---
title: "The Agents Are the Easy Part"
episode: 126
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Bastien Botella"
guest_title: "Head of Growth & Marketing, Fountain"
date_published: 2026-09-25
date_modified: 2026-09-28
duration: 00:44:48
word_count: 6383
topics: ["ai-in-gtm", "demand-generation", "revenue-operations", "brand-positioning"]
canonical_url: https://www.leanscale.team/knowledge/podcast/bastien-botella-fountain-agents-are-the-easy-part/
source: "LeanScale Knowledge Hub — https://www.leanscale.team/knowledge"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# The Agents Are the Easy Part — Full Transcript

> Episode 126 of The LeanScale Podcast, with Bastien Botella.
> Published September 25, 2026 · 00:44:48 · 6383 words.
> Machine-transcribed and **not diarized** — speaker attribution is inferred, so verify
> attribution against the audio before quoting a specific person.
> Structured breakdown: https://www.leanscale.team/knowledge/podcast/bastien-botella-fountain-agents-are-the-easy-part/

## 00:00 — Cold open + intro

**[0:00]** And we get the data again, back into the data lake, and everything starts on ARR.

**[0:06]** And everything is really built just for one single goal, just build more pipeline.

**[0:11]** My guest today is Bastian Botella, who runs growth and marketing at Fountain, the platform

**[0:16]** behind high volume frontline hiring for companies like El Pollo Loco, Bojangles, and UPS.

**[0:23]** And so listing all the data, trying to understand how we can join all this data together, and

**[0:29]** just documenting, has been the first step that we took.

**[0:35]** And that has been the key step.

**[0:36]** Without that, we would be used to trying to make sense of all this data.

**[0:39]** My hunch or assumption is we're enjoying a lot of these models that are being subsidized.

**[0:48]** A lot of teams are using lots and lots of tokens.

**[0:52]** And I'm going to contradict myself here, but I think it's perfectly fine to just roll with

**[0:56]** whatever is the most effective, because velocity and speed to value is really the coin, right?

**[1:07]** That is the first time I have heard the importance of velocity, and it's making a ton of sense.

**[1:15]** Everyone's talking about volume.

**[1:17]** Everyone's talking about the hacks.

**[1:18]** I don't want to have to wait two weeks until the next sprints.

**[1:21]** I think it's a cost of opportunity.

## 01:35 — The receipt: a LinkedIn ad find worth $10K in month one

**[1:35]** Bastian, let's start with a receipt.

**[1:37]** Your agents went through all of your LinkedIn ad data and came back with something about

**[1:42]** images versus carousels that your team hadn't caught.

**[1:46]** And it moved real money in the first month.

**[1:50]** Take me through everything that happened there.

**[1:52]** Hey, Han, from the first of all, thank you so much for having me.

**[1:56]** Super excited to be here.

**[1:59]** And also just excited to talk about marketing OS.

**[2:02]** And I think what you just mentioned here, these ads that we changed is really what shows

**[2:10]** the power of marketing OS.

**[2:12]** So let me just focus through what happened, actually.

**[2:17]** So when we launched marketing OS, and I'm sure we'll talk more about the actual operating

**[2:22]** system in a minute, but when we launched it, one of the very first tasks that was assigned

**[2:29]** to my team by the operating system was to just tweak some of our LinkedIn ads.

**[2:38]** We run a bunch of LinkedIn ads.

**[2:40]** We run quite a lot of them, and obviously my team is checking CPC, CTR, you know, like

**[2:47]** all the YouTube suspects, all the KPIs that we want to track in order to understand performance.

**[2:53]** What's the agents that's leading to inside marketing OS form was not so much just like

**[3:00]** a pure performance indicator.

**[3:03]** It was a correlation between the format and messaging of our ads, or actually a very specific

**[3:10]** set of ads for a very specific ACP, and how those leads that we get from those ads will

**[3:19]** eventually correlate into pipeline.

**[3:22]** So that correlation that agents found is a correlation that's made us save over $10,000

**[3:32]** on month one, which is crazy.

**[3:34]** I never thought it would do that.

**[3:37]** So we were actually the first one to be surprised by the flow of the system.

**[3:41]** Yeah, and I think some of the capabilities are really starting to get mind blowing, but

**[3:47]** it takes a lot of orchestration.

**[3:50]** So I think some people assume, hey, these models are just getting so powerful, they can start

**[3:55]** to do things on their own.

**[3:56]** Yes, to an extent, they're getting much better at reasoning.

**[4:02]** But you also have to build quite a bit of infrastructure to get the right context in

**[4:06]** play.

## 04:07 — Collectors, orchestration, action: the three layers of Marketing OS

**[4:07]** And I would love if you could maybe just walk through what some of the biggest pieces are

**[4:13]** and maybe some things that people who are trying to build out AI programs in the marketing

**[4:19]** space are probably missing.

**[4:22]** Yeah, absolutely.

**[4:24]** So we've been working with AI and agents for quite a while, for months and months and months

**[4:32]** in marketing, but also with our own products.

**[4:35]** We sell AI and we sell agentic solution to our customers.

**[4:41]** And so we've been really at the forefront of this agentic journey from the start.

**[4:49]** And about six months ago, we found that agents are super powerful, but we need some sort

**[4:57]** of garbage and we need systems to make sure that we can feed them the right data.

**[5:05]** And so based on this sort of insight that we got, we started thinking about what would

**[5:16]** the perfect operating system, what would it look like?

**[5:22]** And we came to the conclusion that there were like three big pieces to the system.

**[5:28]** And we built the system from those big three pieces and then we added more on top.

**[5:33]** But the big three pieces were a set of what we call collector agents and those agents

**[5:40]** have only one job, it's to go and fetch data, just collect data everywhere from Salesforce,

**[5:47]** from Gong, from all our marketing tools.

**[5:52]** So post-hoc, Google Analytics, all our marketing spans, so all our internal tools, but also

**[6:02]** external data, market signals, for instance, what our competitors do or what our customers

**[6:09]** do, right?

**[6:10]** So all that is put into one data lake and that's done by our collector agents, the set

**[6:17]** of agents that only job is to collect data and make sense of it.

**[6:23]** The second piece of the framework of the operating system was the orchestration, now that we

**[6:29]** have the data, we need to process it.

**[6:31]** So we have a very wide range of agents that only job is to get the data, make sense of

**[6:39]** it, understand how they can transform it, translate that into actionable items that

**[6:46]** will eventually lead to the outcome that we want.

**[6:51]** And then we have a third layer, that is the actionable agents that will take those insights

**[6:58]** from the orchestration layer and just deliver the outcome.

**[7:03]** And so those three layers, the signal correction, the orchestration that we come up with, all

**[7:09]** the recommendations, and the action agents are the three big layers, that's for marketing

**[7:13]** OS.

**[7:14]** And it's like a perfect feedback loop, when once the action agents deploy something production,

**[7:22]** then we get the data again back into the data lake and everything starts over.

**[7:28]** And everything is really built just for one single goal, just build more pipeline.

## 07:33 — Which layer was hardest to build?

**[7:33]** Which of those three components was the most difficult to build and may relay into most

**[7:42]** difficult to maintain as well, or they might be different?

**[7:48]** They are three very different layers, very different layers.

**[7:52]** I think the one that is oddly the easiest to build, I would say, is the action layer.

**[8:03]** Those are very literal agents.

**[8:07]** They get a scope of work and they just deliver the work.

**[8:13]** They get all of the attention though, right?

**[8:15]** They get all the glory of everything that happened and that's what people are posting

**[8:19]** on LinkedIn or talking about.

**[8:22]** Exactly.

**[8:24]** And that's actually the easy part.

**[8:27]** Those guys, those agents, they're just doing stuff all day long.

**[8:31]** They are just taking tickets and just delivering the tickets.

**[8:35]** And these, I think people have been, we've been building that for quite a while now.

**[8:39]** It was like the first really use case that came out of like the chat and the cheese and

**[8:43]** the clothes, you know, like building content, for instance, like writing articles.

**[8:48]** Those were the first use cases that were found.

**[8:51]** Same goes for coding.

**[8:52]** Coding a landing page has been something that we've been able to do for quite a while now.

**[8:57]** So that piece was actually pretty easy.

**[9:00]** I think that the data collection piece was actually pretty hard because it's not only

**[9:06]** about collecting the data, it's also about making sense of it.

**[9:12]** Like making sense of the data, cleaning the data, finding correlation in this data, right?

**[9:20]** Putting all this data into one single place so agents can make sense of the data and see

**[9:28]** that the user and just track a user from like the awareness level, like the user visited

**[9:34]** the website or landing page down to the user assigned and is now into the revenue buckets,

**[9:40]** right?

**[9:41]** And it's a customer.

**[9:43]** And there are like a bunch of steps in between.

**[9:46]** And that's actually not always very easy to track, at least not for a company that have

**[9:52]** been around for like 10 to 15 years, you know, it's a fountain has been around for 14 years.

**[9:57]** And, and, and it's, and it's hard to, to, to track everything like we are doing a pretty

**[10:03]** good job at tracking like anything from marketing and sales, the reconciliation, all these data

**[10:10]** with market signals and with like all the gun calls.

**[10:13]** And that's when things start to get, to, to get, to get hard and marketing spend as well.

**[10:20]** Agents can do that.

**[10:22]** And and I think that's key.

## 10:25 — List every source, document the keys

**[10:25]** It's been your approach to help the agents make the most sense out of the data.

**[10:30]** Cause I agree.

**[10:31]** I think that's, that's the hairiest part.

**[10:32]** It's like, what's the context of this data that the agent's looking at?

**[10:38]** What approach have you taken both?

**[10:40]** Maybe get as practical as like tools that you've had some of this context in or, or processes

**[10:45]** that you follow on a daily, weekly basis.

**[10:48]** How do you even approach it?

**[10:51]** The first thing that we've done when we started that was just released everything once.

**[10:57]** We just started listing every single data that we have, every single object, every single

**[11:02]** signal, everything.

**[11:04]** And then when we, when we worked out doing that, we try to understand like, what are

**[11:09]** the ideas?

**[11:10]** What, what things can we feed into the agents who agents can join data together?

**[11:17]** Joining data is really hard.

**[11:19]** It's really hard to do it manually.

**[11:21]** It's really hard to build reports with like several sources of data that are joined across

**[11:26]** one single AD because sometimes the AD is not like super obvious.

**[11:31]** And sometimes the AD needs to be sort of built.

**[11:33]** It's not like just a very obvious AD.

**[11:37]** And so, and so listing all the data, trying to understand how we can join all these data

**[11:43]** together and just documenting has been the first step that we took.

**[11:50]** And that has been the key step.

**[11:52]** Without that, we would be still trying to make sense of all this data, but being able

**[11:56]** to just list everything and have a source of truth where we have all the types of data,

**[12:03]** all the keys, and just having agents just making sense of all these data based, based

**[12:10]** on these documentation is what led us to success.

**[12:13]** And so today, when we, when we add more data, what we do is we do the same, we just add

**[12:19]** the entry to the list of data sources, data types, data objects, try to understand how

**[12:25]** we can find a key that we can use to reconciliation with and join with the rest of the data, and

**[12:32]** then the agents to the rest.

**[12:33]** The agents are exceptional at joining data together.

**[12:40]** So now that you have the hard part in place, you built the infrastructure, you have clean

**[12:44]** data, it's joined properly, you have context between the data, you have stuff that these

**[12:48]** outcome agents can actually operate on.

**[12:50]** I'm going to highlight again, for anyone listening, this is the hard part.

**[12:55]** All the outcome stuff, all the fun use cases you see is the easy part, the thankless roll

**[13:01]** up your sleeves, hard work happens here in this data model layer.

**[13:05]** But once you have that in place, we'd love to hear some of the things that your agents

**[13:11]** were able to do.

**[13:12]** I know we talked about the LinkedIn ad example, but I know there's so many things you're deploying

**[13:17]** these against.

**[13:18]** What are a few ideas that highlight the art of the possible?

## 13:21 — Expert agents and Fountain Watch: mining signals from the public internet

**[13:21]** Yeah, we have so many use cases of so many recommendations that come out of marketing

**[13:29]** OS.

**[13:31]** The way that we build marketing OS, and the way that the orchestrator works, is we build

**[13:43]** what we call expert agents within the orchestrator.

**[13:47]** The orchestrator just runs a bunch of agents that each have a specialty.

**[13:52]** They each are like super experts into something very specific.

**[13:59]** For instance, for ABM agents, we will have agents that are experts in one account.

**[14:08]** They're not only one account, they are experts in one account, and one accounts with a very

**[14:15]** specific state of the funnel.

**[14:17]** For instance, they would be experts of one single account for awareness, and they will

**[14:23]** just focus on finding recommendations for this account in awareness.

**[14:27]** Today, we'll use the market signals that we source.

**[14:31]** By the way, we have this tool at Fonten called FontenWatch.

**[14:36]** It's something we built internally, and it's like a signal engine that will just go and

**[14:43]** fetch all sorts of signals from the web, from the public internet.

**[14:48]** It will fetch the acquisitions that have been made lately.

**[14:51]** It will fetch all the press releases.

**[14:53]** It will fetch all the regulations that came out.

**[14:57]** All this stuff is also gathered into our data lake, into marketing OS with FontenWatch,

**[15:05]** and so those experts agents will use all of the data that sometimes talk about the very

**[15:11]** specific accounts, and just so happen that it sometimes is the account that they are

**[15:16]** in charge of, and they can leverage this data to tell us, "Hey, you actually need to launch

**[15:21]** this very specific ABM campaign," because we know that there was a change of leadership

**[15:27]** in that company, or we know that such regulation came out, and they are very exposed to this

**[15:34]** regulation.

**[15:36]** We launch our campaigns based on the recommendations that we get from those expert agents, based

**[15:42]** on all the data that we have in our data lake, and that just runs every day.

**[15:52]** We get all sorts of recommendations, but they are usually around, I would say, three big

**[15:57]** topics.

**[15:58]** The first one is website contents.

**[16:01]** I want to say website, I'm talking about website as well as landing pages, just web contents.

**[16:09]** Web content is one, the land generation is another one, so agents are able to give us

**[16:20]** recommendations and tell us, "Hey, you should re-launch that campaign alongside that landing

**[16:25]** page," and then the third is nurturing.

**[16:30]** We are very good at telling us, "Hey, you should re-launch that specific segments,"

**[16:36]** because that messaging that you can share with them is going to resonate today.

**[16:43]** Maybe not tomorrow, but at least today it will.

**[16:49]** I think it's really impressive that the orchestration can lead all the way to those outcomes, and

**[16:54]** then just produce something that's so relevant, context-driven.

## 16:59 — 55 agents, and why context size drives everything

**[16:59]** One thing I want to get into is, my hunch or assumption is we're enjoying a lot of these

**[17:10]** models that are being subsidized.

**[17:13]** A lot of teams are using lots and lots of tokens, putting lots of model power behind

**[17:19]** some of the tasks they're trying to complete or get done.

**[17:24]** We've built a really interesting model that I think is ahead of how most teams are using.

**[17:30]** I think most teams pick max account, max reasoning, just run everything through this.

**[17:36]** How have you set up the infrastructure that is really leveraging a lot of AI power to

**[17:43]** also operate as lean as possible?

**[17:46]** That's a great question.

**[17:49]** That's a question that we ask ourselves right in the start, because we know that setting

**[17:56]** up the wrong architecture and running with the wrong models can be very costly.

**[18:03]** We made a few tests.

**[18:05]** It took us some time to get it right.

**[18:07]** Even today, we keep iterating.

**[18:10]** We keep moving from one model to the next, also because models are evolving.

**[18:15]** We keep moving from one model to another, and we are still trying to fine-tune every

**[18:20]** single agent, because today we have 55 agents running in marketing hours.

**[18:29]** Those 55 agents, they can be classified into around seven to eight buckets.

**[18:37]** The orchestration, the collector agents, the QA agents, we have agents that do QA for us,

**[18:45]** so they're like loads of different agents.

**[18:47]** I would say that the key for us in the way that we build the architecture is that we

**[18:56]** didn't want an agent to have to look into a huge context.

**[19:04]** That wasn't really the plan, because what really triggers the costs and what also takes

**[19:12]** time and what also woke agents into a health and aging is the massive context.

**[19:21]** We wanted to make sure that only certain agents deal with big contexts, but most agents would

**[19:29]** deal with very, very, very specific data.

**[19:34]** Today only the collector agents deal with big contexts, and those ones have some pretty

**[19:41]** heavy models, but all the expert agents, all the action agents, actually most agents, even

**[19:49]** the QA agents, they actually run on pretty lean model, and mostly because the context

**[19:54]** they have is not as big.

**[19:57]** The difference in quality between Fable 5 and Sony 3 for QA, for instance, is not something

**[20:06]** ... It's not huge.

**[20:09]** It's not something that we would feel.

**[20:12]** The QA is still kind of on the same, because the QA agents has a very, very, very small

**[20:19]** scope.

**[20:20]** It just looks at the recommendation, and we check that every single number, every single

**[20:25]** figure in that recommendation checks out with the data lake, and that's it.

**[20:31]** For the expert agents, the expert agents will only fetch some of the data in the very small

**[20:38]** portion of the data, of the data lake.

**[20:40]** Same again.

**[20:41]** They have a very small scope.

**[20:42]** They have a bigger model than the QA agents, but still smaller than the data collector.

**[20:51]** The key for us was the context, and we worked really hard to actually build several layers

**[20:56]** and have a process where agents just hand off work to the next one.

**[21:02]** Every single agent has the scope, the context for which he only needs to take an action

**[21:07]** on.

**[21:10]** If anything highlights which part of the problem is more difficult more than that, I think

**[21:14]** that's the best example of using the lowest cost model on the action outcome expert agents,

**[21:21]** using the high cost models on getting the data collection and actually making sense

**[21:26]** of it.

**[21:28]** It makes a ton of sense.

**[21:29]** I just think a lot of teams, maybe we have another three months, six months, whatever

**[21:34]** that timeline is, I think we're enjoying some subsidized token usage right now.

**[21:39]** I'm assuming it's going to line up maybe around when Anthropic does their IPO, that they're

**[21:44]** going to pull the rug, and then all of a sudden, it's going to be more usage-based pricing.

**[21:48]** We're going to get rid of these Cloudmax accounts and things like that.

**[21:51]** But I think it's just important for teams to be thoughtful and mindful of how they're

**[21:58]** organizing which model to the agents that they're out there performing.

**[22:03]** Yeah, for sure.

**[22:06]** And I think it's good to at least think about that quite early.

**[22:14]** I think it's perfectly fine to-- and I'm going to contradict myself here, but I think it's

**[22:19]** perfectly fine to just roll with whatever is the most effective for now.

**[22:27]** Because velocity and speed to value is really the coin.

**[22:34]** That's what we all want.

**[22:37]** We want to be faster than our competitors.

**[22:41]** And so I think speed is what matters most.

**[22:47]** But I think it's useful, and it's very helpful to think about when you build a large architecture,

**[22:57]** to think about all those topics right on the stars.

**[23:00]** Because when you build a large architecture, it doesn't cost you too much to actually think

**[23:04]** it through and think about the future.

**[23:07]** Because that operating system, for instance, took us like a month and a half to have a

**[23:14]** solid MVP.

**[23:16]** So by that time, if you figure out that you messed up the architecture, it's unfortunate.

**[23:24]** Totally agree.

## 23:25 — What's next: an autonomous marketing organization

**[23:25]** What's next for MarketingOS?

**[23:27]** What are things that you want to add to the system?

**[23:30]** Things that maybe you're hoping you can accomplish in the near future?

**[23:35]** What's on the roadmap?

**[23:38]** Just more agents, more expert agents.

**[23:43]** Think about it.

**[23:44]** Now we are in a place where-- I want marketing to be autonomous.

**[23:53]** I want to have an autonomous marketing organization.

**[23:58]** What does that mean?

**[23:59]** It means that we have no ceiling on how much we can deliver.

**[24:08]** So I want to be able to move the performance from how much we can deliver to how many outcomes,

**[24:18]** how many intents we can ask the system.

**[24:23]** So my goal today is just to create more agents that are super great at finding activities,

**[24:30]** finding recommendations that will move toward that goal of expanding pipeline.

**[24:35]** Because each agent is going to work toward that goal.

**[24:38]** How we get there, how we actually get the work done, that's something that we can figure

**[24:43]** out pretty easily.

**[24:45]** Because agents can do part of the work.

**[24:47]** Team members can do part of the work.

**[24:49]** We can also use-- we can also hire contractors to do some very specific tasks, because everything

**[24:56]** is so well-scoped.

**[24:58]** So it's really like a shift, not only in operation.

**[25:02]** It's also a shift of mindsets, where we shift from thinking, OK, I need loads of work force

**[25:08]** to work on a bunch of activities to deliver to a mindset where you need people to just

**[25:18]** find the right intents, find the right outcome to ask, and build the right agents that we

**[25:25]** deliver outcomes.

**[25:26]** There is no ceiling, again, no ceiling in terms of activities we can deliver.

**[25:35]** The only ceiling is how much can we ask for?

**[25:39]** Well, once you've made that investment infrastructure, I'm just excited to see what the velocity

**[25:45]** is that your team comes up with, and what the new agents you build are.

## 25:50 — From engineering to founding Clevy to Fountain

**[25:50]** I would love to dive into a little bit-- you have an engineering background, founding company

**[25:58]** background.

**[25:59]** Now you're running marketing, and RevOps, and all these other things.

**[26:03]** But what was the through line, and how did you get to what you're doing today?

**[26:09]** My life has been quite right.

**[26:15]** So yeah, I started in engineering, and I love engineering, for what it's worth.

**[26:20]** I absolutely love it.

**[26:23]** I co-founded my company that we sold three years ago to found them.

**[26:28]** I co-founded it with two other great co-founders.

**[26:31]** One was a CEO of that company, clearly, and one was the CTO.

**[26:37]** And so I kind of organically moved to marketing, because the three founders are as much engineers

**[26:44]** as they are into business.

**[26:46]** You just need to just move as fast as possible.

**[26:49]** And at some points, moving as fast as possible meant having me working on marketing, and

**[26:59]** applying the system methods that I have thanks to engineering, applying that into marketing.

**[27:10]** When we got acquired by found them, I actually went back to engineering.

**[27:17]** And I stayed there for like, I stayed in engineering for about three to four months, I guess, before

**[27:24]** being pulled back into into marketing, because again, we needed to build systems.

**[27:30]** And so I've been working in marketing at found them for for three years now.

**[27:36]** And yeah, to me, marketing is really about finding the systems that will lead you to

**[27:45]** deliver what the customer wants.

**[27:47]** I do think systems thinking can be really helpful in a number of business functions.

**[27:54]** What in particular attracts you to marketing over all the other ones?

**[28:00]** In marketing, there is something really amazing.

**[28:03]** It's all about the data.

**[28:06]** There is I mean, sure, there is some creativity, and I'm very, very thankful to be to have

**[28:11]** a team that is exceptional and that in creatives and in in copywriting and the totally balance

**[28:19]** my my lack of creativity and my obsession for systems.

**[28:24]** I would say that's because everything is about data, everything is about data, or at least

**[28:29]** improvements about data.

**[28:32]** It just makes sense for for marketing to be led by systems, because the more efficient

**[28:39]** you are in finding sense into the data, the more efficient you are into implementing the

**[28:44]** changes, the recommendation and therefore the changes that come from those learnings.

**[28:51]** And the faster you will go into into growing your pipeline, the faster you'll go into understanding

**[28:59]** what your, what your prospects.

**[29:03]** The faster you're going to launch a new initiative, that's really where you get, you get leads,

**[29:10]** you get them, you get leads that actually convert that are qualified and that will,

**[29:16]** that will convert into into pipeline.

**[29:19]** So system to me, marketing is just a way of it's just a fast track to fast track to success.

**[29:26]** Now that being said, doesn't disqualify quality, so you still need to have high quality content

**[29:31]** system to have high quality creative high quality high quality copywriting.

**[29:37]** And to be honest with you, I love to be able to find to find individuals in my organizations

**[29:43]** that are exceptional at it.

**[29:46]** And that will just make those systems super successful, being able to bring high quality

**[29:53]** content high quality visuals.

## 29:56 — Brand versus demand

**[29:56]** I would love your take on, on one debate I always end up having with marketers or even

**[30:03]** thinking about how we run LeanScale to brand versus demand.

**[30:09]** There's some things that you can do that can be difficult to have attribution, but you

**[30:13]** just kind of know that you should be doing them.

**[30:17]** And I think it lives in that having high quality content, copywriting, everything, look and

**[30:22]** feel the aesthetic of what you're, you're building.

**[30:24]** I think that there's importance there.

**[30:26]** How do you think of maybe from a investment gauge, like how much should you be investing

**[30:33]** in brand versus demand?

**[30:36]** And then are there any things about a business that might change what that equation would

**[30:41]** look like?

**[30:42]** Yeah, that's a, that's a great question.

**[30:47]** And, and I would love to be able to, to give like the perfect answer that everyone would

**[30:53]** be, we'd be able to relate to 30% of marketing budget here, 70% there.

**[30:59]** And don't, and don't move, let's do that now.

**[31:01]** Now it's, I think it's way more complicated because it's highly depends on your, on your

**[31:08]** overall strategy as a company.

**[31:10]** It highly depends on your, on your audience as well, obviously for us at Fontaine being

**[31:18]** selling to an audience that are mostly C-level or C-level minus one or minus two.

**[31:25]** It's key for us to be top of mind for them.

**[31:28]** It's key for us to, to be out there and for, for, for prospects to at least know what Fontaine

**[31:35]** does.

**[31:36]** So yes, we are, we are investing a fair amount of budgets, a fair part of our budget into,

**[31:43]** into awareness, into bronze, but it's not only because it's a, not only because we think

**[31:49]** it's good, it's only, it's also because we see that it works really well.

**[31:54]** And what does that mean?

**[31:55]** It means that we do have a large part of our pipeline that comes from inbound and it just

**[32:02]** happened that the pipeline grows when we, when we invest more on, on, on awareness.

**[32:08]** So the correlation really, you know, really works out for us.

**[32:12]** So there is, the reason why we're investing is also because of empiric learnings.

**[32:18]** We learn that the more we, the more we invest, the more pipeline we, we get, but I would

**[32:26]** say that it depends.

**[32:27]** But for my previous company, for instance, awareness in bronze was not like a big game

**[32:32]** changer for us.

**[32:35]** The little growth activities, like the, because we were very, we were very early stage, right?

**[32:41]** So like the smaller activities, like the growth hacking, where the activities that brought

**[32:46]** 80 to 90% of our pipeline at Fontaine, it's, there is a much larger part that is due to

**[32:55]** awareness for sure.

**[32:58]** Yeah.

## 32:59 — AEO: there is no recipe and nobody has one

**[32:59]** One of the biggest questions a lot of the companies we're working with a lot, I have

**[33:05]** personally is how to optimize for AEO.

**[33:10]** I'm finding myself pretty much exclusively going to the LMS to search for products just

**[33:16]** personally or even in a business context.

**[33:20]** And I'm actively leveraging that way of finding products I want to buy.

**[33:27]** What from a marketing perspective, what's the best way to optimize?

**[33:32]** I know it's very early stages, but anything that you have that helps people get an edge

**[33:36]** on AEO would be super helpful.

**[33:39]** Yeah, absolutely.

**[33:41]** So the one learning that I got from, I don't know, like a year, maybe like two years or

**[33:48]** two years, just meeting companies that are experts in AEO.

**[33:53]** The one learning that I got is that there is no recipe for you.

**[33:58]** Nobody knows exactly how it works.

**[34:00]** No magic words.

**[34:02]** The only thing that we can do is just watch the trends and just react as fast as possible

**[34:11]** to those trends.

**[34:14]** And that can be by publishing an article, that can be by editing the website, can be

**[34:20]** by just doing a press release for whatever product you have or use you have.

**[34:27]** But down the line, the only thing that works is just being extremely attentive to what

**[34:37]** the market sense and to signals.

**[34:41]** So looking back to, settling back to Fontaine Watch, which again is that layer at Fontaine

**[34:49]** that just collects signals from everywhere, from the markets.

**[34:55]** What we'll do is that we will, so we will collect those signals and let's say, I'm going

**[35:00]** to give you an example.

**[35:01]** Let's say there is a new regulation that come out that is going to affect, to have an impact

**[35:10]** on a segment of our customers.

**[35:13]** What's going to happen is we have marketing rights that is going to pick up that signal

**[35:20]** and it's going to tell us that it has two actions for us, two recommendations.

**[35:26]** Recommendation number one is to edit our, like, marketing websites page, like it's going

**[35:35]** to pick up the page that talks about regulations, it's going to say, hey, you need to address

**[35:39]** this regulation.

**[35:40]** It could be, you know, other collections that says, hey, you should really talk to us to

**[35:45]** learn more about how you can mitigate your risk given this new regulation.

**[35:52]** It can be just some, just contents addressing this regulation.

**[35:58]** And number two is going to be maybe a blog post.

**[36:02]** So Marketing OS is going to say, okay, this is a regulation.

**[36:05]** This is everything I know about this regulation.

**[36:08]** This is how it's going to impact your customers.

**[36:11]** There is no legal advice at all.

**[36:14]** It's just purely facts and I'm going to make that recommendation to write a blog post.

**[36:21]** So then it's passed on to the action agents, the action agents is going to get the information,

**[36:28]** write the blog posts.

**[36:30]** We have the human in the loop at some point.

**[36:32]** So we are going to say, yes, I approve that.

**[36:34]** It's approved and it goes to the website.

**[36:37]** So within a few hours from the regulation going live, we have a blog post that is published,

**[36:47]** the website that has occurred to action, that is directly to demo of the product that we

**[36:53]** have that help our customers.

**[36:57]** And that will rank in church PT, in Claude, in the Netherlands in general.

**[37:05]** If that one doesn't rank, it will be the next one.

**[37:07]** If it's not the next one, it will be again the next one, but AEO is a game of volume.

**[37:12]** AEO is a game of getting the right contents at the right time, type your brand.

**[37:22]** And if you do that relentlessly, you get a good ranking on it.

**[37:28]** You get a good ranking.

**[37:29]** You're going to get a good ranking if customer asks for the brand in your category, but you're

**[37:33]** also going to get a good ranking when anyone asks a question about that regulation because

**[37:38]** church is going to quote your article.

**[37:42]** And I don't know what the recipe is in terms of the phrasing and how we should put titles

**[37:49]** and subtitles and all that stuff.

**[37:51]** I have no idea.

**[37:52]** What I know is that the faster I am picking up the signals and publishing, the higher

**[37:57]** I am in church PT ranking.

**[38:00]** And that's just good enough for me.

**[38:02]** It's worked really well so far.

**[38:03]** So we are just keeping, we just keep doing the same.

## 38:08 — Why velocity beats the two-week sprint

**[38:08]** That is the first time I have heard the importance of velocity and it's making a ton of sense.

**[38:16]** Everyone's talking about volume.

**[38:18]** Everyone's talking about the hacks.

**[38:19]** Oh, right.

**[38:20]** This type of content, right?

**[38:21]** That type of content, right?

**[38:22]** This or that.

**[38:23]** But if you're the first person to write content and the second people are searching about

**[38:30]** it, then you probably already stacked up people looking at that article, looking at whatever

**[38:35]** you put out into the ether.

**[38:38]** And I think the importance of speed here has to be so much more valuable than I think a

**[38:44]** lot of people are thinking.

**[38:47]** It's key, but it's not only key for AEO.

**[38:52]** It's also key for SEO, even, I mean, SEO is declining, but it's still out there, right?

**[38:58]** So, yeah, I work prospects and customer studios being studios, Google.

**[39:07]** So it's still out there.

**[39:08]** So it's still useful for SEO.

**[39:11]** It's also useful for campaigns, being able to come up with the right awareness campaign

**[39:18]** or the right ABM campaign, targeting very specific segments before all of our competitors

**[39:29]** plays into the success.

**[39:31]** So velocity, speed to me, is at the core of marketing with DNA.

**[39:41]** Marketing OS is not only about volume, sure, volume is there, but it's also about velocity.

**[39:47]** Like I don't want to have to wait two weeks until the next sprints or a week before, until

**[39:51]** the next sprints, in order to consider making an article that will talk about something

**[40:01]** that was, or the signal that was found like a week ago, it's, I think it's a cost of opportunity.

**[40:11]** The one huge advantage of agents is that they can just run super fast and they can be proactive.

**[40:20]** So whenever they see something, whenever they see an opportunity, they will come knocking

**[40:24]** on our door, on my door, on my marketing manager, or like a member of my team and say, you really,

**[40:33]** really, really need to pay attention to this because this will grow your pipeline.

**[40:41]** So yeah, velocity is key and it's not only, you know, it's not only a DNA that we have

**[40:45]** in marketing, it's actually something that we, we leave by the fountain.

**[40:52]** Velocity is everything, and I think all of the hard work in infrastructure that you put

**[40:59]** in has really unlocked and enabled that.

**[41:02]** I think some people are trying to be really fast, but they don't have the foundations

**[41:06]** in place, and this is another excellent example of being able to leverage that infrastructure

**[41:13]** to have high velocity for creating content that helps with the AEO, along with all the

**[41:17]** other things you're doing in paid ads and everything else.

**[41:20]** So I think if teams are wanting to do this, you got to start with that infrastructure

**[41:25]** layer.

**[41:26]** That's a hard part.

**[41:27]** And then you can get really creative on how you execute against it.

## 41:32 — The agent builder inside Marketing OS

**[41:32]** Totally. And, and to be honest, velocity is not only something that we use for doing

**[41:41]** marketing activities, creating campaigns and iterating over campaigns and other contents.

**[41:46]** It's also something that we, we also leverage in, in the architecture of MarketingOS.

**[41:54]** In MarketingOS, for instance, if you have an idea of an agent, you don't need to go

**[42:00]** include and try to figure out how to build it and then somehow add it to MarketingOS.

**[42:05]** You can just open MarketingOS and say, "Hey, I want to build an agent that will do whatever

**[42:14]** is your idea.

**[42:15]** So I want an agent that will go and find such signal and based on that signal, we'll launch

**[42:24]** campaigns for such ACP."

**[42:26]** And MarketingOS is actually built to build agents.

**[42:29]** We have an agent builder inside MarketingOS, which is exceptional because all of our agents

**[42:33]** have the same taxonomy, have the same, have the same, like, prompts, I mean the prompt

**[42:42]** architecture, the same one across every single agents.

**[42:46]** It's the same, the skills are shared between, the skills are shared between all agents.

**[42:53]** So it's really amazing that you can just create any, any agent that you want without having

**[42:57]** to worry about anything really.

**[42:59]** You can just describe your, you can just describe your use case and just, and marketing ways

**[43:03]** we build the agents and we're decorating production.

**[43:05]** That's huge.

**[43:06]** So you're not needing to gather, like you said, all the skills, all the data connectors,

**[43:11]** all the context, "Oh, I'm packaging up this new thing," and then the handoff takes time

**[43:16]** and then like, "Oh, okay.

**[43:17]** Well now we're going to like reshape it for this environment and make it production grade."

**[43:21]** Being able to build it directly from MarketingOS is very impressive.

**[43:25]** Yeah, it's huge difference for us.

## 43:28 — Wrap

**[43:28]** Bastian, I really appreciate everything you shared on this podcast.

**[43:32]** It is awesome what you're building with MarketingOS and I think the infrastructure plus the execution

**[43:38]** layer, all of the hard work that you put into it, and then getting real use cases.

**[43:42]** I mean the LinkedIn ad comparisons, the awareness that you're pulling from the market and immediately

**[43:48]** building AEO content is just, it's very, very impressive.

**[43:53]** And I love the approach of moving from engineering, but finding love in the marketing world and

**[44:00]** really applying that systems thinking to something that really has a lot of data to work with

**[44:06]** and has a lot of systems that you can build and I'm going to assume there will be a lot

**[44:13]** more engineers falling into the marketing field in the future as well.

**[44:16]** So I just want to thank you again for everything you shared.

**[44:20]** I've always loved working with you and I can't wait because I know this is only the beginning

**[44:26]** of so much stuff you're going to build to see what you do next and follow all of Fountain's

**[44:30]** success.

**[44:31]** Of course, thank you so much Anthony.

**[44:36]** It's been great.

**[44:37]** I love this.

**[44:38]** So thanks for having me and yeah, you're on.