Latest / The Neon Show / “Yes.” Building Claude Code for Marketing | Kashish, Hightouch

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“Yes.” Building Claude Code for Marketing | Kashish, Hightouch · The Neon Show

• 0:00
  SPEAKER_01: Most marketers today are afraid that AI will take their job.
• 0:03
  SPEAKER_00: I think it's wrong.
• 0:04
  SPEAKER_00: The dream state is AI can do so much work that the marketer becomes the decision maker and the creative body.
• 0:10
  SPEAKER_00: We made all these controversial decisions that were wrong for most startups, but correct for iTouch.
• 0:15
  SPEAKER_00: Everyone else says go after SMB and earn the right to go after Enterprise.
• 0:18
  SPEAKER_00: We went after Enterprise on day one.
• 0:20
  SPEAKER_00: A lot of the founders that don't scale, they're always firefighting the needs of today versus building the company of tomorrow.
• 0:26
  SPEAKER_00: I would disagree.
• 0:27
  SPEAKER_00: I don't think Chat GPT and Gemini can stay on brand, especially in enterprise.
• 0:31
  SPEAKER_00: I can look at all of your ads and tell you if this ad will perform or not.
• 0:35
  SPEAKER_01: If you're right about your lead on the market, what does the marketing look like in five years and what is the role of human?
• 0:41
  SPEAKER_00: Um it really depends.
• 0:42
  SPEAKER_01: I think like Kashish, welcome to the Neon Show.
• 0:48
  SPEAKER_01: Excited to be doing this today in your office in SF.
• 0:51
  SPEAKER_00: Yeah, thank you for having me, and thank you for coming to our office.
• 0:53
  SPEAKER_01: Before we dive into high touch, right?
• 0:55
  SPEAKER_01: Would love to learn your personal journey.
• 0:57
  SPEAKER_01: You came at the age of five from Haryana to US.
• 1:00
  SPEAKER_00: Yeah, so I moved to the US when I was about five years old.
• 1:03
  SPEAKER_00: Yeah.
• 1:04
  SPEAKER_00: Um, and the reason we moved to the US, what my dad tells me, is for free public education.
• 1:09
  SPEAKER_00: Um, we lived in Haryana, so we didn't live anywhere near a big city.
• 1:13
  SPEAKER_00: And my parents felt that in India I would not be able to get the kind of education I would get in the US, which I think was correct.
• 1:20
  SPEAKER_00: I think the way I understand it is if you have connections in India, you can get a good education.
• 1:24
  SPEAKER_00: And without connections, it's really hard.
• 1:26
  SPEAKER_00: Um, whereas in the US, it's free and it's meritocratic.
• 1:30
  SPEAKER_00: So I was really lucky to kind of move to the US, move around every few months until we settled in Atlanta.
• 1:36
  SPEAKER_00: And Atlanta is where I really grew up, um, elementary, middle, and high school.
• 1:40
  SPEAKER_00: Um I would say that I got the best public education that exists on this planet in high school.
• 1:46
  SPEAKER_00: Um, went to college in Philadelphia.
• 1:49
  SPEAKER_00: I went to Penn and studied robotics.
• 1:51
  SPEAKER_00: And then I moved to San Francisco right after.
• 1:53
  SPEAKER_00: I was supposed to be a VC at Best Mer Venture Partners in New York, but I really wanted to live in San Francisco.
• 1:59
  SPEAKER_00: I really believed that by living in San Francisco, good things would happen and that were unpredictable.
• 2:04
  SPEAKER_00: I just felt like the people here are builders and constantly up to something new.
• 2:08
  SPEAKER_00: And so I kind of took a leap of faith of moving to San Francisco without a job.
• 2:13
  SPEAKER_00: Um, I was interviewing for machine learning jobs and eventually started what is now high touch.
• 2:20
  SPEAKER_00: At the time, it was an AI travel agency.
• 2:22
  SPEAKER_00: And so October 2018 is when I started the AI travel agency, which was called Carrie Travel.
• 2:27
  SPEAKER_00: And then slowly convinced Tadis and Josh, who were my housemates, to um join the company.
• 2:32
  SPEAKER_00: And then we pivoted the company into high touch after COVID.
• 2:36
  SPEAKER_01: So uh your journey in high touch just started pre-COVID and you were a first-time founder, right?
• 2:42
  SPEAKER_01: You got into uh uh YC.
• 2:45
  SPEAKER_01: Uh right.
• 2:46
  SPEAKER_01: But the initial few, the first few years, how much time did you run the travel agency?
• 2:52
  SPEAKER_01: Like one year?
• 2:53
  SPEAKER_00: Yeah, about a year, year and a half.
• 2:56
  SPEAKER_00: Um and we got it to like almost 100K of travel booked per week.
• 3:00
  SPEAKER_00: So we had some decent volume, but in travel you can't make any margin.
• 3:03
  SPEAKER_00: So even with decent volume, it's like one to two percent margin and it doesn't really mean much.
• 3:08
  SPEAKER_00: Um, and then when COVID happened, we had to make a decision: should we wait for travel to resume and kind of build in the meantime, or should we pivot the business to something else?
• 3:19
  SPEAKER_00: And it was kind of a no-brainer to pivot to something else because one, we thought COVID wouldn't last much longer than other people thought.
• 3:25
  SPEAKER_00: We thought it'd be at least like one to two years.
• 3:28
  SPEAKER_00: Uh, and then two, the margins were so bad anyways that we thought let's just work on a business with much higher margin.
• 3:33
  SPEAKER_01: So, was HighTouch an internal product that you use at Carrie?
• 3:37
  SPEAKER_00: No, unrelated.
• 3:39
  SPEAKER_00: All of our pivots in the early days were kind of just like unrelated.
• 3:42
  SPEAKER_00: Start the code base from scratch.
• 3:44
  SPEAKER_00: Um, they did come from some unique insight that some customer had given us, but the code and the product was unrelated.
• 3:49
  SPEAKER_01: So, so what was the unique insight on the travel product and what was the unique insight when you pivoted?
• 3:54
  SPEAKER_00: Yeah, so I would say um in travel, the unique insight was just that like people will pay money and use your service for like basically delegating their agency.
• 4:04
  SPEAKER_00: It's very similar to LLM.
• 4:05
  SPEAKER_00: Like, if the LLM can book better travel than you, then you will just have someone else do it.
• 4:09
  SPEAKER_00: Um the original high-touch product was actually not an AI marketing product.
• 4:12
  SPEAKER_00: The original high-tech product was an AI customer success product.
• 4:16
  SPEAKER_00: And so we basically took this like a travel agent is nothing more than like a really, really good concierge or like customer support, customer success.
• 4:25
  SPEAKER_00: So we took that but applied it to BDB SaaS.
• 4:27
  SPEAKER_00: And we said, these B2B SaaS companies, they should be doing their entire customer success motion over Slack.
• 4:32
  SPEAKER_00: That was the original high-tech product.
• 4:35
  SPEAKER_00: Um, we realized that customer communications is not that valuable, but customer data is extremely valuable.
• 4:41
  SPEAKER_00: So that took us to customer data.
• 4:42
  SPEAKER_00: And then customer data took us to reverse ETL.
• 4:45
  SPEAKER_00: Um, we found that getting CSMs and AEs to do their work in some new platform is unfeasible.
• 4:52
  SPEAKER_00: But putting better data in Salesforce and better data in gain site is actually extremely valuable because the salespeople and the CSMs already live in Salesforce and GainSite respectively.
• 5:00
  SPEAKER_00: And better, richer data in those tools would just transform what they what value they can provide their customers.
• 5:06
  SPEAKER_01: So that was reverse ETL.
• 5:08
  SPEAKER_01: And how did reverse ETL come to the current product CDP?
• 5:12
  SPEAKER_00: So maybe for the viewers, like we can give some background.
• 5:15
  SPEAKER_00: 2020 is when we started working on reverse ETL.
• 5:18
  SPEAKER_00: Reverse ETL is basically you give me a SQL query, I'll pipe that SQL query to your SaaS tool of choice.
• 5:23
  SPEAKER_00: So you can maintain one central database.
• 5:25
  SPEAKER_00: Let's say this one central database has all your user metrics, revenue metrics, and so on.
• 5:29
  SPEAKER_00: You just maintain that.
• 5:30
  SPEAKER_00: I'll pipe that to your sales tools, finance tools, customer support tools, and customer success tools.
• 5:36
  SPEAKER_00: So now you can maintain the same definition of revenue, LTV, whatever you want in all your different tools just by updating your database.
• 5:42
  SPEAKER_00: That was the original value pop of reverse ETL.
• 5:45
  SPEAKER_00: B2C companies loved it, B2B companies loved it, really everyone just needed this abstraction because they didn't want to build these reverse ETL pipelines internally.
• 5:52
  SPEAKER_00: So we did that for the first two, three years.
• 5:55
  SPEAKER_00: After doing that, we found that marketers were the ones willing to pay the most for this use case.
• 5:59
  SPEAKER_00: Because marketers have this cardinality.
• 6:02
  SPEAKER_00: They have like 50 ad tools, a few different customer engagement tools, and so on.
• 6:07
  SPEAKER_00: And the value of customer data for them could be millions of dollars.
• 6:10
  SPEAKER_00: If you sync the data correctly to Facebook, Google, Snapchat, TikTok, you can now stop marketing customers that already bought.
• 6:17
  SPEAKER_00: One of our banking customers immediately saved$6 million by unmarketing customers that already bought, right?
• 6:24
  SPEAKER_00: So they're happy to pay upwards of like$600K to a million dollars.
• 6:29
  SPEAKER_00: Um, because marketing became the number one use case, we started building higher-level UIs and interfaces for marketers.
• 6:35
  SPEAKER_00: That became the CDP.
• 6:37
  SPEAKER_00: And so what we discovered is that there's this market called customer data platform.
• 6:40
  SPEAKER_00: Customer data platform is inherently flawed because all these CDPs said, I want to store your data and I want your marketers to live in my platform and do all the things here.
• 6:50
  SPEAKER_00: In Enterprise, this was broken.
• 6:51
  SPEAKER_00: So we talked to all the large enterprises using Snowflake, Databricks, and BigQuery, and they said, I have a CDP, it's my data warehouse.
• 6:58
  SPEAKER_00: Can you just give me a UI on top that makes it accessible for marketers?
• 7:01
  SPEAKER_00: And then they can sync the data to the marketing tools.
• 7:04
  SPEAKER_00: So the syncing platform we had already built, that was Reverse ETL.
• 7:07
  SPEAKER_00: We just needed to build these um self-serve interfaces for the marketers to access the data.
• 7:11
  SPEAKER_00: And that's what became composable CDP.
• 7:13
  SPEAKER_00: And composable CDP is what took us to like over 100 million in revenue and like really, really high enterprise traction.
• 7:20
  SPEAKER_00: And that journey was mostly 2023 to 2026, where really composable CDP blew up.
• 7:26
  SPEAKER_01: And then how did you launch the other new product?
• 7:29
  SPEAKER_00: Um, after we had captured the marketing team, so now we're doing all insights and data syncing for marketing.
• 7:36
  SPEAKER_00: Now the marketer says the next biggest bottleneck is content and orchestration.
• 7:41
  SPEAKER_00: If you can create the email campaign for me or the ad campaign for me, which is the content assembly task, and you can orchestrate that campaign for me in my campaign tools and ad tools, then you're really doing like the end-to-end marketing workflow.
• 7:52
  SPEAKER_00: Um, and all of it is powered by agents.
• 7:55
  SPEAKER_00: So they didn't know that, right?
• 7:56
  SPEAKER_00: They just said, build the workflow for me.
• 7:58
  SPEAKER_00: At the same time, ChatGPT 4 came out and we realized that actually like agents can do all this work.
• 8:03
  SPEAKER_00: And so those two things happen at the same time.
• 8:05
  SPEAKER_00: And so we started building so many more products at the same time to um give them the like lifecycle marketing um studio and then the advertising studio that really does like 80% of the work they would need to do in order to launch their email and ad campaigns.
• 8:21
  SPEAKER_01: And CDP, like customer data platform, have existed for a long period of time.
• 8:25
  SPEAKER_01: Uh, what was your unique insight that you could replace the existing CDPs or say that the existing CDPs are redundant in an AI era?
• 8:37
  SPEAKER_00: Yeah, so this is um still the core insight, and it's still really working, which is that it's impossible for an enterprise brand to stuff their data into some schema that is rigid.
• 8:49
  SPEAKER_00: They'll have their own data warehouse.
• 8:51
  SPEAKER_00: In their data warehouse, they'll have a customer with multiple bank accounts.
• 8:54
  SPEAKER_00: They'll have a customer with multiple pets, they'll have a customer with children, and they're buying clothes for the children.
• 8:58
  SPEAKER_00: These kinds of things cannot be represented in someone else's TAS tool.
• 9:01
  SPEAKER_00: They can only be represented in your enterprise database.
• 9:04
  SPEAKER_00: And so that was the core insight.
• 9:05
  SPEAKER_00: It was that the semantic understanding of data is heterogeneous between different brands.
• 9:10
  SPEAKER_00: Um, and if you can represent that in your SaaS, then um all the marketers and product people, whoever it is, can come in and actually access that data in a very um customized way for their business.
• 9:22
  SPEAKER_00: Um, this is our core offering.
• 9:24
  SPEAKER_00: It's the semantic layer on your data warehouse and this UI, which is audiences, journeys, identity resolution, and so on on top of your database.
• 9:31
  SPEAKER_00: And so the two advantages, there's three advantages.
• 9:34
  SPEAKER_00: One, on day one, we have all the data.
• 9:36
  SPEAKER_00: There's no six to twelve month process of giving us the data.
• 9:39
  SPEAKER_00: Uh, two, the data is infinitely flexible.
• 9:41
  SPEAKER_00: So if you update your definition of like account balance, now you have a set checkings account balance and savings account balance.
• 9:47
  SPEAKER_00: You can make that update in your database, we'll just inherit that immediately.
• 9:50
  SPEAKER_00: And now your marketers will inherit that immediately, and they can run their marketing programs based on inheriting that.
• 9:56
  SPEAKER_00: So these insights is what resulted in a much more um purpose-built product for enterprise and a like very high-value product for enterprise.
• 10:07
  SPEAKER_00: So there's many enterprises that actually never bought CDP.
• 10:10
  SPEAKER_00: Um, people think CDP was enterprise because all the CDP vendors convinced them of this.
• 10:14
  SPEAKER_00: But actually, CDP was stuck in mid-market.
• 10:17
  SPEAKER_00: CDP never broke into enterprise because it was not composable.
• 10:20
  SPEAKER_00: We built the first composable CDP and then we started just dominating enterprise.
• 10:23
  SPEAKER_00: And that was the core insight.
• 10:25
  SPEAKER_00: And then lastly, um we can now do this for all like Martic and Adtech.
• 10:32
  SPEAKER_00: So we're not like inherently a Martic platform.
• 10:34
  SPEAKER_00: Um we do this for Martec, we do this for ads, and we do this for product.
• 10:37
  SPEAKER_00: So any in-app personalization you want to build will give you this as well as email, as well as advertising.
• 10:44
  SPEAKER_01: Understood.
• 10:46
  SPEAKER_01: And did segment never have that?
• 10:49
  SPEAKER_00: No.
• 10:50
  SPEAKER_00: So um most of the CDPs they had this architecture, which was an event schema.
• 10:57
  SPEAKER_00: Event schema is what?
• 10:58
  SPEAKER_00: It means that I have customers, and the customers have um it things they can do.
• 11:02
  SPEAKER_00: They can do button clicked, cart checkout, um, checkout clicked, and like refund started.
• 11:07
  SPEAKER_00: So each person can do events.
• 11:09
  SPEAKER_00: It's basically impossible to roll up on an event schema, the canonical.
• 11:14
  SPEAKER_00: Actually, how much money does this have in their bank account?
• 11:16
  SPEAKER_00: Actually, do they still use the product or not?
• 11:18
  SPEAKER_00: And so on.
• 11:19
  SPEAKER_00: So the best way to represent a customer is actually um a relational database, not an event schema.
• 11:24
  SPEAKER_00: So almost all these CDPs that were built on event schema were inherently flawed.
• 11:28
  SPEAKER_00: So I mean that's the simplest way I can describe it.
• 11:30
  SPEAKER_00: For more technical customers, they understand this.
• 11:32
  SPEAKER_00: And as soon as you say this, it's night and day that some CDPs operate on event schema, others on the relational database that are set up by the enterprise brand and have truly like all the data.
• 11:44
  SPEAKER_00: And then you get this big governance benefit too, right?
• 11:46
  SPEAKER_00: So let's say I have some large brand like Domino's.
• 11:49
  SPEAKER_00: Um, Domino's Pizza has to now run North America, Europe, and Asia.
• 11:54
  SPEAKER_00: Each one will have a separate database and separate governance, and we'll just inherit all that governance for their marketing teams across the world.
• 12:00
  SPEAKER_01: Got it.
• 12:01
  SPEAKER_01: So you become the most easy platform to integrate?
• 12:05
  SPEAKER_00: Most easy and most flexible.
• 12:07
  SPEAKER_00: So it's like somehow giving them much more power with much lower integration cost.
• 12:11
  SPEAKER_01: Okay.
• 12:12
  SPEAKER_01: And they don't have to to duplicate the the database.
• 12:15
  SPEAKER_01: Correct.
• 12:16
  SPEAKER_00: Exactly.
• 12:17
  SPEAKER_01: And what's the biggest ROI the customers have seen with high tech?
• 12:21
  SPEAKER_00: Yeah, so basically how a CMO thinks of it, and this is all pre-AI.
• 12:25
  SPEAKER_00: So I'm mostly talking about CDP pre-AI.
• 12:27
  SPEAKER_00: Um, the CMO says, I have all this customer data.
• 12:31
  SPEAKER_00: If my experience, my if my customer experience was informed by the customer data, then the customer experience would convert much better.
• 12:38
  SPEAKER_00: Yeah.
• 12:38
  SPEAKER_00: Because your experience in my app and in my website and in my email program would be completely tuned to you.
• 12:45
  SPEAKER_00: And you would feel so much like I know you and I know what you want to buy and what you're thinking about today, that you would always convert with me versus convert with my competitor.
• 12:53
  SPEAKER_00: So they can like clearly track how better customer data increases the conversion of their campaigns and makes their customer more sticky, higher LTV and so on.
• 13:02
  SPEAKER_00: And so, like large gaming company, large media company, large retail company, all three have like case studies where by bringing in what I would call self-serve data for their marketing team, which is what Hatchet provides, they were able to completely transform like their metrics into higher conversion, lower CAC, higher LTB.
• 13:22
  SPEAKER_01: Got it.
• 13:24
  SPEAKER_00: And this is pre-AI.
• 13:25
  SPEAKER_00: So this is simply I'm a marketing team, my data team has all this data.
• 13:29
  SPEAKER_00: I now have access to the data and I can use it now.
• 13:31
  SPEAKER_00: Post-AI, so you called this out, which was agentic CDP.
• 13:35
  SPEAKER_00: We started by building a composable CDP, which we still is the foundation of everything.
• 13:39
  SPEAKER_00: We then added the ability for agents to use our semantic layer to find opportunities within the customer base.
• 13:46
  SPEAKER_00: So the agent will say, You have this customer base that plays tennis.
• 13:49
  SPEAKER_00: You've never once marketed them a tennis racket or tennis shoe.
• 13:52
  SPEAKER_00: If you just simply did that, instead of sending them the same weekly email every week, you would make extra 30 million.
• 13:57
  SPEAKER_00: What do you think?
• 13:58
  SPEAKER_00: And the marketer says, that's a great idea.
• 14:01
  SPEAKER_00: Um that insights product that's autonomous and able to write abstract queries on the data warehouse.
• 14:06
  SPEAKER_00: Um, that was not possible pre-AI.
• 14:08
  SPEAKER_00: Post AI, it's one of our most used features and allows the marketing team to discover all these new insights about their customer base that they never had before.
• 14:15
  SPEAKER_01: So now you are giving insights autonomously without even the marketer pinging, hey, can you give me new insights?
• 14:21
  SPEAKER_01: Correct.
• 14:21
  SPEAKER_00: Exactly.
• 14:22
  SPEAKER_00: And it's very different than like the AI products you see on the market, right?
• 14:25
  SPEAKER_00: Usually it's some chat box on the market, ask it a question, it'll give you the objectively correct answer.
• 14:30
  SPEAKER_00: This is saying, we know this marketing team needs to reduce churn.
• 14:34
  SPEAKER_00: Let's build research agents that are constantly scanning the customer data, the email data, and the advertising to see where are all the opportunities for us to reduce churn.
• 14:43
  SPEAKER_00: Um, and then that leads you to the next platform, which is now that you've identified these opportunities, you know you want to run the tennis campaign.
• 14:50
  SPEAKER_00: You then need the content to run the campaign, which is then the other products that we're working on.
• 14:55
  SPEAKER_01: So so the so the market that you are in, like the CDP market, there was never a company that became 20 billion to 50 billion, right?
• 15:04
  SPEAKER_01: So so it was the product problem or the uh size of the market problem?
• 15:09
  SPEAKER_00: Uh product issue.
• 15:11
  SPEAKER_00: So um almost every enterprise I meet, what do they tell me?
• 15:14
  SPEAKER_00: They say that they never bought CDP because they thought CDP was flawed.
• 15:19
  SPEAKER_00: They instead invested in Terraform or Oracle DB, now Snowflake and Databricks, and that became their like CDP.
• 15:26
  SPEAKER_00: And then they invested in a team of engineers to make that connect to all their other IT systems.
• 15:31
  SPEAKER_00: So they could not even conceive like the idea of buying CDP because it was broken for enterprise.
• 15:36
  SPEAKER_00: This is like the largest part of the market.
• 15:38
  SPEAKER_00: That's the part of the market that we're going after.
• 15:40
  SPEAKER_00: And so that I think is why the CDPs that you're thinking about that are kind of stuck in the 100 to 300 million revenue range were stuck there.
• 15:49
  SPEAKER_00: And when they were at our scale, so let's say we're in the 100 to 200 million range, they were not growing 100%.
• 15:54
  SPEAKER_00: They were growing 20, 30%.
• 15:56
  SPEAKER_00: And it's because they were only selling to the mid-market, the D2C consumer brand that was trying to get their data ready for the first time.
• 16:02
  SPEAKER_00: Not the enterprise brand that has so much more data than they can actually utilize.
• 16:06
  SPEAKER_00: That's one.
• 16:07
  SPEAKER_00: Two is actually um CDP market is better than you think because Adobe and Salesforce actually have collectively almost two billion of revenue.
• 16:16
  SPEAKER_00: Just on CDP?
• 16:18
  SPEAKER_00: Just on CDP.
• 16:18
  SPEAKER_01: And this is marketing CDP.
• 16:20
  SPEAKER_00: Um, just on CDP.
• 16:22
  SPEAKER_00: Marketing cloud actually for them is even bigger.
• 16:23
  SPEAKER_00: Salesforce has like five billion on marketing cloud, Adobe has like one to two billion on marketing cloud.
• 16:27
  SPEAKER_00: Just for CDP, they have like two billion-ish total.
• 16:30
  SPEAKER_00: Um, and so that's the part of the market that people can't really see because it's hidden within these suites.
• 16:36
  SPEAKER_00: Um, and so these there's a lot more CDP market size out there than you would think.
• 16:42
  SPEAKER_00: Um sorry, and they so yeah, I I think like we we think for high touch the CDP market's gonna become a billion dollars.
• 16:51
  SPEAKER_00: And then we think the lifecycle marketing and advertising markets that we're working on will become even bigger.
• 16:57
  SPEAKER_01: Billion dollar revenue market.
• 17:00
  SPEAKER_01: Would you enter marketing cloud then?
• 17:02
  SPEAKER_01: That's a natural choice.
• 17:04
  SPEAKER_00: Um we're considering it, yeah.
• 17:05
  SPEAKER_00: So we are very much actively working on what I would call content creation for lifecycle marketing and orchestration.
• 17:14
  SPEAKER_00: Um, I don't want to convince an enterprise brand to switch from Salesforce Marketing Cloud to HighTouch for the marketing cloud itself.
• 17:21
  SPEAKER_00: I'd much rather say, use my agents to orchestrate your marketing cloud and to build the email templates that go into your marketing cloud.
• 17:27
  SPEAKER_00: And once I've done that, I've done the strategic pieces that really matter the most.
• 17:31
  SPEAKER_01: So let's take one of the largest customers that you would have, right?
• 17:36
  SPEAKER_01: So a customer, a large customer in an enterprise would pay you what, a million dollars today?
• 17:41
  SPEAKER_01: Or could it go into double digit million also?
• 17:44
  SPEAKER_00: Uh yeah, let's say we're between one and one and ten.
• 17:47
  SPEAKER_01: One and ten.
• 17:47
  SPEAKER_01: I'd say let's assume four or five million dollars on average.
• 17:51
  SPEAKER_01: Can you can you walk through the life cycle of that customer on on how high touch integrates?
• 17:56
  SPEAKER_00: Yeah.
• 17:58
  SPEAKER_00: Oh, let me think of some examples.
• 18:02
  SPEAKER_00: Yeah, so I think maybe a good example would be like the largest food delivery app.
• 18:07
  SPEAKER_00: Um, so say they start with um they have to do marketing to merchants, they have to do marketing towards consumers.
• 18:14
  SPEAKER_00: This is two different tables in the database.
• 18:16
  SPEAKER_00: Um in the in the past, this was actually two different CRMs and two different CDPs.
• 18:21
  SPEAKER_00: Um, because the CDPs are so rigid, they can only handle merchants or consumers.
• 18:26
  SPEAKER_00: So the data team comes to us and they say, hey, look, it's just data.
• 18:29
  SPEAKER_00: Can you help me build one abstraction on top of all of these data?
• 18:32
  SPEAKER_00: And then my marketers that are doing merchant marketing versus consumer marketing, they'll live in the same place.
• 18:36
  SPEAKER_00: And actually, they can do cross-marketing and whatever they need to do.
• 18:39
  SPEAKER_00: So that was the first step, um, building them just the CDP for the merchant marketers and the consumer marketers.
• 18:48
  SPEAKER_00: Um, the more data-driven that became, the more the conversion in each of these two verticals for them started improving.
• 18:53
  SPEAKER_00: Then they said, with AI, can I drive insights to figure out which merchants need more marketing or which consumers are about to drop off and what I can sell them, or which like dinner kind of like um marketing programs I can give them.
• 19:08
  SPEAKER_00: So we gave them this insights layer.
• 19:10
  SPEAKER_00: They told us that they tried twice to build this insights layer internally.
• 19:14
  SPEAKER_00: Every company right now is trying to do AI SQL and solve like the analyst job with AI.
• 19:19
  SPEAKER_00: Uh, it's not working.
• 19:21
  SPEAKER_00: The reason our AI agent is better at writing SQL queries is two things.
• 19:24
  SPEAKER_00: One, our semantic layer informs that agent on what the right queries are, what the right tables are, which is semi-obvious, but building a good semantic layer and setting it up in enterprise is quite difficult.
• 19:34
  SPEAKER_00: We have a very self-serve UI where you can see our graph and like set up that layer.
• 19:39
  SPEAKER_00: Second, is we taught agents how to stop hallucinating.
• 19:42
  SPEAKER_00: When you ask Cloud to run a SQL query for you, Cloud will oftentimes lie that it ran the query.
• 19:47
  SPEAKER_00: Or it'll run top 50 in the query and not return the rest of the table because it takes too long.
• 19:52
  SPEAKER_00: And then it'll just fabricate the results for the rest of the table.
• 19:55
  SPEAKER_00: So there's all these different things that happen in AI that we have kind of solved and built smaller agents that are constantly evaluating larger agents to make sure the SQL queries that are run are um actually answering the question that was asked and actually accurate.
• 20:09
  SPEAKER_00: So we are now able to answer all objective insights for them, and that's like the agentic CDP.
• 20:13
  SPEAKER_00: Then they say, hey, like, can you now agentically write all my emails and run all my ads?
• 20:21
  SPEAKER_00: Um and so for this, we've built what we call content assembly.
• 20:24
  SPEAKER_00: Content assembly means that I will take from your existing assets and existing images all the images of like people eating food, all the damn assets, and so on.
• 20:33
  SPEAKER_00: And I'll teach an agent how to create new collateral using these assets.
• 20:38
  SPEAKER_00: So rather than the rather than the agent hallucinating people that are not real and like food that's not real, it's all real.
• 20:43
  SPEAKER_00: But it's um cross-product of real assets into new assets.
• 20:47
  SPEAKER_00: Um that's like the content generation product that we built.
• 20:49
  SPEAKER_00: And this will create any email you want based on the templates of this delivery company and any ad you want based on previous ads that are on brand.
• 20:59
  SPEAKER_00: So we've solved the brand adherence problem in AI.
• 21:01
  SPEAKER_00: There's no other company right now in the world that can do brand adherence as well as high tech can.
• 21:05
  SPEAKER_00: And so when the marketer onboards, they find like within like a few days, we are now like on brand and able to create production assets that are then doing the marketing, um, doing all their ad programs for them.
• 21:17
  SPEAKER_00: So there's a digital finance company, they've stopped doing ads that are assembled by human creative designers.
• 21:26
  SPEAKER_00: All the ads are, all the brand design is made by humans, and then all of the assembly is done by AI and then launched into market by AI.
• 21:33
  SPEAKER_00: So those are kind of like the journey.
• 21:35
  SPEAKER_00: You kind of go from solving the data problem, then the marketing self-serve problem, then the content assembly and orchestration problem.
• 21:42
  SPEAKER_00: And then eventually you start just building these more autonomous agents that are coming to you with work.
• 21:48
  SPEAKER_00: And they're saying, hey, um, this subset of customers is really interested in Thai food.
• 21:54
  SPEAKER_00: You should just market them Thai food every Saturday or every Friday.
• 21:58
  SPEAKER_00: And you can't.
• 22:00
  SPEAKER_00: Because you have no images for Thai.
• 22:02
  SPEAKER_00: So tell someone to go do a photo shoot for Thai food, and then I'll build you all the campaigns you need for all the different regions in North America to run this campaign.
• 22:11
  SPEAKER_01: But you still need some other tool to execute that campaign.
• 22:15
  SPEAKER_00: Only in email and SMS.
• 22:18
  SPEAKER_00: In ads, we can just push it directly to Facebook, Google, and so on.
• 22:21
  SPEAKER_01: Okay.
• 22:21
  SPEAKER_00: And in email and SMS, all these companies have some tool.
• 22:24
  SPEAKER_01: So personalization has not been like clearly solved yet.
• 22:30
  SPEAKER_00: And it won't be.
• 22:31
  SPEAKER_01: Why?
• 22:32
  SPEAKER_00: Because AI is like gonna be able to do a much better job of personalization than was even possible or like even thinkable.
• 22:46
  SPEAKER_00: So like what everyone wants is one-to-one personalization.
• 22:49
  SPEAKER_00: For one-to-one to exist, you need somehow for like inference to allow me to check the data and the past transactions and all the possible products I could sell to any every individual consumer.
• 23:00
  SPEAKER_00: I think there are like architectures and paradigms that will enable this, but there's a lot of work to be done.
• 23:05
  SPEAKER_00: And then it requires the correct data flow in and the correct like human approval flow or agentic approval flow out to actually enable these campaigns.
• 23:12
  SPEAKER_00: I think we're like similar to coding.
• 23:14
  SPEAKER_00: Um you can have like Devin write 60 tickets per hour, um, or not tickets, sorry, um 60 PRs per hour to solve tickets.
• 23:22
  SPEAKER_00: Is it the right PR or not?
• 23:23
  SPEAKER_00: That's the real question.
• 23:24
  SPEAKER_00: So like I think that's gonna be the bottleneck for marketing very soon.
• 23:28
  SPEAKER_00: Um HighTech will solve the content assembly and like data task.
• 23:32
  SPEAKER_00: Once that happens, there will be like so many um PRs for marketers to look at and say yes or no.
• 23:39
  SPEAKER_00: And I think that's the dream state.
• 23:40
  SPEAKER_00: The dream state is um AI can do so much work that the marketer becomes the decision maker and the creative body rather than the like operational executor of these things.
• 23:51
  SPEAKER_01: Most marketers today are afraid that AI will take their jobs.
• 23:55
  SPEAKER_00: I think it's wrong.
• 23:56
  SPEAKER_00: I understand the concern.
• 23:57
  SPEAKER_00: I think it's like totally wrong.
• 24:00
  SPEAKER_01: Yeah.
• 24:01
  SPEAKER_01: So you're how much how many years are we away from that kind of that level of personalization?
• 24:09
  SPEAKER_00: Um, it really depends.
• 24:10
  SPEAKER_00: I think like some brands could achieve that next year.
• 24:13
  SPEAKER_00: Um, but again, it's like the the bar is gonna keep sliding.
• 24:16
  SPEAKER_00: So like we're gonna achieve like 2026 capability in 2027, but then by 2027, technology has moved so much that actually the bar is higher and higher.
• 24:25
  SPEAKER_00: So I think like personalization is a never-ending task.
• 24:28
  SPEAKER_00: But we should achieve like superhuman personalization this year.
• 24:35
  SPEAKER_00: And it really just depends if people like can transform their teams quickly enough to adapt to it.
• 24:40
  SPEAKER_00: This fear that you just mentioned that marketers are worried about their jobs being taken is one of the reasons why transformation is so hard.
• 24:46
  SPEAKER_00: Um, I truly don't think it's possible for AI to take their jobs because um what's instead going to happen is there's gonna be so many more campaigns we can run and want to run.
• 24:57
  SPEAKER_00: So, in fact, you'll need more marketers, more designers, and more just like people to run all these campaigns.
• 25:03
  SPEAKER_00: Um but the individual output per person will increase like crazy.
• 25:08
  SPEAKER_00: So if like one campaign, one marketer runs a campaign per quarter right now, maybe that marketer runs a campaign per week in the future.
• 25:15
  SPEAKER_00: Um but the appetite from the marketing team goes up like 100x, so even outpaces that increase in productivity.
• 25:22
  SPEAKER_01: So do you sell to the CMO or marketing ops person?
• 25:26
  SPEAKER_00: Both, but it's really the CMO.
• 25:28
  unknown: Yeah.
• 25:29
  SPEAKER_00: And I've talked to like almost like 50, 60 CMOs in the last six months.
• 25:33
  SPEAKER_00: And they're all wondering how to create this transformation in their company.
• 25:38
  SPEAKER_00: Um and the reason this transformation hasn't happened yet is because Gen AI was so bad at staying on brand that everyone that tried Nano Banana, that tried Gemini, that tried um any of these um image and video models, they experienced the lack of brand adherence and they experienced last year's models.
• 25:55
  SPEAKER_00: Um like just in the last two weeks, the um improvement on Image Gen is like amazing.
• 26:01
  SPEAKER_01: Can you can you share some of the GTM insights that that are unique to you that worked in the last three to four years to reach to the enterprise and make them customers?
• 26:13
  SPEAKER_00: Yeah, so I guess um, it's I'll say maybe last like three years, and I'll say maybe this year.
• 26:20
  SPEAKER_00: So last three years, biggest insights are mostly just focusing on verticals where you can really change their life.
• 26:29
  SPEAKER_00: So, like some vertical that doesn't have a lot of first-party data, CDP doesn't really matter because most of their marketing program is not going to be influenceable by data.
• 26:38
  SPEAKER_00: Um, you find instead a retailer or um a media brand or a travel brand.
• 26:45
  SPEAKER_00: They know their customer, most of the customers are logged in, they can really influence the customer experience with first-party data.
• 26:50
  SPEAKER_00: So convincing our team to only go after the right verticals, um, hiring the right team, um, really, really being in the details myself of like what are we talking to customers about to get them to convert and then convert at higher ESV ACV.
• 27:07
  SPEAKER_00: These kinds of like really tactical things, honestly, is what grew the go-to-market function and the like revenue so quickly.
• 27:13
  SPEAKER_00: Um, and then just making sure you're going after the right market, right?
• 27:15
  SPEAKER_00: So we never went after SMB.
• 27:17
  SPEAKER_00: Everyone else says go after SMB and earn the right to go after enterprise.
• 27:21
  SPEAKER_00: We went after enterprise on day one.
• 27:23
  SPEAKER_00: Um we were starting to close Fortune 500, at least Fortune like 1000 in the first year of business.
• 27:28
  SPEAKER_00: Why?
• 27:28
  SPEAKER_00: Because we admitted to ourselves that if we're gonna be a serverless later on the data warehouse, we're not fit for SMB, we're only fit for enterprise.
• 27:36
  SPEAKER_00: So we just did enterprise on day one.
• 27:38
  SPEAKER_00: Um and then more recently, what's been working is running a modular company.
• 27:42
  SPEAKER_00: What I mean by modular company is that you have different businesses within your company.
• 27:47
  SPEAKER_00: And instead of them competing for resources and sales headcount and marketing headcount, they have their own budgets and they can just run.
• 27:53
  SPEAKER_00: I think Rippling does an amazing job of this.
• 27:55
  SPEAKER_00: Rippling runs their HR business, IT business, other business quite independently.
• 28:00
  SPEAKER_00: And their GMs are incentivized quite independently to make their business succeed or not succeed.
• 28:06
  SPEAKER_00: And so pre-AI, we didn't need to do this.
• 28:09
  SPEAKER_00: We were mostly a CTP business.
• 28:10
  SPEAKER_00: But in the last two years, we've now built these other two businesses that have their own PMs, their own engineers, soon their own salespeople and their own marketers that will be able to kind of run and not get blocked by approval or like convincing of other folks.
• 28:25
  SPEAKER_01: Got it.
• 28:26
  SPEAKER_01: So tell me more about building a modeler company and when should a founder think of building one?
• 28:32
  SPEAKER_00: Yeah, probably never.
• 28:33
  SPEAKER_00: I think most founders probably should not build one.
• 28:35
  SPEAKER_00: Right.
• 28:35
  SPEAKER_00: Um, it's much better to have just a coherent strategy and one thing that really works.
• 28:39
  SPEAKER_00: So if you can have like um, let's say you're a ramp, you have so many products, fine, but the corporate credit card is your main product and the main moneymaker.
• 28:49
  SPEAKER_00: You'd much rather just have that, and the corporate card gets you into every business.
• 28:53
  SPEAKER_00: And then once you get in the business, you sell them other things like spend management and so on.
• 28:56
  SPEAKER_00: I think that's ideal.
• 28:58
  SPEAKER_00: Marketing is very heterogeneous, and marketing has many buyers.
• 29:02
  SPEAKER_00: And so by selling to marketing, we kind of don't get to have the one single entry point.
• 29:06
  SPEAKER_00: Um, the person who's doing ads may not care what the IT person is doing or the email person is doing.
• 29:13
  SPEAKER_00: And so we think this modular company makes sense when you have multi-persa and when you want to grow in like nonlinear ways.
• 29:22
  SPEAKER_01: So uh for high touch, right?
• 29:25
  SPEAKER_01: What could be the feeling for revenue?
• 29:27
  SPEAKER_01: Could it be a billion dollars?
• 29:29
  SPEAKER_00: Um, I would at the moment I think it can be multi-billion.
• 29:34
  SPEAKER_00: Um, but we're always learning and iterating.
• 29:37
  SPEAKER_00: So like we don't have any, we're not married to IPOing or getting acquired.
• 29:43
  SPEAKER_00: We basically think like we should try to double revenue or more every single year and make sure that as we become a larger and larger company, we can sustain that pace.
• 29:54
  SPEAKER_00: Um, going multi-product is one reason for that, because we want to make sure that even at like a billion of revenue, what's the next year?
• 30:00
  SPEAKER_00: Is it 1.1 or is it 1.7?
• 30:02
  SPEAKER_00: And we want to make sure that we have the right product mix so that if and when we get to that scale, we can still keep growing at the same rate.
• 30:09
  SPEAKER_00: And so historically we've doubled year over year or more.
• 30:11
  SPEAKER_00: I think last year was like 120% growth.
• 30:14
  SPEAKER_00: Um, I want to be able to maintain that growth, which means investing in the right regions, the right products, and the right uh teams.
• 30:21
  SPEAKER_01: So let's say we discussed about it, but you know, if you can put it in more details, if you are right about your lead on the market, what does the marketing look like in five years and what is the role of human?
• 30:32
  SPEAKER_00: Uh, great question.
• 30:33
  SPEAKER_00: Okay.
• 30:34
  SPEAKER_00: So five years is unpredictable, but I think what will happen in five years is approximately this.
• 30:41
  SPEAKER_00: Um, a very, very um creative and smart marketer will wake up in the morning.
• 30:46
  SPEAKER_00: They'll have five ideas in their inbox created by AI.
• 30:49
  SPEAKER_00: AI worked all night, thought about the five ideas for them to improve their metrics.
• 30:53
  SPEAKER_00: Those five ideas could be in getting new customers, retaining customers, cross-selling, and so on.
• 30:58
  SPEAKER_00: Um, the marketer gives AI feedback on each of these ideas, and then AI runs with those ideas to coordinate across different teams, get the collateral that they need, um, build the email or the ad, and then come back to the marketer with, okay, I've based on your brief, which I helped you write, I've come back with the collateral and everything that you need.
• 31:16
  SPEAKER_00: Um, the legal approval, the brand approval, the collateral, um, the marketing, email campaign execution, and so on.
• 31:23
  SPEAKER_00: And now let's work on this together and launch this campaign.
• 31:26
  SPEAKER_00: Um, the more and more autonomous it gets, the less human discretion is necessary to say yes, no.
• 31:31
  SPEAKER_00: But the human should always say, This is a good idea, or here's a better idea.
• 31:34
  SPEAKER_00: So, very similar to cloud code plan mode.
• 31:37
  SPEAKER_00: Um, the plan is always wrong.
• 31:38
  SPEAKER_00: It's like somewhat good, but like always has something to improve on.
• 31:41
  SPEAKER_00: Um, the marketer in this case should be even more um able to push and pull on what the plan is.
• 31:49
  SPEAKER_00: So maybe the agent says, like, we're gonna run this tennis campaign, and the marketer says, hmm, give me some research on basketball, soccer, and squash.
• 31:58
  SPEAKER_00: And then the AI comes back.
• 31:59
  SPEAKER_00: So this is kind of the like kind of um push and pull.
• 32:02
  SPEAKER_00: And then when we're ready to run the experiment or the campaign, um AI can run it end to end and come back in four weeks with the results.
• 32:09
  SPEAKER_00: So something along these lines where every step has some human discretion: the legal step, the brand step, the creative step, and the marketing step.
• 32:16
  SPEAKER_00: Um, but AI can like run with the task and seek these approvals and come back and constantly be running these multiple threads.
• 32:25
  SPEAKER_00: So maybe one is a research thread for churn, maybe one is like this three new email campaigns and three new ads.
• 32:31
  SPEAKER_00: Um, and it's very similar to like the stuff you see on Twitter for these engineers that are managing like 10 or 20 Devon agents or 10 or 20 cloud codes.
• 32:40
  SPEAKER_00: Um, it should work very similarly.
• 32:42
  SPEAKER_00: And I think actually within one to two years, um the marketer will be saying that they can't do their job without AI or without high touch.
• 32:55
  SPEAKER_00: So today, if you're an engineer, it's quite impossible to do your job without cloud code, codecs, or like one of the um coding models.
• 33:03
  SPEAKER_00: Um, I spent like a couple weeks this year just coding full time.
• 33:06
  SPEAKER_00: And I literally like got to the point where I just couldn't write, like I couldn't do my job if um cloud code was down for an hour.
• 33:13
  SPEAKER_00: Um and they had a couple outages, like the whole team just stopped working.
• 33:16
  SPEAKER_00: Marketers will start working this way, where without high touch and without like basically, like people will think of high touch as the harness for marketing teams, the same way cloud code is the harness for coding teams.
• 33:28
  SPEAKER_00: Um without that harness, they can't do their job.
• 33:30
  SPEAKER_00: And that harness should have all the context of their past marketing, their email campaigns, ad campaigns, have access to all the customer data, and know what the CMO's top-level goals are, and have the destination um access, whether it's MCP, CLI, or API, doesn't matter, but the destination access to actually execute those campaigns.
• 33:47
  SPEAKER_01: But uh you will only like high touch is limited, I believe, by the customer data the company has.
• 33:54
  SPEAKER_01: You don't have a data of prospects, let's say hypothetically uh dominoes or a uh uh wants to go into one into a new country.
• 34:07
  SPEAKER_00: Yep.
• 34:08
  SPEAKER_01: Right.
• 34:08
  SPEAKER_01: Then then they then they are blinded because nobody has the data on the new country.
• 34:14
  SPEAKER_00: Um I think honestly, post AI, we have everything.
• 34:17
  SPEAKER_00: So we have built previous to AI, right?
• 34:20
  SPEAKER_00: It was sitting on your data warehouse, whatever structured data you have, you have, everything else you don't.
• 34:25
  SPEAKER_00: Now we've built agents that are scraping competitor insights, scraping competitor ads, um, figuring out what triggers matter based on weather or sporting events and so on.
• 34:35
  SPEAKER_00: And we're starting to build a lot of synthetic data for our customers via these agents.
• 34:40
  SPEAKER_00: And we host them all.
• 34:41
  SPEAKER_00: It's all part of the platform.
• 34:42
  SPEAKER_00: Um, and if someone were to drop in a lead list of, let's say, all customers we could go after in Japan, we have that lead list as well.
• 34:49
  SPEAKER_00: And we can marry it to the first party data and have the agent recurse over that as well.
• 34:53
  SPEAKER_00: So, like the world that we lived in before that was like, here's your SQL database, was very limiting, completely agreed.
• 35:02
  SPEAKER_00: And that's why this company is so exciting now, because now like the agent can just operate on unstructured data quite freely.
• 35:09
  SPEAKER_01: So but but the company would still need a qualified lead list to go after it, right?
• 35:13
  SPEAKER_01: Without it.
• 35:14
  SPEAKER_00: Kind of, but like marketers don't think this way.
• 35:16
  SPEAKER_00: Marketers think of it as let me run 50 million of ads in Japan.
• 35:20
  SPEAKER_00: By running the ads, I'll get my lead list in my database.
• 35:24
  SPEAKER_00: And then I'll further market that person more and more.
• 35:26
  SPEAKER_01: So, how would uh let's say if they want to spend$50 million in Japan, how would they approach it using high touch?
• 35:33
  SPEAKER_00: So, this we do.
• 35:34
  SPEAKER_00: We have like basically um we would have the agent look at all the ads that are working in other geographies, develop a brief or a plan for which ads we can run in Japan, and how to allocate that budget across the different content and campaigns.
• 35:47
  SPEAKER_00: Um, and then they would work hand in hand with the marketing team.
• 35:49
  SPEAKER_00: I think something like 50 million in Japan could take weeks for the team to develop together with AI.
• 35:54
  SPEAKER_00: But they would come with a whole like plan of here's the creative, we'll try, and the experiments will run.
• 35:59
  SPEAKER_00: And then the marketer will just incrementally run more and more ad experiments with AI and then recurse on those.
• 36:04
  SPEAKER_00: So it's the same way an ad agency works.
• 36:06
  SPEAKER_00: And it's the same way like high touch would launch in like, let's say, we launched in like um Australia and India recently.
• 36:12
  SPEAKER_00: So we are running ads now in Australia and India, um, but we can do them localized in different languages with the exact same collateral.
• 36:20
  SPEAKER_00: And um whatever doesn't work, the AI just will turn off.
• 36:24
  SPEAKER_00: And whatever does work, it'll bring back to our marketing team and say, hey, this is working.
• 36:28
  SPEAKER_00: I suggest you spend 5x more budget here.
• 36:31
  SPEAKER_00: So that's kind of like how marketing teams work.
• 36:33
  SPEAKER_00: They work in a very experimentation-driven way, and they just double down on what's working and they just kill what's not working.
• 36:38
  SPEAKER_01: Correct.
• 36:40
  SPEAKER_00: Um, and so this is like um, you know, like on Twitter, people are talking about loops and having like some loop where it's like the agent tries a thing, gets a reinforcement loop on if it's working or not, and then tries a thing.
• 36:51
  SPEAKER_00: Ads is actually the best loop.
• 36:53
  SPEAKER_00: So I think ads is a quite um configurable with AI.
• 36:57
  SPEAKER_01: But how would you give the team an approximate ROI that spending$50 million of opening a new market, let's say Japan, would give you 100 million revenue in the first year?
• 37:09
  SPEAKER_00: Um, it's this is like age-old problem in marketing, right?
• 37:13
  SPEAKER_00: Um, marketing success does not necessarily equal revenue success.
• 37:17
  SPEAKER_00: So that is ultimately up to someone else.
• 37:19
  SPEAKER_00: I think what we would have to prove is that if you spend 50 million in ads, then the leads we bring in are worth$50 million.
• 37:26
  SPEAKER_00: And then it's up to like, is it the right product mixed?
• 37:28
  SPEAKER_00: Can we serve that market and so on?
• 37:30
  SPEAKER_00: Um, but like usually ads teams are responsible for like cost per impression or cost per um conversion or lead.
• 37:37
  SPEAKER_00: And then once we've converted that lead, um, it's really the economics of the business on whether it was worth it for that conversion.
• 37:42
  SPEAKER_01: Yeah, but ultimately everybody in a company is responsible for revenue.
• 37:46
  SPEAKER_01: Yeah.
• 37:46
  SPEAKER_01: And this is how in AIH the company are getting structure.
• 37:50
  SPEAKER_01: Are you a builder?
• 37:51
  SPEAKER_01: Or let's say in any company, right?
• 37:52
  SPEAKER_01: Are you creating products or are you bringing revenue?
• 37:55
  SPEAKER_01: Right.
• 37:55
  SPEAKER_01: So I think every department today in any enterprise had that kind of pressure.
• 37:59
  SPEAKER_01: Yeah.
• 38:00
  SPEAKER_01: Because you cannot hide behind anything.
• 38:02
  SPEAKER_01: So a marketer cannot say that my job was to bring an MQL.
• 38:05
  SPEAKER_01: Yeah.
• 38:06
  SPEAKER_01: Now get the sales team to run the SQL or in a consumer company.
• 38:10
  SPEAKER_01: And the marketers cannot say they I got the best leads worth 500 million.
• 38:15
  SPEAKER_01: It's just that the team is not able to convert, so it's not my problem.
• 38:18
  SPEAKER_00: So I mean, since our all of our use cases are mostly consumer, it's pretty solved in consumer.
• 38:23
  SPEAKER_00: Like once you have the lead, um, you have the analytics of whether that person eventually bought the product.
• 38:28
  SPEAKER_00: Um, and so my only comment on this is there are some businesses that can't prove revenue in the first year.
• 38:34
  SPEAKER_00: They can prove like conversion of leads, but then revenue comes next year.
• 38:37
  SPEAKER_00: There's other businesses like quick consumer businesses where the lead should convert in the first 30 days.
• 38:43
  SPEAKER_00: And so I think those businesses can have a good reinforcement loop very quickly.
• 38:46
  SPEAKER_00: And most of these companies that are running like significant ad spend have that kind of reinforcement loop.
• 38:51
  SPEAKER_01: So uh uh, you know, I have interviewed some of the guests in my podcast, which are like some of the leading AI researchers here in SF, and they have said that uh most problems today, even outside of you know the digital workforce, uh are can if they can be a coding problem, then they can be solved via AI and agents.
• 39:14
  SPEAKER_01: What's what's your view on the marketing harness?
• 39:17
  SPEAKER_00: Yeah, so I think um I guess two things.
• 39:19
  SPEAKER_00: One, uh I completely agree that most problems are coding problems because coding is just an expression of logic.
• 39:26
  SPEAKER_00: Maths, people.
• 39:27
  SPEAKER_00: Yeah.
• 39:27
  SPEAKER_00: And it's ultimately like, what is even coding, right?
• 39:30
  SPEAKER_00: It's like um if if you think of it as like an expression of logic or an expression of thinking, then it's just a way for like the model to operate in like our plane versus like a different plane.
• 39:43
  SPEAKER_00: Um so yeah, I think marketing problems will be solved by coding agents, if that makes sense.
• 39:47
  SPEAKER_00: They won't be solved by coding agents without much, without a harness for them to use.
• 39:54
  SPEAKER_00: But with the right harness, um content assembly, research, campaign ideation, all these things can be solved by coding agents, because the coding agent can like recurse over logic, over data, over campaigns, over image embeddings and video embeddings, and so on to figure out what to do next.
• 40:13
  SPEAKER_00: Um, and then if I answer the question about like what is a marketing harness, I think like basically like you have a foundation model.
• 40:21
  SPEAKER_00: Um, the foundation model needs a harness to operate on marketing activities.
• 40:25
  SPEAKER_00: Marketing activities requires two pieces of context and then some execution layer, the, or three pieces of context.
• 40:31
  SPEAKER_00: The context is your customer data, which comes from the data warehouse and some semantic layer, which HighCitch provides.
• 40:37
  SPEAKER_00: Then it comes from all of your channel data, which is your ad channels and your email channels.
• 40:42
  SPEAKER_00: Then it comes from your business context and your CMO saying what matters, your CEO saying what matters, and what are the business priorities.
• 40:48
  SPEAKER_00: With these three pieces of context, the context window would be greatly exceeded.
• 40:54
  SPEAKER_00: So the harness has to index this information and turn it into vector databases that, or like document stores, that the harness can then talk to and query in order to get the right information at the right time.
• 41:06
  SPEAKER_00: This context engineering task is actually a lot of work.
• 41:08
  SPEAKER_00: And it's heterogeneous between different enterprises.
• 41:10
  SPEAKER_00: Some enterprises use this database, some people use this channel tool.
• 41:14
  SPEAKER_00: Highly heterogeneous task, and that state management or context management has to be constantly refreshed and constantly indexed.
• 41:21
  SPEAKER_00: So that's like the number one task.
• 41:23
  SPEAKER_00: The second task is being able to actually execute that campaign, which means create the content and actually execute in the channel tools.
• 41:30
  SPEAKER_00: Um I can go into detail for that, but that's quite thick as well, as we call it.
• 41:35
  SPEAKER_00: So it's like a thick harness versus a thin harness.
• 41:36
  SPEAKER_01: We'd love to know what's the thick harness, what's the thin analytics?
• 41:39
  SPEAKER_00: Yeah, so I basically think of it as like a thin harness is like a thin wrapper.
• 41:42
  SPEAKER_00: Anyone could build it in like five minutes.
• 41:44
  SPEAKER_00: Um I could probably tell a cloud code agent to build it while we finish this conversation.
• 41:48
  SPEAKER_00: A thick harness is something that it would require me to have like tens of engineers working for months to build this, and actually they still might not finish.
• 41:56
  SPEAKER_00: We will be improving the thickness or like the capabilities of this context engineering probably for the next three years.
• 42:02
  SPEAKER_00: Um, and the more enterprise customers that sign on, the more they'll challenge what context we provide them, and we'll build more and more on the context engineering piece.
• 42:10
  SPEAKER_01: Why would an enterprise care?
• 42:12
  SPEAKER_01: Like an most marketers are not tech non-technical users.
• 42:15
  SPEAKER_00: Yeah, they don't care at all about the technology.
• 42:17
  SPEAKER_00: They care about the AI being able to operate on their task.
• 42:20
  SPEAKER_00: And without the context, the AI cannot operate on the task.
• 42:23
  SPEAKER_01: Yeah.
• 42:23
  SPEAKER_01: So why would they care about the thick harness?
• 42:26
  SPEAKER_00: Uh they don't care.
• 42:27
  SPEAKER_00: It's only high-tech cares, right?
• 42:29
  SPEAKER_00: Because we need to make sure that we're working on a business that is long-term defensible and provides real value.
• 42:35
  SPEAKER_00: If the IT team at this brand can simply create that harness in five minutes, there's no point in high-touch building it.
• 42:42
  SPEAKER_00: They should just build it in-house.
• 42:44
  SPEAKER_00: If it would take them a year to build this internally, then actually it's a great product for high-touch to build.
• 42:48
  SPEAKER_00: So the thickness of the harness is much more about us building something that is durable and it is about us building something that is differentiated from the foundation model.
• 42:58
  SPEAKER_00: If Gemini and ChatGPT can do it, the marketer will just use Gemini and ChatGPT.
• 43:02
  SPEAKER_00: But if we're the only um harness that can do this, then they'll come to us and we will earn the right to serve them that token.
• 43:10
  SPEAKER_01: So for example, uh ad creation chat GPT can do it, Gemini can do it.
• 43:15
  SPEAKER_00: I would disagree.
• 43:17
  SPEAKER_01: Why?
• 43:18
  SPEAKER_00: I don't think ChatGPT and Gemini can stay on brand, especially in enterprise.
• 43:23
  SPEAKER_00: Um and additionally, not only can they not stay on brand, um, they don't know what performs.
• 43:29
  SPEAKER_00: Once you do this context engineering task, I can look at all of your ads um for the entire history of your brand and compare that to the generations and tell you if this ad will perform or not perform.
• 43:40
  SPEAKER_01: But for example, you know, uh if anthropic and chat GPT uh can give forward deployed engineers to uh an enterprise.
• 43:51
  SPEAKER_00: Yeah, then this can be built.
• 43:52
  SPEAKER_00: Yeah.
• 43:53
  SPEAKER_01: So do you get these questions often that say, hey, what happens if anthropic and open AI?
• 43:58
  SPEAKER_00: Um as soon as I Show them the context?
• 44:01
  SPEAKER_00: No.
• 44:01
  SPEAKER_00: So when I show them like here's all the places we're taking context from and indexing so the LLM can then read and query from this, immediately they're like sold that they need a higher harness for marketing versus waiting for the foundation model.
• 44:15
  SPEAKER_01: So would you say that in the current market serving enterprise, uh high touch has uh has the best marketing harness or has the only marketing harness?
• 44:24
  SPEAKER_00: 100%.
• 44:26
  unknown: Yeah.
• 44:26
  SPEAKER_00: Only invest for sure.
• 44:27
  SPEAKER_00: Yeah.
• 44:27
  SPEAKER_01: Only and best.
• 44:28
  SPEAKER_00: And it's in its in in it's getting better rapidly.
• 44:31
  SPEAKER_01: Okay.
• 44:32
  SPEAKER_00: Yeah.
• 44:32
  SPEAKER_01: Well, other companies aren't able to build it.
• 44:35
  SPEAKER_00: I think they're not thinking about it this way.
• 44:36
  SPEAKER_00: I mean, maybe they could build it if they tried, um, but they're not investing in context that is unstructured.
• 44:46
  SPEAKER_00: They don't have the structured data context that we have built via CDP for the past five years.
• 44:50
  SPEAKER_00: Um, they have not built the destination execution layers that we built for the past five years.
• 44:55
  SPEAKER_00: So there's so many primitives that we get to inherit from our SaaS business for the last six years that are primitives that this harness uses, um, that are just easily available, that our engineers have worked five years on, right?
• 45:06
  SPEAKER_00: So these primitives are easily available.
• 45:08
  SPEAKER_00: Then the incremental 30 to 50 engineers that we've tasked with building the context, the execution, the content generation, and so on, they're not able to invest that kind of resource in building each of these things at once.
• 45:19
  SPEAKER_00: They're focused on one agent to do one thing, such as JPEG generation, or one agent to do one thing, which is media buying on Facebook.
• 45:25
  SPEAKER_00: But it's not the end-to-end.
• 45:27
  SPEAKER_01: Got it.
• 45:28
  SPEAKER_00: And I kind of find that like when agents can do the end-to-end, the power is not just like two X more, three X more, it's like 100x more, because it has context on your customers in Japan are interested in these things because the ads in Japan are performing better about these images.
• 45:44
  SPEAKER_00: Yeah.
• 45:44
  SPEAKER_00: So I should tell the whole rest of the agentic and human marketing team that this is working, and then they'll execute on this.
• 45:52
  SPEAKER_01: Got it.
• 45:53
  SPEAKER_01: So for a company to implement high touch across all its geographies, how much time and to see its results across all its geographies?
• 46:03
  SPEAKER_01: US, Japan, Asia, how much time does it take?
• 46:07
  SPEAKER_00: Um, it will probably take a month per geography.
• 46:09
  SPEAKER_00: Um you can do in parallel or you can do serially, so it's up to you.
• 46:12
  SPEAKER_00: And we do give you a forward deployed team.
• 46:15
  SPEAKER_00: So especially for creative assembly, um, we have like forward deployed designers actually that will sit with your designers and onboard them onto this platform, and forward deployed marketers that will sit with your marketers and onboard them as well.
• 46:27
  SPEAKER_00: So, but I would say like month is enough for any single geography.
• 46:32
  SPEAKER_01: And and what what is the stack that you usually replace during this?
• 46:36
  SPEAKER_00: We don't replace staff.
• 46:37
  SPEAKER_01: No, stack.
• 46:39
  SPEAKER_00: Oh, stack.
• 46:39
  SPEAKER_00: Okay, see.
• 46:40
  SPEAKER_00: Um I don't know.
• 46:43
  SPEAKER_00: So like we can offer we can replace the CDP.
• 46:47
  SPEAKER_00: We can replace many parts of Adobe and like journey optimization and so on.
• 46:52
  SPEAKER_00: But in this like marketing harness world, it's really not about replacing a stack.
• 46:57
  SPEAKER_00: It's really about taking your stack and suddenly like superpowering that with significantly more personalization, more campaigns, more content, more data.
• 47:05
  SPEAKER_00: So we're really thinking like, it's like if you ask like me, like which part of the stack does cloud code replace, it doesn't.
• 47:11
  SPEAKER_00: It's just enabling significant more.
• 47:13
  SPEAKER_00: And now there's like 10x more PRs on GitHub, 10x more bugs in big GitHub, and like so on.
• 47:18
  SPEAKER_01: But but cloud code has completely replaced the IDE.
• 47:22
  SPEAKER_00: Yeah.
• 47:23
  SPEAKER_00: But like I don't know how much you even pay for ID anyways, it doesn't matter.
• 47:27
  SPEAKER_01: So, you know, we are towards the conclusion of the podcast, right?
• 47:30
  SPEAKER_01: You know, kudos of the journey of building high tech.
• 47:33
  SPEAKER_01: Like it seems like you're just getting started now.
• 47:36
  SPEAKER_00: Yeah.
• 47:36
  SPEAKER_00: It feels that way too.
• 47:38
  SPEAKER_01: Uh and uh if you have to summarize, what are some of the decisions that you know you as a founder's or team made, right?
• 47:47
  SPEAKER_01: That made high-tech today where it is.
• 47:51
  SPEAKER_00: Um we went enterprise really early, which everyone said not to do.
• 47:55
  SPEAKER_00: We went multi-product very early, which everyone said not to do.
• 47:58
  SPEAKER_00: Uh, we hired salespeople very early, which everyone said not to do.
• 48:01
  SPEAKER_00: Uh, we made all these controversial decisions that were wrong for most startups, but correct for high touch.
• 48:06
  SPEAKER_00: And so every single year we ask ourselves: what is best for high touch?
• 48:10
  SPEAKER_00: And like, how do we think first principles about the needs of high touch customers and high touch employees rather than like the standard operating playbook of a startup?
• 48:19
  SPEAKER_00: And I think those things have really helped us.
• 48:21
  SPEAKER_01: And why do you think they work for high touch and they don't work for some of the other startups?
• 48:25
  SPEAKER_00: Each individual thing has its own reason.
• 48:27
  SPEAKER_00: So most companies, if they go multi-product too soon, they will lose focus, nothing will work, and each thing will be mediocre.
• 48:34
  SPEAKER_00: Um, for us, we knew that like A, the products were so linked together, and B, that the reverse ETL product would become commodity.
• 48:41
  SPEAKER_00: So we needed to build the next level of the platform to actually like really increase revenue growth in our own metrics.
• 48:46
  SPEAKER_00: So it's just these decisions that are controversial.
• 48:50
  SPEAKER_00: Like you could say that like Docker should have made some of these decisions.
• 48:53
  SPEAKER_00: They should have focused on enterprise, they should have focused on like what comes after the initial product and so on.
• 49:00
  SPEAKER_00: So some of them have parallels, but like many of them don't.
• 49:03
  SPEAKER_00: Slack did the right thing by focusing on one product forever and not caring about enterprise versus SMB.
• 49:09
  SPEAKER_01: And and uh why enterprise was the right choice for you, though it would have, I assume, taken many months to close your first pain enterprise customer.
• 49:17
  SPEAKER_00: Yeah.
• 49:17
  SPEAKER_00: Um, because we had product market fit in enterprise, and we did not have product market fit in SMB.
• 49:22
  SPEAKER_01: How do you define product market fit for yourself?
• 49:24
  SPEAKER_00: Um the ability for customers, well, basically us able to provide way more value than alternatives in enterprise versus SMB.
• 49:38
  SPEAKER_00: In SMB, A, we can't provide value because we're not even fit as an architecture.
• 49:44
  SPEAKER_00: B, like even if we can be fit as an architecture, the value is like minimally better than alternatives.
• 49:50
  SPEAKER_00: In enterprise, the value is significantly better than alternatives, and the alternatives don't even work.
• 49:55
  SPEAKER_00: So it's just great product market fit versus like mediocre.
• 49:59
  SPEAKER_01: So in your journey, you know, as a CEO from 0 to 1, 1 to 10, 10 to 100, and 100 to X, what are the things as frameworks that help you scale?
• 50:10
  SPEAKER_00: Mostly just, I don't know.
• 50:13
  SPEAKER_00: I don't really have any frameworks that helped us scale.
• 50:15
  SPEAKER_00: Um in the early days, we really believed in marketing driven growth.
• 50:19
  SPEAKER_00: So we thought like hiring sales is too slow.
• 50:22
  SPEAKER_00: Um we want to hire salespeople to close deals, but we want the pipeline to come inbound.
• 50:26
  SPEAKER_00: And so we really focused on like what can we build and like do marketing around so the pipeline could come inbound.
• 50:32
  SPEAKER_00: And we didn't settle for like triple, triple, double, double.
• 50:35
  SPEAKER_00: Like we went from two to 10 and then 10 to 20 in revenue.
• 50:38
  SPEAKER_00: And like we just really focused on like how do we get as many logos as possible?
• 50:42
  SPEAKER_00: Don't worry about charging them crazy amounts, um, just focus on land.
• 50:47
  SPEAKER_00: Um, then we switched over to focus more on revenue and like providing value.
• 50:51
  SPEAKER_00: Now we're focused on actually with AI, there's so much more impact that we can make.
• 50:55
  SPEAKER_00: So let's actually build this modular multi-product company and do the end-to-end stack for marketing teams, all with AI, rather than like some pieces of the stack.
• 51:04
  SPEAKER_00: And so each decision and each like um each decision is made stage appropriate based on like what opportunity you see that year and how do you plan for the next opportunity.
• 51:14
  SPEAKER_00: But every year, like the reason multi-product, if it doesn't work this year, it doesn't matter that much, but it needs to produce next year.
• 51:21
  SPEAKER_00: So that's why we start doing it like six months ago.
• 51:23
  unknown: Right.
• 51:24
  SPEAKER_00: So like I think like a lot of the founders that don't scale, they're always firefighting the needs of today versus building the company of tomorrow.
• 51:31
  SPEAKER_00: And so I really try to make sure that we're building the company of tomorrow versus um night and day living in the business of today.
• 51:39
  SPEAKER_01: So then uh how do you prioritize meeting the the quarterly targets and everything?
• 51:46
  SPEAKER_00: Yeah, that's what the exec team is for.
• 51:47
  SPEAKER_00: The exec team's job is to meet the quarterly targets, and then our job is to make sure that we are working or they are also working on the targets for next year.
• 51:58
  SPEAKER_01: So then the product reports to you or your co-founder?
• 52:01
  SPEAKER_00: Yeah, so um we kind of share all the responsibilities, but it's me and two other co-founders.
• 52:07
  SPEAKER_00: Um, and then the reporting structure is also kind of flexible, but all of product and marketing reports to him, and then like revenue and finance reports to me.
• 52:15
  SPEAKER_01: Got it.
• 52:17
  SPEAKER_01: And and where do you spend 80% of your time on?
• 52:22
  SPEAKER_00: Um, it really varies.
• 52:23
  SPEAKER_00: I think of like every month or every almost like two weeks as a sprint.
• 52:27
  SPEAKER_00: So like last two weeks I might have spent on headcount planning.
• 52:31
  SPEAKER_00: Um, the previous two weeks I would have spent on how do I make the ads platform scale quickly?
• 52:35
  SPEAKER_00: And are we like ready to scale it?
• 52:38
  SPEAKER_00: Um the previous two weeks I might have spent on something completely different.
• 52:41
  SPEAKER_00: But I just don't think like I think it's like much more like the Jensen type operating model.
• 52:45
  SPEAKER_00: Like do one thing deeply and then get out, and then do one thing deeply and get out.
• 52:48
  SPEAKER_00: Got it.
• 52:49
  SPEAKER_00: More so than like every day.
• 52:50
  SPEAKER_01: Got it.
• 52:51
  SPEAKER_01: So, so some uh, you know, the last few questions are rapid fire.
• 52:54
  SPEAKER_01: Cool.
• 52:55
  SPEAKER_01: So so one AI tool that has 10x your productivity.
• 53:00
  SPEAKER_00: Um definitely just cloud.
• 53:02
  SPEAKER_00: I'm not very good at using AI outside of like Cloud and oh actually OpenCloud, OpenCloud, Cloud, and Glean.
• 53:07
  unknown: Yeah.
• 53:08
  SPEAKER_01: Uh one uh one interesting GTM hack that has worked for you.
• 53:14
  SPEAKER_00: I don't have any hacks.
• 53:17
  SPEAKER_00: Back in the day, we used to do like a bunch of like scripting on like LinkedIn, but that's like a long time ago.
• 53:23
  SPEAKER_01: What has helped you build uh the largest pipeline at every stage of the company?
• 53:29
  SPEAKER_00: Um just being like in the details with the salespeople and like the sales team, I mean, being like really in the details of like which customers are they talking to, what are those customers saying?
• 53:38
  SPEAKER_00: If you're in the customer conversation, then you can like do the right marketing, do the right pitch, build the right product, and so on.
• 53:45
  SPEAKER_01: Uh one founder, operator, or investor that has changed the trajectory of the company.
• 53:52
  SPEAKER_01: Oh, interesting.
• 53:55
  SPEAKER_00: Um well, maybe one example is um there's this YC partner named JJ, JJ Fliegelman.
• 54:03
  SPEAKER_00: And he like in the very early days, he kind of gave us the idea for reverse ETL.
• 54:08
  SPEAKER_00: Like we were working on um something adjacent, and he was like, guys, like, this is never gonna work.
• 54:14
  SPEAKER_00: Just sync the data to Salesforce, and here's how you could do it.
• 54:17
  SPEAKER_00: And we were like, why would you pay money for that?
• 54:19
  SPEAKER_00: He's like, I don't want to build it.
• 54:20
  SPEAKER_00: And I have like great engineers, I'm never gonna build it.
• 54:23
  SPEAKER_00: He was the founder of company at the time, now he's a YC partner.
• 54:25
  SPEAKER_00: And he was like, just build it, I'll just pay you 24K.
• 54:28
  SPEAKER_00: And like, this is just gonna sell like hotcakes because everyone needs this.
• 54:33
  SPEAKER_01: Gotta one book or podcast that has helped you a lot.
• 54:38
  SPEAKER_00: Um, is really lame, but the qualified sales leader, it's by John McMahon.
• 54:42
  SPEAKER_00: He's like the classical like Bible for sales.
• 54:45
  SPEAKER_00: I think it's just like still really accurate on the things that like a founder CEO should know in order to operate a sales team.
• 54:53
  SPEAKER_01: And and what message in the book resonated with you the most?
• 54:58
  SPEAKER_00: I can't paint any one thing.
• 54:59
  SPEAKER_00: Like, I think like the best thing I have, I don't know if it's from the book or just in general, is like you, as the CEO, need to do the pipeline generation with the team.
• 55:08
  SPEAKER_00: Everyone thinks I'll hire SDR team or marketing team or like a CRO and they'll just do it all.
• 55:13
  SPEAKER_00: If you're not talking to the customer and sourcing the leads with the team, then you're not gonna figure it out.
• 55:17
  SPEAKER_01: So you sit on the pipeline generation every day?
• 55:20
  SPEAKER_00: I was sitting on it every day for the last like four years.
• 55:24
  SPEAKER_00: I stopped doing it this year.
• 55:25
  SPEAKER_01: Okay.
• 55:26
  SPEAKER_00: But like before that, uh yeah, and I would hand select every patch for every rep in the company.
• 55:34
  SPEAKER_01: Every pot for every rep?
• 55:36
  SPEAKER_00: Every um account select like the um territory for each rep, I would just hand select.
• 55:41
  SPEAKER_00: So which um customers or prospects, sorry, can this rep reach out to?
• 55:45
  SPEAKER_00: I would just choose with the rep together.
• 55:48
  SPEAKER_01: Got it.
• 55:48
  SPEAKER_01: Well, thank you so much.
• 55:49
  SPEAKER_01: I loved doing this podcast with you.
• 55:51
  SPEAKER_00: Thank you as well.
• 55:52
  SPEAKER_01: I clearly see like uh high touch going to a ten billion dollar company.
• 55:56
  SPEAKER_01: Very easy.
• 55:56
  SPEAKER_00: I hope so.
• 55:57
  SPEAKER_00: Yeah.
• 55:58
  SPEAKER_01: It's been such a pleasure hosting the podcast.
• 56:00
  SPEAKER_00: Thank you.