So the result was a combination of short term recommendations that were immediately Now I'll hand over to Andrew, who will talk you through problem one.

Okay.

Can you hear me? I just need to do a quick check.

Yeah, we can hear you.

Great.

Thank you.

Okay.

I'll now pass over to Andrew for the longer term recommendations.

But whatever you'd like to share would be amazing.

No, thank you.

Thank you for the introduction.

That was great.

So at Sesame, or Sesame, depends on which continent you live in, in terms of how you pronounce it.

But at Sesame, what we do is we're a brand automation platform or a brand management platform.

And so what we do is we ingest creative content into our platform and then marketers or advertisers can take that content and they can extrapolate it out across different media channels, across different file formats and tailor that content for different demographics, basically different audiences, right? So we're trying to provide hyper localized content at scale.

So I'm curious your thoughts on AI and how the semi, am I saying it right, is using AI from that perspective?

And so it's super valuable there as well.

Yeah, I totally agree.

And I think like part of the debate on it this past year, right? It's like, of course, like MCP crashed onto the scene, like so many technologies, right? Maybe got a little bit ahead of its skis in terms of the hype wave.

But I think it really is the right tool for the job for so many types of agents that folks are building.

And I've even noticed this, like Cornelia and I have been talking about it here at the show.

I think because AI coding agents are the most successful and widely adopted type of agent so far in the industry, right? I think a lot of engineers and maybe even product leaders sort of have some tunnel vision on that type of architecture being the best way to build an agent.

And so a lot of the discourse around sandboxes and giving your agents the ability to write any kind of arbitrary code, pull in any kind of library, That works really, really great for these long-running agents using the most intelligent models, working on very, very complex tasks.

I've got the podcast going in the car, got the podcast going, but like coming to events like this and seeing that this morning was, was pretty interesting.

So I was wondering maybe to just kick us off, if you want to kind of introduce what you guys do by way of what was the problem that you ran into or that you saw people running into that inspired you to start building what you're working on now?

I think for, do you want me to? Okay.

I think in the beginning for me, right, the research was started in 2023 and then the company got founded in 2024.

But during that early position, it was more so the curiosity and the complexity of the problem that drew me into it and invited the possibility of solving something that seemingly everyone else had come to accept as just being a, know a general trade-off right or a general cost of using the technology so looking at that through a lens of why does it have to be this way right why does why does it have to be generally accepted that this is going to be the paradigm moving forward in the industry right and we just have to accept these flaws as they are and find supplementary ways around them rather than dealing with the problem at its core.

That was a big factor to me, right? So it was something where I think, how do I put this? It, historically I've been drawn to complex problems.

So back in like the 2013, 2014 time, I was just getting into the field of artificial intelligence, right? And it was something where, I didn't know the Transformers were a thing then.

So I was coming in completely new to the field, very green.

I have like an analogy I want to make, but I want to explain that analogy after y'all explain what the solution is, because when it got it, I thought it was kind of hilarious and ding on my head, but I'll save that for a, um, uh, and after.