How investors can profit from Robotic Process Automation and Artificial Intelligence: the real value of AI

Our next presenter is Dr. David Milhouse, founder and chair at Actium AI.

Dr. Milhouse has been an entrepreneur in international private equity, venture capital and funds management since 1983.

Today, he'll be talking about how investors can profit from robotic process automation and artificial intelligence, the real value of AI.

Welcome to the presentation, David.

Thank you very much, Angelina.

It's a huge pleasure, of course, to be here with your investment markets again.

So my job today is really to explain to your investor group

where the profit is in artificial intelligence.

You'll all have heard the hype which is sort of reminiscent of the dot-com boom that I'm old enough to remember and the bust that came from that.

And is AI going to follow a similar pattern?

The first thing I should say about all of this hype about artificial intelligence is that there is no such thing as artificial intelligence anywhere on the planet.

You have things that look intelligent but actually aren't.

It's all process automation.

The second thing I want to say is a preface to this short seminar.

is that none of this technology should be used in any corporate professional regulatory sense without high level skilled supervision.

So I want to show you now what Axiom's role is.

Our role is to make organizations or enterprises intelligent of themselves by the deployment of technology inside that enterprise

to automate their workflows.

And Atchum is in a position now where it can do that in any industry, any profession, any language, up to 50 languages, anywhere on the planet and at any time.

So we call this an IWA model, which no one else on the planet has, and I'll explain why.

The first thing to know about artificial intelligence, so-called,

is what is implicit in these two triangles.

So there is no AI industry of itself.

There are actually at least two of them.

And one on the left-hand side is what's known as large language models, which rely on machine learning algorithms.

And the other is the highly contextual usage of that firepower.

for making good decisions and usages at a heavily contextual level.

Now, I always say to my colleagues that there is no point being anywhere in any investment market unless you're right at the top of the triangle.

So there are relatively few organizations that are at the top of the triangle, whether they be in the large language model space like Amazon or OpenAI or Gemini.

or indeed in the heavily contextual applications of it, which are now known as these agentic AI scaffolds.

90 plus percent of what investors see and are approached to invest in are down the bottom of the triangle.

So these include the chatbots and the low value add-ins and all those sorts of things, and they are not investment grade opportunities.

What we've done in Actium is to position ourselves right at the top of those two triangles.

And we bring together the qualities of the large language models with the contextual usages in any industry, any language, any time.

Now, the way we do this is that we have our own proprietary robotics.

And one of the problems that people face is the issue of garbage in, garbage out.

And some of you would have heard the term hallucinations, which is where these large language models actually fake an answer.

So our own Archimedes robot, which is proprietary to Actium, deals with those issues in the sense that we provide legal veracity about what goes in and we interpret with a legal veracity framework of what these models produce.

So it's a process of

automation.

It's certainly a process of machine learning.

There is implied or inferred artificial intelligence in it and importantly what comes out is interpreted through the legal linguistics model which is the Archimedes proprietary robot.

Now independently this has been called the Rosetta Stone of Law

And for those of you that are schooled in classical literature, Rosetta Stone was the discovery of the translation of ancient Egyptian into ancient Greek.

So Actium-AI has the only technology on the planet that links this sort of legal linguistics with artificial intelligence.

The way we do this is what we call the Actium-AI egg.

So the yolk of the egg is where all of the large language models are and we think there's about a quarter of a million large language models on the planet and that grows day by day and these are the ones that really are at the top end of town requiring huge capital investment and as some of you would note huge valuations whether they deserved or not.

Now so we use those as our yolk

We then wrap around that a private data protective membrane because the large language models of themselves can only access public data and they have.

They cannot access your private data unless you give it to them and that private data will then be used to train those large language models further.

So what we do is I de-identify your

your private data through that protective membrane, and then we put that inside our legal linguistics and our agentic AI scaffolds, and then we de-identify the results of that and put it either in your own secure data center or a third-party data center.

So the Atrium AI egg is not something that anyone else has, and it results in this heavily scalable

business model.

Now, we are a B2B business.

We are not a B2C business.

You will never be able to buy this at Harvey Norman or anywhere else like that.

And what we do is identify and team up with people that are already in the business of providing professional services or corporate services where there are large documentation requirements.

We call those alliance partners.

and we create with them an investment grade asset by setting up a special purpose vehicle in which they have an equity position.

So that then includes all of the massive data crunching that the large language model providers are able to do.

It includes de-identification and re-identification technologies.

Very importantly, it deals with the issue of legal and compliance veracity

So we get away from the garbage in, garbage out, hallucination type problems.

And then, because the applications of this are so heavily contextual, we scale the world for organizations that can fill up those needs as part of the agentic AI scaffold.

We then, because those people then need to market what they do using these technologies, we provide them with integrated high-speed marketing and investor networks.

So that is what Axiom AI offers.

And alliance partners may have five users.

They may have 15,000 users.

They may have 500 users.

But ultimately, it's a matter of how many robots we deploy through the existing networks of those alliance partners that generates the value inside Axiom.

So other people have said these sorts of things.

Dr. Thompson, who's a world authority on this,

And he recently hosted a world webcast on major class actions in the United States for unjust enrichment by the large language model providers.

And he says that we are the only Australian company, at least, that specializes in modern day corporate governance AI.

And there's truth in that.

And we are constantly approached by those that are involved in corporate governance to install our robots inside their organizations as alliance partners.

The journalist that is quoted there, I explained to him that we can read Magna Carta dated 1225 because he asked me that one of the criticism was that what about all of the precedents in the common law?

And we can do that because we can do it in 50 languages, including Old English.

How do we scale this?

Well, it's easily franchisable or licensable and we already have our first licensee

And the issue for investors always is scale.

How do you get scale?

How do you get speed to market?

And we do that by the Alliance partnerships and the licensee model which are already starting to be in place.

Some examples would be for those of you that are involved in the law in litigation finance and Australia is now the second most litigious country on the planet in terms of litigation financing class actions.

only behind the United States and not far ahead of Germany, I have to say.

And the documentation, these are absolutely huge.

And to some extent overnight.

So we've been asked recently, can we read 16 and a half thousand emails for a court hearing the following morning?

And we did.

So these are just one of hundreds or thousands actually of applications where documentation volume

overwhelms the organisations and the lawyers and the compliance people, the regulators, people at board level, for instance, in compliance committees or advisory committees.

And this is the technology to bridge those gaps for them.

So that is Actium AI.

Thank you, Angelina.

Can you explain Active AI's business model?

And how does your company plan to generate sustainable revenue?

And what are your projections for growth over the next three to five years?

Well, the growth and getting the scale quickly, because we do want to be able to become a listed public company, is done by the B2B component of the alliance partner model.

because our alliance partners are selected because they already have a lengthy client list, a lengthy user list that can benefit from the action robotics.

So once we've taught the alliance partner how to use the robotics, then they're in a position to implement those robotics through their user base.

And as I mentioned, it might be 500,

and it might be 5,000, it might be 50,000, it could be half a million robots.

So we're able to do that.

And then to turbocharge that process, we sell through licensees who are licensed to do exactly the same thing and are able to share in those sorts of benefits.

So the way that speed to market is achieved are those two mechanisms.

And the way that we get value for shareholders inside Actium AI is A, through a process of digitizing all of the documentation sets and processing those.

And that's called tokenization.

And every time that the machine ticks over, Actium AI gets a fee.

And the fee actually is a world price for that.

The second way we make money for shareholders is that

the equity shares in the various alliance partnerships and there may be 50 of those, there might be 500 of those over time.

They increment in value over time and we equity account those into our own balance sheet.

The AI space is highly competitive.

What makes your technology unique compared to other AI solutions and how do you plan to maintain the edge as the industry evolves?

Well, it's realistic to say that there is always competition and there always will be competition.

Our technological competitive advantage

is the fact that no one else has the Archimedes AI robotics.

No one on the planet and no one has integrated that with all of the other applications or agents that are called in the scuffle process to produce the entirely contextual results that people need to get.

The other way we do it is that we scour the press every day for who's doing what in

this technology space around the planet.

And we now have a large database of who's who in the zoo, because we find that every question we get is, can you do this?

Can you do that?

And sometimes we have to source technologies from overseas, whether it be from Europe or the UK or the United States or Canada or wherever.

And we integrate those into the end user application set.

Now,

you have to have an organisation that a. knows what to look for and b. what to do with it and is then able to integrate that with its own introductory archimedius robotics.

Thanks for your time Dr Muehlhaus.

Thank you Angela, it's a pleasure.