B (3:13)

So one thing that I also think is really interesting is, you know, from our coverage, Amazon has released a lot of new feature functionality at Unboxed and really throughout the whole entire year. What does it mean to essentially partner with a group that keeps on leveling up? They're learning, I imagine, from their partners and being inspired by their partners, but at the same time you get to follow their roadmap and get some ideas, hopefully from being part of that broader partner network. If you want to say.

C (3:43)

Totally, I think it's a great question. So first, obviously Amazon is making big bets in AI. There's a lot of really, really smart people at Amazon and if you're a large technology company that's not going on all in on AI right now, that would definitely be very yellow and red flaggy. So. So of course Amazon is doing this like we can't view their AI initiatives as competitive for us because AI is simply the way in which we interact with software right now. But I think if you look at many of the announcements that Amazon made associated with their ads agent, in particular, the messaging around the ads agents is fundamentally different than the way that we present our product to our customers. So I saw a variety of quotes from the VP of Product, Kelly McLean at Amazon that said the ads agent is effectively an easy button that's democratizing access to the Amazon DSP for more new advertisers, which is great. It's an easy way to get access to some really powerful tools that Amazon has built and is using agentic AI as the mechanism for doing so. So in effect, the ads agent is raising the floor on advertisers in the Amazon tsp when we're doing the exact opposite. We're raising the ceiling on the most sophisticated advertisers. So within agentic AI, we no longer buy software, we hire software, we train and we manage it just like we would a team member. And many of the most sophisticated advertisers that we work with have very prescribed and high conviction ways with which they want to operate the Amazon DSP that needs to be completely tailored to them the same way that they would train and manage a team member in operating the Amazon dsp. And it's our job, the technology company, to give our customers the tools to train their AI agent in exactly that manner. So whereas Amazon ads and Their AI efforts are going to build one to all or one too many. We're very much building one to one and that we are not building directly one to one, but we're giving our customers the tools to make it a one to one, unique, bespoke, enterprise level experience. We actually believe that Amazon's initiatives with agentic AI is a tailwind for Jiji because now everyone is recognizing that they have to have an AI strategy and they have to leverage AI tools to achieve the outcomes that they hope to have for their clients. But the challenge is that many of the most sophisticated advertisers need something that's bespoke and unique to them, the same way that their team members that they hire onboard would be unique and bespoke to them.

B (6:20)

That makes a lot of sense. I know another thing that you've kind of helped clarify to people a bit and I definitely want to talk about your background because I find it really interesting, but you said that there's a lot of confusion around what a vertical AI agent actually is. And in your view, maybe you can talk to us about what are the core building blocks. We can get a little nerdy if you want again.

C (6:42)

Sure, sure.

B (6:43)

Like models, APIs, all the different, you know, nuts and bolts that matter most when you're trying to, you know, ship real innovation these days.

C (6:51)

Yeah. So an interesting thing is everyone now has AI, they're an AI first company. And I think from a buyer, whether you're selling to agencies or brands, the buyer needs to understand like, where is there like true AI or what are the companies that are more marketing versus substance. And I think about it in a variety of different ways. So like one, we were talking to a large Holdco leader and they mentioned asking me for guidance, like how should I think about using all of these agents that are now like pitching us agents for this, agents for that. And I think we've mentioned this, like people hire Gigi to be a media manager for the Amazon dsp. And so other agents might be a report analyst, SQL generation agent. Others might be an audience planning, like holistic planning across media channel agent. Generally speaking, these are roles that humans perform at your company. And if, if the agent that you're hiring is able to enhance that role that requires you to hire less of those people in those seats, then that's a true vertical AI agent. So I would use that heuristic for making those sorts of buying decisions. And so the role that we have is many agencies right now have tons of open roles and it's difficult to Hire folks that have X number of years of experience navigating the Amazon DSP or other enterprise DSPs. There's a lot of attrition in this industry. And so like what we say is like don't hire those two roles, like give Gigi a shot first and then like see how it can enhance the existing team that you have. So that's, that's one heuristic. The other is just on pure token usage. So like another way that people can assess things is if that company isn't coming to you with a business model that's aligned to token usage, then they're likely not actually offsetting as much tasks to AI in which their use of LLMs is likely very limited because token usage comes with costs. And that's natural to the business model that people just need to have on a consistent basis. So those are two heuristics for making buying decisions within vertical AI agents. And then I think the building blocks of actually building a vertical AI agent are threefold, which is one, obviously the models are the models. And so when we began this journey with Gigi a little over a year ago, we just used GPT5, the latest version of GPT5 across the board for consistency and reliability and uniformity and speed to market. But we've quickly learned that relying on a single model and relying on out of the box frontier LLMs is not actually optimal for designing the use cases that we want for our customers. And so we now use a constellation of models across frontier LLMs like GPT5. Or we begun to fine tune open source models and in the future we actually might begin to build our own model specifically designed for media buying and measurement. The other building block would be RAG retrieval augment generation. So RAG is content and data that doesn't live on the open intranet that allows you to customize a GG or a vertical AI agent beyond the corpus of general intelligence that an LLM will have. So what are examples of that? We at GIGI have created thousands of little bite sized RAG documents that we train Jiji to be able to understand how to read certain metrics or execute certain tasks within the Amazon dsp. In addition to the RAG that we produce to train the GG agent with a baseline level of knowledge, we get all of our customers to say, hey, what are the training docs, collateral process docs, standard operating procedures that you would send to a new team member and how could we begin to train your GG agent to be unique to you? And that's all through rag. And then the next part is tool calling. So similar to a handyman or woman with a set of tools, if I ask you to put a nail on a wall, you need a hammer to do that. And so an LLM needs access to tools. And generally speaking, within the lens of media buying and measurement, the tools that the GG agent has to call are API endpoints or metrics within Amazon's ad tech that we can then instruct Jiji to, hey, if a customer asks you to do this, you need access to this tool. A lot of people have questions around mcp, and so mcp, you were gonna. Was that the next question?

B (11:27)

Well, it's funny because I was gonna say, like, I think Rags and MCP servers are gonna be like, everybody will know what they are within this industry a year from now. Right. Because it's like that these are cores to unlocking additional growth.

C (11:43)

Totally. Like, anyone working with an AI needs to understand nuances of RAG and rag orchestration. And then a good way to just think of MCP servers is rather than us integrating with every single API endpoint within Amazon's ad tech as a tool call, we can use. And Amazon announced an early version of their MCP server. And so we can say, hey, Amazon Ads Agent, these are the problem that we need solve for our customers. Can you demonstrate which tools we should call to achieve the outcome that we're hoping to achieve for our customer? And then the MCP server will expose the appropriate tools for that outcome, rather than us having to deterministically call a certain API endpoint for that tool. So those are, generally speaking, the building blocks. The other big piece with it was associated with RAG is customer advertising. Data is obviously RAG as well. That doesn't live on the Internet and allows for someone to customize their GG agent. So across, like customer best practices and customer data, that's how a GG agent gets customized for each individual enterprise that we work with. And a really important design decision that we've made that everyone asks us is like, are you taking all this data and best practices and laddering it up to a master model? And the answer is emphatically no. While we understand that that could have probably been beneficial for the overall efficacy of Jiji's general intelligence, we think it's really important, both a functional and legal perspective, to ensure that all of our customers rag, whether it be data or content, is unique to them and bespoke to them.

B (13:14)

Yeah, and I think that that's obviously a differentiator. Right. You have organizations that are going to want to fine tune and they're going to have different business rules and a lot of different things that they're obviously going to demand it. Right. So it becomes a little bit of a selling point to keep those things separate. But when you were talking before, I want to go back to something about the complexity of everything that you went through. And I was going to joke and say, you sure you've only been in ad tech for eight years? Because you mentioned your own journey when we were talking offline. From lawyer to somebody who's pretty fluent in these tools, I'll give you a 98 out of 100. What advice would you give leaders or teams who don't come from technical backgrounds but need to build or use AI systems which, let's face it, as is most people these days.

C (14:03)

I was chatting with a friend, one of our investors who mentioned how, how I'm enjoying being a founder and actually this is my third time being a founder and, and, and you know, there are trade offs being a founder. Like this is, we are at an early stage. We're certainly not killing it by any means, but we're doing well at a very early stage. And I said, you know, like, I'm enjoying it. I'm really passionate about solving this problem. I love our team. But more importantly, like, I feel like it' a gift that we've been given like our respective jiji team to be able to build at the forefront of all of this because we're just learning so much. Like whenever we hire a new team member, we like to say like, you're going to get a black belt in AI by becoming a member of the jiji team. And I think like one of the things that I really enjoyed and I think is one of the things that's allowed all of us on the GG team to be successful is that we just have this insatiable curiosity to learn. And we began this journey, as I said, we knew we needed to go on on the AI. We didn't know what that meant and it's really been an incredible amount of work across our 14 person team to go from AI novices using ChatGPT to being getting a 98 score from you, Jeremy, in our understanding of technical AI terms. And so it's just a lot of hard work and curiosity.

B (15:26)

I want to give you room to grow. I don't want you to get a swelled head. But no, in all seriousness though, given where you are in terms of sitting within this ecosystem and growing and getting the recognition through the partner awards, I wanted to get your perspective on this. Looking into 2026, what's next a for Jiji but also for the broader AI powered media world. Let's just say from Amazon DSP advancements to, you know, obviously data collaboration continues to be something that's entering the lexicon. You know, the average marketing practitioner is now very well, you know, familiar with these terms. So what, what can you tell us? Do you have any bold predictions? Maybe we can kind of start with Gigi and then kind of lens out.

C (16:17)

Really, really simply on the Jiji side. We want to, we want to expand aggressively to other forms of media buying and measurement. We very extensively buil product to be. While the rag, orchestration and tool calling that GG has access to right now is all associated with the Amazon dsp, we certainly want to expand to many more mechanisms buying media on other enterprise DSPs as well as on site retail media. That would be the natural extension of our product and we're working very hard to achieve that. I'm going to get back to the AI piece and on like a bold prediction and not necessarily a bold prediction, but what I'm looking to work towards is we began this conversation of understanding the tasks that someone in a media management role at an agency has and how can we automate that. And so if we talk to someone, the number one task that Gigi has not been able to solve is deck creation, automatic deck creation. The amount of time that people spend building decks for their clients is probably the biggest time suck that people have right now. And we sought to solve that problem in 2025. We worked really hard. One of our top engineers worked for a couple months to try and solve that. So we have all of this advertiser context, we have all of this data, we have all of the inputs that they've given into Jiji and how can we automatically create a deck based on the template that they've given us? And you know what? The technology wasn't there like, we tried really hard. This speaks to the evolution of the models and what's unlocked and the type of work that you can begin to evolve in the work that you're assigning to agentic AI. But I've spent the past 10 days or so on a nano banana rabbit hole, which is Google's AI image generation model. And I think that we're getting really close to having automatic deck creation and having phenomenally produced decks at human level. Deck creation that could offload a ton of work. And so we're really excited to hopefully provide that to our customers. It's a hard problem to solve. And again, we tried in 2025 and for 2026. I know that this is like very in the weeds, but. But deck creation speaks more broadly to like, something that every single person and many companies offers. And I think that once the technology of deck creation, image creation, becomes commoditized and it gets back to all of these inputs that a vertical AI agent uniquely do and have to build the best possible deck for their verticals.

B (18:53)

It's been a time suck for me in the past. So I cosign. I think that's terrific and I think it's a lesson to everybody listening to this, right? Which is you need the vision for the thing that you want to create. And AI might not actually be there yet, but it's moving so incredibly fast that you need that vision and then the AI will probably get there at some point in the near future. But listen, this was a great conversation. I can't take up any more of your time because I want you to get started working on that deck creation for all of us at home. But Adam, this was really fantastic. So thank you so much again for making the time to speak with us.

C (19:32)

Thank you. Appreciate it.

A (19:33)

That's it for today's episode. Thank you so much to Jeremy and Adam for the conversation. Thanks, of course, to the production crew. And thank you so much to everyone listening into the special Remarketer podcast miniseries, AI Driven Media Management with Gigi made possible by Amber Amazon ads. And of course, happiest of holidays to everyone.