we address the questions that many of
you have been asking.
Today’s
call is part of delivering on that commitment.
Over
the course of this update, we’ll focus on two areas. First, we’ll give you an overview of our technology and where we see
it’s creating real value in the market. And then we will address a number of questions about the company’s current management
and operations that we were not able to fully cover during our previous earnings call and our previous webinar.
These
answers will also serve as an update to relevant parts of our business.
So
with that, let’s begin with an update on some recognition that we’ve recently received.
This
month, IEEE featured our work in a six page article in their print edition, which reaches roughly 380,000 members of the IEEE community.
The
IEEE, if you’ve not heard of it, is a professional organization of electrical engineering, electronics engineering, and related
areas that develop and regulate critical standards, including those that cover WiFi, Bluetooth, and spatial web.
VERSES
was instrumental in helping define the spatial web standard and getting it through the approval process.
While
much of the material had been already previously published online, being selected for the print edition was especially gratifying.
And
we see it as meaningful recognition of the progress the team is making and the growing interest in what we’re building.
So
with that, we’re going to talk about the technology.
James,
take it away.
James
Hendrickson
Thanks,
Dave. We have had a lot of questions about our technology, so we thought it would be helpful to give a further overview building on the
call we did at the end of February.
As
we said previously, several years ago, we started on an ambitious journey to approach AI from a different perspective than from other
AI systems.
We
started with the work of Professor Karl Friston, one of the world’s most widely cited neuroscientists, who developed the theories
that best describe how the human brain works.
We
look to the human brain, which evolution has made enormously efficient.
And
because if we built an AI system using the same principles used by the human brain, we would get the type of AI that people actually
want in the world. An AI that is very accurate, is more reliable, trustworthy and efficient, doesn’t consume a lot of power, handles
uncertainty well and can learn in real time.
It
has been well known for some years that the brain uses a process called active inference to operate.
Our
brains constantly make predictions about the world around us. We assess what we are uncertain about and what we need to resolve.
Once
our brains model the world, they learn over time and continuously improve.
When
you walk toward a crosswalk, your brain predicts whether cars will stop. If you’re unsure, you look both ways, wait or step back.
Your brain updates its understandings every moment until you feel safe to walk.
That
cycle, sense, predict, update, act, is active inference in motion, a continuous predict and act loop that is deeply coded into humans,
allowing us to adapt and improve.
Our
brains are highly energy efficient, operating on very little power.
However,
while the principle was understood, no one knew if this could be translated into computer code.
But
of course, the human brain does this, and so we have to learn from it.
We
developed solutions to this scaling challenge in three ways.
The
first, level of detail. Just as you can zoom in on your house in a map application to see a car in the driveway or zoom out to view the
entire city, our models can flexibly scale zooming in on specific details or zooming out to capture broader patterns.
Second,
with specialization.
Much
like the brain assigns different functions to different parts of the brain, some modules are expert in image understanding, others in
tracking and prediction, or in strategic gameplay and learning.
This
enables efficient problem solving.
And
finally, network effects. Intelligence is amplified when agents, devices and systems collaborate. Underpinning this is the Spatial Web
Standard, a framework we help develop so AI agents, sensors, and physical devices can coordinate effectively.
These
solutions have been built into our product Genius.
While
we won’t give a full product demonstration on this call, I think it’s useful to cover both its main modules and what it looks
like for a user.
Genius
has four modules: SENSE, THINK, ACT and SHARE.
SENSE
perceives the world much like we do, integrating sensory information that gives computers the ability to truly see, process and understand.
THINK
serves as a digital brain with modules for memory, prediction, and reasoning that work together to continuously refine an internal model
of the world.
Learning
is central to the THINKmodule. Over time, our models learn and become more efficient, pruning what they no longer need while continuously
adapting.
The
result is a system that becomes smarter, more efficient, and more reliable with experience.
THINK
has underpinned our work on financial services, but it isn’t restricted to any specific industry vertical.
The
ACT module allows robots and agents to learn new tasks quickly in physical and digital worlds without the extensive pre training that
conventional systems require.
And
SHARE enables agents from traffic signals to drones to lunar rovers to collaborate securely and share not just knowledge, but also skills.
What
one learns can be distributed instantly.
Together,
SENSE, THINK, ACT and SHARE form the sight, brain, body, and ecosystem of an AI that perceives, learns, adapts, and improves with experience.
Today,
users can access Genius in two ways. The first, custom implementations. Our financial services customer pays us to develop Genius for
services and implementation, including onboarding, integration, and customization. And the second is Genius recurring software licenses.
However,
both paths are working with customers who are highly expert users and are solving valuable enterprise level problems.
Genius
is therefore accessed primarily through the SDK and API, although elements are available as a drag and drop tool.
So
this isn’t suitable for non-experts, which is one of the reasons we do not prioritize making it available to the mass market. For
the next year at least, we primarily expect that the day to day users will be machine learning engineers and other technically adept
experts.
As
many of you know, as many of you will know, the ARC-AGI-3 challenge launches on March 25th.
VERSES
is developing further capabilities for Genius, which will allow us to solve complex problems such as those tested by the ARC-AGI-3 challenge.
ARC-AGI-3
is a test of human like intelligence. It does this by setting tasks that are deliberately designed to show the gap between cutting edge
AI systems and human intelligence.
The
underlying philosophy behind it is that intelligence is simply how quickly can you learn new skills?
ARC
measures success on the axes of cost and the ability to perform tasks. We have previously seen Genius perform as well as leading models
in other benchmarks, but at a fraction of the cost.
This
makes us optimistic about the ARC-3 challenge. So this challenge launches soon, but it will be ongoing for a long time. For instance,
Google recently published ARC-AGI-2 results, which more than 10 months after the benchmark started.
And
I would encourage all of you, if you have interest in this, go to the ARC-AGI website and try playing the games yourself and understand
what was being asked of us from a computer science perspective.
There’s
a lot more to talk about on our technology, far more than we have time to cover today. Significant areas I would highlight are our models,
our research partnerships with organizations such as Die Edge and more detail on the Genius product.
Let
us know what you would be interested in hearing about more about in the future. Okay, back to Dave to answer outstanding questions from
previous webinars.
And
the way that we’re going to work here, I think I’m going to ask the questions, but I’ll let Dave set this up.
David
T. Scott
Okay,
thank you very much, James. As always, we welcome your questions and we do our best to address as many of them as possible, subject of
course to legal, regulatory and customer confidentiality constraints.
So
for today’s discussion, we’ve grouped together questions that cover similar themes. We had over 60 plus questions from the
last webinar and we grouped them together so that we can answer many of them all in a few several questions.
So
while you might not exactly hear the wording of the question that you submitted, you should hear a clear and relevant response that addresses
the underlying topic.
And
I noticed here that there’s a lot of new questions that have been raised right on the spot. Unfortunately, we’re not able
to cover those, but we will make sure that we do that in future updates.
So
with that, let’s move to the question and answer. We’re gonna have James ask the questions and then I’ll answer them.
James
Hendrickson
Great.
All right. The first question here is, do you plan to list on the Nasdaq?
David
T. Scott
So,
you know, listen, we continue to consider a variety of options for access to capital, and Nasdaq is certainly one of them, and we’ll
continue to do so. So right now, everything’s on the table.
James
Hendrickson
Who
are our current customers? What is the status of the previously announced customers?
David
T. Scott
Yeah,
I saw that a lot of people ask this question and we’re asking for specific names, for example. But as a public company, you know,
we’re legally obliged to provide accurate information at all times, including when we disclosed information about our customers.
We
do not, generally, though intend to provide a running commentary on specific customer relationships, as doing so will undermine our ability
to negotiate favorable terms with both current and prospective customers and suppliers.
For
instance, we will not generally disclose when a contract completes or reaches its natural end.
James
Hendrickson
So,
Dave, why can’t we name the financial services customer that we announced?
David
T. Scott
It’s
because usually it’s a requirement of the customer’s contract with us that their name remains confidential.
This
is entirely up to the customer, and it’ll be their approval before we can actually ever announce them as a customer. So it’ll
be up to the customer to decide.
James
Hendrickson
In
your engagement with your portfolio management manager customer, what is the title or the position of the key person you’re negotiating
with?
David
T. Scott
This
is a great question, and it gets to sort of who our buying center is.
But
given the strategic nature of the work that we’re doing around portfolio management and investment decision making, these engagements
naturally involve some of the top senior executives who are responsible for things like investment strategy, risk management and overall
portfolio performance. And we expect that the key buyers will be on the customer’s senior leadership teams, for instance, the CIO.
James
Hendrickson
Great.
Okay. What’s the potential with dAIEDGE? So I’m going to take this one.
David
T. Scott
Yeah,
I’m going to ask you to answer this one. You know this better than I do.
James
Hendrickson
dAIEDGE
is a European Union project to develop distributed AI systems at the edge. The edge means close to the physical world, which requires
low power autonomous decision making for things like robots and drones.
So
we continue to work on an ongoing project with dAIEDGE.
And
in November, we published examples of this work in a blog post describing a robotics use case, which you can find on our website under
the blog section. Importantly, this work is fully funded by dAIEDGE. This provides an opportunity to demonstrate the practical applications
of our technology in robots and edge based systems and to explore additional opportunities as the collaboration develops. You can read
more about this at daiedge.eu so you can go out there and have a look.
All
right. Next question. When will the newsletter resume, Dave?
David
T. Scott
Yeah.
So we’re continuing forward with our efforts on the newsletter. The next newsletter will be published within a few days after this
webinar, and we intend to continue with monthly newsletters going forward. So as we’re doing these webinars, we’ll be producing
our newsletters accordingly.
And
of course, if you have areas that you’d like us to cover, please reach out to us. We’d be happy to include that in the newsletter
and address things there that [are] either questions from investors or from people who are interested in what we’re working with
or just areas that you’re interested in learning more about.
James
Hendrickson
How
will you increase investor confidence, Dave?
David
T. Scott
So
this is what I go to sleep thinking about and wake up thinking about, right?
We
believe that the most effective way for us to build investor confidence is through consistent execution of our strategy, combined with
clear and regular communication.
Our
focus is on delivering tangible progress, advancing our technology, deepening our customer relationships, and demonstrating a clear path
to commercialization.
As
we continue to execute and provide updates on that progress.
We
believe confidence will follow naturally. So for us, it’s all about putting one foot in front of the other.
James
Hendrickson
How
is VERSES commercializing its technology? Dave, do you want to take this one?
David
T. Scott
Sure.
Yeah. I mean, so our immediate focus is on deepening our commercial relationships in financial services, including those that we’re
already working with, primarily by continuing to develop additional product features that expand the value of the platform.
We
expect our progress to accelerate over time because we now have a product that can be applied to similar users with only a small amount
of customization.
While
the financial services product will continue to evolve, it is reaching a level of maturity where we’re actually confident that
it can actually scale to additional users.
Building
on this foundation, we are targeting additional institutions with more than $100m in assets under management that face similar challenges
around risk management and improving returns from their investment portfolios.
As
these relationships develop, we expect to monetize them through a combination of setup fees, consulting engagements and ongoing software
licenses.
James
Hendrickson
Great.
So
let’s see the next one here. Why can’t Genius be bought today by ordinary users?
And
I will address this one. At this stage, our product genius is geared primarily for enterprise business to business or B2B users within
the financial services industry who are able to derive meaningful value from its capabilities. During the beta phase process and since
the launch of Genius, we learned two really important things about the market for Genius.
The
first, there’s a growing frustration with the limitation of LLMs and an openness to alternatives without a clear understanding
of how those alternatives differed from LLMs. And the second was using Genius requires coding expertise, experience working with complex
mathematical models and problems that benefit from probabilistic decisions, decision making in Genius.
So
it’s great that there is interest in alternatives to LLMs, but there is a learning curve that for the time being makes Genius less
accessible to ordinary users.
Over
time, as the product evolves, the market evolves and the user experience becomes more accessible, there may be opportunities to broaden
the access. But for now, our priority is ensuring that Genius is deployed with customers who can fully leverage its capabilities.
Why
are the next question here? Why aren’t more people buying Genius? Dave.
David
T. Scott
So
like many other enterprise grade technologies, Genius is designed to solve very specific and complex problems for our solvers. Because
of that, it tends to be sold into organizations where the value per customer is high.
That
dynamic means that the sales process is naturally more deliberate than a consumer facing product.
Enterprise
customers typically require time to evaluate, customize, and integrate before adoption.
However,
the trade off is that each such successful deployment can generate significantly higher revenue than a per customer model.
As
we continue to develop the product, we expect greater portions of our sales to come through channel partners. Over time, we also expect
a growing share of revenue to come from software licensing as the platform becomes easier to deploy and scale across multiple customers.
James
Hendrickson
Great.
Okay, next question here. Could you give more information about what the deliverables for financial customers look like? Dave, why don’t
you take that one?
David
T. Scott
So
we believe that financial services is particularly well suited to our technology because firms manage large portfolios where even the
smallest improvements in decision quality can create significant economic value.
Our
work focus on building probabilistic models that help portfolio managers evaluate scenarios, manage uncertainty, and make better allocation
decisions.
In
practice, the deliverables include a production ready model on the Genius platform, integration with clients’ data and decision
support systems, and tools that allow managers to run simulations and test portfolio strategies.
These
engagements typically begin as project based implementations, but they’re designed to evolve into ongoing software deployments
that can be licensed and expanded over time.
James
Hendrickson
Great.
Okay. Let’s change gears a little bit here. Dave, do you have updates on the legal case that we disclosed previously?
David
T. Scott
Yeah.
So outside of what we’ve already disclosed in our public findings, we’re not able to comment on active legal proceedings.
As
always, we’ll continue to provide updates through our required disclosure processes.
James
Hendrickson
Right.
What is happening to help commercialize the spatial web, Dave?
David
T. Scott
Yeah,
the spatial web. I’m particularly excited about it, but at the moment, our primary commercial focus is on financial services. And
that’s where we see the clearest path to near term revenue and customer adoption.
That
said, the spatial web remains an important part of the broader vision behind our technology.
If
profitable opportunities emerge that allow us to advance on or commercialize the spatial web related capabilities in a way that aligns
with our strategy, we will absolutely pursue them. But for now, however, our priority is to concentrate on resources where we see the
most our resources and where we see the most immediate commercial traction.
James
Hendrickson
Okay,
great. Let’s see. How defensible is VERSES existing IP?
David
T. Scott
So
our intellectual property is protected a number of ways. It includes patents, copyrights and trade secrets.
Beyond
formal IP and legal protections, our defensibility is strengthened by the practical experience where we have gained implementing active
inference across multiple industry verticals.
That
operational knowledge combined with our growing set of customer and partner relationships creates an additional barrier to entry.
In
addition, the work of our research and product teams along with the continued development of our Genius platform further deepens that
moat over time. So from my perspective, we’re well protected.
James
Hendrickson
Great.
Thank you. All right. Now we’re taking a little bit more technical turn here. Describe your advantage in lowering multivariate
inferencing costs.
David
T. Scott
You
got to take that one.
James
Hendrickson
Oh,
I was going to hand that to you on the spur of the moment. Okay, LLMs typically operate with multivariate inferencing with the associated
costs.
By
contrast, our models adapt and optimize themselves over time, discarding parts that no longer add value. The result is systems that require
less training data and significantly less compute.
In
our benchmarks, we consistently see faster performance and lower compute requirements than many alternative approaches. For example,
in the Game World benchmark, our approach was approximately 40 times more cost efficient than one of the other competing systems.
I
don’t know why you didn’t take that one, but let’s go to the next one here. Will Genius mainly be used with in house
programming rather than licensing to or through developers and programmers?
David
T. Scott
So
when we begin developing Genius for a specific industry like financial services, we have to start by working with our in house team in
close collaboration with our Lighthouse customers. This allows us to refine the product, build the core functionality needed for that
sector, and ensure that it delivers real value in a live environment.
As
the product for that industry matures, we expect that the model will evolve. So over time, a larger share of our work will be carried
out by the customer themselves, as well as by channel partners and those who license the software.
James
Hendrickson
Great.
That’s a pretty normal approach to this. So I think it’s a great way that we’re approaching that. Next question. Is
Genius the only commercialized product?
David
T. Scott
So
Genius is our core technology platform, and as a result, all of our software revenues are generated through Genius.
Future
product developments, will also take place, but it’ll all be taking place within this platform specifically.
So
while of course while our commercialization efforts today are focused primarily on financial services, Genius has a broad set of capabilities
that can be applied across multiple industry verticals. We know this, right?
So
over time, we do expect to extend the platform to additional sectors as opportunities arise.
All
right, great. And I think this might be the last question we have.
James
Hendrickson
How
much has been spent on R and D to date?
David
T. Scott
So
of course, this information has been disclosed in our public financial statements that have been published so far.
So
if you’re interested in answering this question, we do encourage you and as an investor to refer to our filings for the most accurate
and complete details regarding our historical R&D expenditures.
Is
that it, James?
James
Hendrickson
I
think that’s it for the questions that we’ve had so far. Looks like there’s been a number of other good questions that
have come in, so we will record those and address those on the next call.
David
T. Scott
Yeah,
speaking of which, I just want to thank everyone for their time today. We have hundreds of people on this webinar and I want to thank
you for listening in. And also, I want to thank you for asking these questions. They’re great questions and they deserve to be
answered and we will get to every single one of them over time.
Any
questions that we haven’t answered today, as I said, will be addressed if possible, if I’m able to do so in the next webinar
or other types of communications from the company.
So
stay tuned. Our next call will be April 21st, 2026. And thank you and have a great day.
Thanks
everyone.