How Dappier Is Building AI Agents for Media Companies

Summary

Marketecture Podcast Episode Summary: "How Dappier Is Building AI Agents for Media Companies"

Release Date: May 5, 2025
Host: Ari Paparo and Eric Franchi
Guests: Dan Geikman (CEO, Dappier) and Krish Arvapalli (CTO, Dappier)
Podcast: Marketecture: Get Smart. Fast.

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Introduction

In this insightful episode of the Marketecture Podcast, hosts Ari Paparo and Eric Franchi engage in a deep conversation with Dan Geikman, CEO, and Krish Arvapalli, CTO of Dappier. Dappier, a burgeoning tech company founded just under two years ago, is making significant strides in the integration of AI agents within media companies to enhance content monetization and distribution.

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Dappier's Mission and Role in the AI Landscape

Dan Geikman [01:20]

Dan Geikman opens the discussion by outlining Dappier's core mission:
"At Dappier, we view our role as monetizing the shift that's happening from pages to agents. We help media companies, brands, websites, ultimately get their content into a format that AI companies can interact with, help them monetize that transaction, and basically launch AI agents across their owned and operated sites and every consumer channel." ([01:20])

This shift signifies a transformative change in how consumers interact with and discover content, moving from traditional page-based interactions to dynamic AI-driven engagements.

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Current Opportunities and Deployment Strategies

Dan Geikman [02:08]

When questioned about the immediacy of the AI opportunity, Dan explains:
"We structured our business to deal with the real opportunity right now is to help folks that have audience actually deploy agents to their owned and operated properties... We give media companies the tools to deploy those on their sites, monetize them in various surfaces." ([02:08])

Dappier is actively enabling media companies to integrate AI agents across their digital properties, allowing for enhanced content discovery and monetization through existing revenue streams like advertising.

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Integration with Social Platforms and Enhanced User Experiences

Dan Geikman [03:22]

Ari probes into how Dappier's technology interfaces with platforms like X (formerly Twitter) and Instagram. Dan elaborates:
"On the other side of their X account is their AI agent. So essentially you can tag your account and it'll interact with you so you can have those conversations... on WhatsApp or Twitter channel." ([03:45])

This integration ensures that brands maintain a consistent and interactive voice across multiple consumer touchpoints, enhancing engagement and user experience.

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Content Training and Data Utilization

Krish Arvapalli [05:56]

Krish delves into the technical aspects of how AI agents are trained:
"We have customers coming in with RSS feeds, MRSS feeds, PDFs, documents, they could be ebooks, publishing content... that's typically what we use as our data pipeline." ([06:01])

By leveraging diverse data sources, Dappier's AI agents are contextually aware, enabling them to provide richer, more relevant interactions for users navigating content.

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Company Overview and Growth

Dan Geikman [06:24]

Dan provides a snapshot of Dappier's current status:
"The company is just under two years old... about seven full-time people. We've raised just under $2 million in capital to date." ([06:29])

Despite its nascent stage, Dappier has attracted notable clients, including mom.com and several news publishers like 9 and 10 News in Michigan.

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Customer Success Stories and Scaling Up

Dan Geikman [06:45]

Highlighting early successes, Dan mentions:
"We're live with news publishers like 9 and 10 News in Michigan, all of the Morgan Murphy media properties around the country... dozens of media brands launching in the coming days and weeks." ([06:45])

These deployments showcase the practical applications of Dappier's AI agents in real-world media environments, driving engagement and revenue.

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Business Model and Monetization

Dan Geikman [08:00]

When discussing Dappier's business model, Dan states:
"We do everything on a revenue share usage basis. So essentially if those agents are monetized, we work on a rev share basis with publishers. If they just want to use our tech infrastructure, there's a usage fee that is volume-based." ([08:21])

This flexible model ensures that clients can either share revenue generated through AI agent interactions or opt for a usage-based fee structure, catering to varying business needs.

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Competitive Advantage and Market Positioning

Dan Geikman [08:00]

Dan highlights Dappier's unique position in the market:
"Our number one competitive advantage is we've built the full-stack solution with monetization. We help you build, distribute, and monetize them end-to-end." ([08:00])

Unlike competitors that may focus solely on building AI agents, Dappier offers a comprehensive solution encompassing creation, distribution, and monetization, thereby providing greater value to media companies.

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Defending Against Industry Giants

Dan Geikman & Krish Arvapalli [08:49]

Addressing concerns about competition from tech giants like Amazon and Google, Dan argues:
"Any one of those players can do one thing well for themselves. You need to think about this from a new distribution paradigm and you need to think about the Rails to manage and monetize that." ([08:49])

Krish adds:
"We recently launched Adapt your Ms. Server which is becoming the Rails of content distribution into AI... RTMP server is the pathway to that." ([09:29])

Their approach emphasizes specialized infrastructure and partnerships that large companies may not prioritize, allowing Dappier to carve out a niche in the AI-driven content distribution space.

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Simplifying Technical Concepts for Broader Understanding

Dan Geikman & Krish Arvapalli [10:01 - 10:57]

In an effort to make their technology more accessible, Dan provides a simplified explanation:
"Agents have data they're trained on, but they are not current. To inject net new data that's relevant and timely, you need to connect to a third-party source. MCP is a standard that's available so any data source can talk to any LLM." ([10:23])

This ensures that AI agents remain up-to-date and relevant by continuously integrating fresh data from various sources.

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Lightning Round: Quick Insights

1. Number One Competitive Advantage
Dan Geikman [07:53 - 08:00]
"We've built the full-stack solution with monetization, helping you build, distribute, and monetize end-to-end."

2. Business Model
Dan Geikman [08:21]
"Revenue share usage basis: revenue sharing for monetized agents or volume-based usage fees."

3. Competition with Big Players
Dan Geikman [08:49]
"Big players can do one thing well, but we provide the infrastructure to manage and monetize content across the AI ecosystem."

4. Simple Example of Technology Usefulness
Dan Geikman [10:23]
"AI agents need current data; we connect them to third-party sources to keep information relevant and timely."

5. If Dappier Were an Animal
Dan Geikman & Krish Arvapalli [10:57 - 11:04]
"Horse."

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Conclusion

The episode offers a comprehensive look into how Dappier is pioneering the integration of AI agents within media companies, focusing on content monetization and distribution. With a clear vision, innovative technology, and a strategic business model, Dappier positions itself as a vital partner for media brands navigating the evolving digital landscape.

Listeners gain valuable insights into the practical applications of AI in media, the company's growth trajectory, and the competitive landscape, making this episode a must-listen for professionals in advertising, marketing, and technology sectors.

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Notable Quotes:

• Dan Geikman [01:20]: "At Dappier, we view our role as monetizing the shift that's happening from pages to agents."
• Dan Geikman [02:08]: "We give media companies the tools to deploy those on their sites, monetize them in various surfaces."
• Krish Arvapalli [05:56]: "We have customers coming in with RSS feeds, MRSS feeds, PDFs, documents..."
• Dan Geikman [08:00]: "Our number one competitive advantage is we've built the full-stack solution with monetization."
• Dan Geikman [08:49]: "Any one of those players can do one thing well for themselves."

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Learn More:

For those interested in exploring the full podcast episode and accessing a library of executive interviews, visit marketecture.tv. Subscribe to their weekly newsletter for strategic insights on the latest industry news and join their vibrant Slack community at @adtech God.com.

Transcript

[Ari]

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[Dan Geikman]

Absolutely. Thanks Ari. Appreciate you having us on.

[Krish Arvapalli]

Sure.

[Ari]

So what do you do?

[Dan Geikman]

So at Dapier, we view our role as monetizing the shift that's happening from pages to agents. We help media companies, brands, websites, ultimately get their content into a format that AI companies can interact with, help them monetize that transaction, and basically launch AI agents across their owned and operated sites and every consumer channel. So we think that there's a pretty interesting shift that's underway in terms of how consumers interact and discover content. And we think that there's a lot of folks out there that need help in navigating that shift and this kind of new blue ocean of AI opportunity.

[Ari]

And is this a real opportunity right now where there's somewhere you can deploy these agents or are you building ahead of potential infrastructure changes?

[Dan Geikman]

Yeah, great, great question. So we that's actually we structured our business to deal with the real opportunity right now is to help folks that have audience actually deploy agents to their owned and operated properties where they can monetize through the existing revenue supply chain of the Internet, dip their toe into the kind of world of AI agents and also bring kind of consumers the experiences they're starting to enjoy across other consumer apps when they interact with things like Instagram, Facebook, LinkedIn, Amazon that are all introducing copilot like experiences. So we give media companies the tools to deploy those on their sites, monetize them in various surfaces. So in Search in Article Dedicated Answer pages and basically allow them to compete at parity with AI experiences monetized largely with advertising. And we also see really cool use cases coming to market they could take advantage of today, for instance, launching their AI agent behind their X account. Right. Or in their WhatsApp and Telegram. So we think brands, media companies should be able to talk to their consumers wherever they are in a single voice. And I think that's one of the key promises of what AI agents can do.

[Ari]

So right now, so you have the website maybe the easiest thing to understand. Lots of folks helping brands create agents that are websites for customer service, for upsells, things like that. I assume that's part of your offering. But then you mentioned X. So how would a brand user technology in conjunction with a platform that. Like X or Instagram.

[Dan Geikman]

Yeah, so essentially, yeah, essentially on the other side of their X account is their AI agent. So essentially you can tag, you know, tag your account and it'll interact with you so you can have those conversations. The brand could be tweeting, so to speak, because I don't know what the nomenclature is for X posts, but tweeting, so to speak, on your behalf and then interacting with customers. The same thing can happen, you know, on a WhatsApp or Twitter channel. Then as far as the, you know, the actual website deployments go, I think there's a lot of folks working on customer service kinds of deployments. We're specifically focused on content discovery and monetization in those spaces. So our agents are context aware. They help you kind of ask follow up questions that both either help you navigate the article or page you're reading or augment it with other data and kind of provide a richer user experience.

[Krish Arvapalli]

I just wanted to add a few thoughts there. It's whether it is behind an axe or on a page, the agents can take it a step further. So they can push them to some, you know, subscribe to a newsletter or schedule a demo or kind of have a conversation really. And so those interaction types that are a little bit more, and you know, a little more kind of engaging is kind of what we, what an agent can do automatically. So we do that for brands, we do that for anyone with a website. Really?

[Ari]

Do consumers know they're speaking to an AI?

[Krish Arvapalli]

Yes, I mean they typically do because they're, you know, they know that we market like for example, we market as Ask, you know, a brand. Right. So for example, we work with mom.com which is one of our publishers websites. This is Ask mom, right. It says an assistant for anything parenting. Right. And we also kind of like Dan said, we, the agent answers any parenting questions and things like that. It's on their website. It learns the content, it learns the knowledge that they have and it actually pushes them to do something with it. Right.

[Ari]

And what's the corpus that it trains on.

[Krish Arvapalli]

Really? I mean it could be. So you know, we have customers coming in with RSS feeds, MRSS feeds, PDFs, documents, they could be ebooks, publishing content, it could be other, any kind of analytical information that you want the agent to be trained on. But that's typically what we use as our data pipeline.

[Ari]

What's the size and age of your company? How many employees? What sort of funding have you done?

[Dan Geikman]

Yeah, so the company is just under two years old. So yeah, about 18 months old, seven full time people. We've raised just under $2 million in capital to date.

[Ari]

Nice. Do you have any customer success stories you're willing to share?

[Dan Geikman]

Yeah, I mean, so we're, we're, you know, we're kind of, again, we're just kind of starting with some of these exciting deployments. But Krish mentioned Mom.com, we're live with news publishers like 9 and 10 News in Michigan, all of the Morgan Murphy media properties around the country. And we're just kind of scaling up. So we're excited about these kind of various use cases, but dozens of media brands launching in the coming days and weeks and starting to kind of bring these new interactions to market. And I think just one thing to circle back to, right. I think the reason we built this, the solution, the way we have is that there is a lot of opportunity today with the audience where it is today and the ability to deploy these agents to consumer services. We all understand. At the same time, I want to really make sure we highlight this. Our thinking is that there is tech infrastructure that's being built and needs to be able to access this content with a sustainable business model. And so we're playing the long game here of both providing the infrastructure to serve consumers today and thinking about how content gets monetized, distributed, discovered in the future.

[Ari]

All right, let's do a quick lightning round. So quick questions, quick answers. What's your number one competitive advantage?

[Dan Geikman]

Our number one competitive advantage is we've built the kind of the full stack solution with monetization. So there's a lot of folks that are building rag and helping folks build agents. Our idea is we want to help you build them, distribute them and monetize them. And doing that end to end we think is pretty important.

[Ari]

I'm sorry, that just brings up a question I forgot to ask the business model. So how do your publishers pay you or how do you pay them?

[Dan Geikman]

Yeah, so we do everything on a revenue share usage basis. So essentially if those agents are monetized, we work on a rev share basis with publishers. If they just want to use our kind of tech infrastructure, there's a usage fee that is volume based.

[Ari]

Great. And then next lightning round question. So why won't the big guys, the Amazon, Google's, Facebook just do this and crush you?

[Dan Geikman]

Yeah, I think it's a great question. I think this industry is getting like all kind of new content distribution mediums becomes really, really complex and multifaceted and typically, you know, when the distribution complexity scales, you know you want a partner that can handle your content distribution management, monetization. Think about it like content management system for distribution into the AI ecosystem from a single place. So any one of those players can do one thing well for themselves. You need to think about this from a. You know, we think about AI as a new distribution paradigm overall and you need to think about the Rails to manage and monetize that.

[Krish Arvapalli]

I did want to answer that real quick. So we also have the, we recently launched Adapt your Ms. Server which is becoming the Rails of content distribution into al. So effectively that allows us to be behind any ecosystem whether you're on open, whether it's the OpenAI ecosystem or the anthropic ecosystem, they all need content and data coming out of the dapier network. So RMTP server is kind of the pathway to that.

[Ari]

Probably most people listening to this have no idea what you're talking about. Can you give a really, really, really simple example of why that's useful?

[Krish Arvapalli]

Yeah, I mean so anyone that's building an agent right in the Gentex universe or an LLM that wants to access data outside. Right.

[Ari]

Okay, hold on, I'm interrupting you right here. Dan, take over please. Give me a really, really, really simple example of why this matters.

[Dan Geikman]

Yeah, basically agents have data that they're trained on. LLMs have data that they're trained on, but they are not current and up to date. If you want to inject net new data that's relevant, current, timely into that answer, you need to connect to a third party source. MCP is a standard that's being available so that any data source can talk to any LLM.

[Ari]

Okay, thanks. Sorry to interrupt your Chris, but you know I need to dumb down for.

[Dan Geikman]

Me what I was saying was it was crazy and Chris practiced simplifying that before.

[Krish Arvapalli]

I know. Yeah, I get to be a little bit too technical, but yeah.

[Ari]

Yeah. Last question. If Dapier was a animal, what animal would it be?

[Dan Geikman]

Dapier was animal. What animal would it be?

[Krish Arvapalli]

Oh, man, that's a good question.

[Ari]

We ask that every. Obviously you don't listen to my podcast because we ask that of everybody.

[Dan Geikman]

That's a great question. Let's call it. I don't know, let's call it a horse.

[Krish Arvapalli]

Yeah, I was going to say horse, too. So there we go.

[Ari]

Okay, we'll take horse. All right. Dan and Chris, thank you for joining us.

[Krish Arvapalli]

Yeah, thank you.

[Dan Geikman]

Appreciate it.

[Krish Arvapalli]

Appreciate it.

[Dan Geikman]

Foreign.

[Ari]

Thank you for listening to the marketecture podcast. New episodes come out every Friday and an insightful vendor interview is published each Monday. You can subscribe to our library of hundreds of executive interviews at marketecture tv. You can also sign up for free for our weekly newsletter with my original strategic insights on the week's news at News Marketing. And if you're feeling social, we operate a vibrant Slack community that you can apply to join@adtech God.com.