Brian Bell · Aug 24, 2026 · 11 min read

Ignite Startups: How AI Is Reinventing Lifecycle Marketing with Neha Mittal | Ep291

Neha Mittal, co-founder and CEO of Just AI, explains how AI is reshaping lifecycle marketing by replacing complex campaign logic with continuous personalization, faster experimentation, and measurable revenue impact, while changing how marketing teams are structured and scaled.

Marketing teams have spent decades chasing the same promise: deliver the right message to the right customer at exactly the right moment.

The tools improved. Customer data became richer. Marketing automation became more sophisticated. But the fundamental process remained surprisingly manual. Teams built audience segments, created branching workflows, wrote multiple versions of campaigns, ran A/B tests, waited for results, and repeated the process.

Neha Mittal believes AI is finally changing that model.

Mittal is the co-founder and CEO of Just AI, a Team Ignite portfolio company building AI-powered infrastructure for lifecycle marketing and personalization. Her path to founding the company has taken her from Goldman Sachs to startups, Berkeley, Twitter, Pinterest, and eventually Y Combinator.

Along the way, she repeatedly encountered the same problem: companies possessed enormous amounts of customer data, but their ability to turn that data into truly personalized experiences remained limited.

Just AI was built to close that gap.

From Goldman Sachs to the Startup World

Mittal's entrepreneurial instincts appeared early.

While studying in Singapore, she was introduced to the concept of "technopreneurship"—combining technology and business to build companies. The idea immediately appealed to her, and she participated in a startup program while still in college.

After graduation, however, she followed a more conventional path and joined Goldman Sachs.

The prestige was real. The fit was not.

Within a few years, Mittal realized she wanted to build rather than remain inside a large institution. When a former university colleague invited her to join a startup that was only a month old, the offer came with roughly a 60% pay cut.

She accepted almost immediately.

The financial tradeoff initially seemed dramatic, but the psychological effect was the opposite. Once she made the jump, she realized she was far more energized by startup life than by the security and status of a traditional finance career.

That experience became an early lesson in career risk: sometimes the things that appear most dangerous from the outside are exactly the moves that create momentum.

Learning Through Startups, Twitter, and Pinterest

Mittal eventually moved to Silicon Valley, attended Berkeley, and worked in education technology before co-founding City Structure, a startup focused on helping property owners understand how they could expand or modify their properties using data and drone technology.

Her career then moved toward a problem that would ultimately become much more consequential.

At Twitter, GDPR forced the company to understand how user data was being handled across the organization. Mittal joined to help build that capability from scratch. The effort grew from a small team into an organization involving hundreds of people.

From there, she moved into Twitter's growth organization.

Growth teams faced a different problem: they needed daily active users to increase, but many growth decisions were still driven by ideas, manual experimentation, and individual judgment.

A marketer might propose a campaign. The company would run an A/B test. Then the team would wait to see whether anything improved.

Mittal and her colleagues began building internal systems that could make growth experimentation more systematic. According to Mittal, those efforts eventually drove roughly 15–20% movement in daily active user metrics.

The team occasionally joked that the internal product could become a company of its own.

Years later, it effectively did.

Why Lifecycle Marketing Matters

Lifecycle marketing begins after a company acquires a user.

Consider a product-led company such as Notion. A user signs up, but acquisition alone does not create a valuable customer.

The company still needs to:

activate the user,
teach them how to use the product,
re-engage them if they disappear,
retain them,
convert free users into paying customers,
upsell or cross-sell additional products.

Those interactions happen through channels such as email, push notifications, SMS, and in-product messaging.

The economic logic is straightforward. Mittal notes that acquiring a new user can cost dramatically more than retaining an existing one. That makes lifecycle marketing one of the most important—but often underappreciated—parts of the growth stack.

The "Octopus Problem" of Traditional Marketing Automation

Traditional marketing platforms made segmentation easier, but they also created another problem.

Teams began building increasingly complex workflow trees.

If a customer is free, send one message.

If the customer is paid, send another.

If they live in one country, add another branch.

If they performed a certain action, create another condition.

Multiply those rules across hundreds of campaigns and organizations end up with what Mittal describes as an "octopus" of if-else logic.

Eventually, marketers no longer know what is working, why it is working, or which branch should be changed next.

AI changes the underlying approach.

Instead of manually encoding every decision rule, a company can provide a system with hundreds of customer signals, define the desired outcome, and allow a machine-learning system to determine which message should be shown to which user.

That is the shift Just AI is betting on.

From A/B Testing to Continuous AI Optimization

Just AI began with a relatively narrow problem: optimizing marketing copy.

The platform could generate multiple variants of messages and apply reinforcement learning to determine which versions performed best for different customers.

Over time, the system could feed those results back into the models, creating a continuous learning loop.

This approach compresses experimentation dramatically.

Mittal described customers effectively running what would previously have amounted to years of A/B testing within a much shorter period.

But that creates an unexpected problem.

If AI can generate thousands of variants and optimize hundreds of campaigns, who manages all of that output?

The old bottleneck was creating enough content and experiments.

The new bottleneck becomes operational oversight.

AI removes one form of scarcity and creates another.

Why AI Still Needs Guardrails

Allowing AI to communicate directly with customers introduces obvious risks.

A hallucinated or poorly localized marketing message can damage a brand instantly.

Mittal described an early localization experiment in which a Korean-language output produced an inappropriate translation. Experiences like that reinforced an important product principle: automation should not mean uncontrolled automation.

Just AI therefore separates content creation from autonomous decision-making.

Marketers can first generate message variants, review them, apply company context and brand standards, and approve the content.

Only after that approval does the AI decide which customer should receive which message.

The system also applies safeguards around experimentation and measurement.

For example, running 20 variants against an audience of only 100 weekly users is unlikely to produce meaningful statistical results. Similarly, optimizing around an event that occurs once per week may not provide enough data to support reliable decisions.

AI may automate execution, but companies still need sound measurement.

Explainability May Matter More Than "AI Magic"

One of Mittal's more counterintuitive lessons is that customers often care less about impressive AI performance than founders might expect.

A platform can show large improvements across a dashboard, but if the customer cannot understand why the metric improved, confidence remains limited.

By contrast, weaker results can still be useful when the system clearly explains what happened and what should be done next.

That creates an important distinction between AI products that merely generate outputs and AI products that help businesses make decisions.

For enterprise users, transparency increasingly becomes part of the product.

The Marketing Team Is Getting Smaller—and Broader

AI is also changing organizational structure.

Mittal has observed lifecycle marketers moving closer to product teams, with some marketers increasingly functioning like product managers.

That shift makes sense.

If lifecycle communication is increasingly automated and driven by product behavior, the separation between "marketing experience" and "product experience" becomes less meaningful.

At the same time, companies may need fewer dedicated specialists.

A lifecycle operation that once required multiple marketers, writers, designers, analysts, and experimentation specialists may increasingly be run by one person using AI-supported systems.

Mittal's longer-term vision is even more aggressive: lifecycle marketing could eventually become a responsibility handled by one person—or half of one person's role.

The function would also be judged differently.

Instead of being seen as a team that "runs campaigns," lifecycle marketing would become a measurable revenue-generating function.

What Kind of Revenue Impact Can AI Marketing Produce?

According to Mittal, lifecycle marketing typically contributes roughly 10–15% of company top-line revenue in the types of businesses Just AI works with.

Just AI has seen additional improvements of approximately 15–20% on that lifecycle contribution, translating in some cases into roughly a 3–4% improvement in overall top-line revenue.

The efficiency impact can be significant as well.

Mittal said some customers have removed planned job openings because work that previously required additional hires could instead be handled through automation.

One-to-One Personalization Is More Complicated Than It Sounds

Marketers often describe the future as "one-to-one personalization."

Mittal thinks the concept is frequently misunderstood.

The simplistic interpretation is that a company examines one customer's behavior and determines the perfect message for that individual.

But personalization rarely works in isolation.

To understand what a particular customer is likely to respond to, the system must learn from the behavior of the broader population—especially users who behave similarly.

True one-to-one personalization therefore depends on population-level learning.

The system becomes personalized precisely because it learns continuously from everyone.

Building an AI Startup Without Scaling Too Early

Mittal also shared lessons from building Just AI through Y Combinator.

One of the most valuable was raising relatively little money early.

Limited capital forced the company to remain focused on finding product-market fit instead of creating the appearance of progress by hiring aggressively.

Large funding rounds can encourage founders to deploy capital simply because it exists.

More employees create additional communication layers. More teams create coordination problems. And organizations can become slower before they have even discovered exactly what they should be building.

But Mittal also discovered the opposite danger.

Waiting too long to hire can become a disadvantage in an AI market evolving at extraordinary speed.

The challenge is not simply staying lean.

It is knowing when to stop staying lean.

Founder Breakups and Near-Death Experiences

The path was not smooth.

During Y Combinator, Mittal went through a co-founder breakup—an event she describes as enormously difficult.

She also remembers early customer churn as a series of near-death experiences for the company.

Losing an early customer hurts differently from losing one later. Founders have often invested enormous personal effort into those first relationships, and every churn event can feel like a verdict on the entire company.

In hindsight, Mittal sees those failures differently.

They forced the company to confront problems instead of allowing mediocre outcomes to remain hidden.

The difficult moments produced clarity.

The Bigger AI Opportunity

Mittal believes today's founders and technologists are operating during a historically important period.

She expects future generations to look back at the current San Francisco AI ecosystem in much the same way people remember the early mobile or internet eras.

The tools are still imperfect.

AI can be frustrating.

Using it may initially take longer than performing a task manually.

But Mittal argues that companies cannot afford to avoid the experimentation phase.

The organizations that continuously experiment with AI today are discovering workflows and use cases that competitors may not recognize until much later.

And the implications go far beyond marketing.

Brian Bell argues that within the coming years, it may become possible for solo founders to manage entire businesses through layers of AI agents—a CMO agent, CTO agent, product agent, and other automated systems managing additional agents underneath them.

That future still sounds like science fiction.

But many aspects of today's AI landscape would have sounded equally unrealistic only a few years ago.

The Bottom Line

The future of lifecycle marketing is not simply about using AI to write better emails.

It is about replacing static campaigns with systems that continuously learn.

Instead of marketers manually creating hundreds of audience segments and campaign branches, AI can evaluate enormous numbers of customer signals, determine which messages work, learn from the results, and optimize the next interaction.

That could transform lifecycle marketing from a labor-intensive operational function into a measurable revenue engine.

And it may fundamentally change the size and structure of marketing teams.

The companies that win will not necessarily be the ones that automate everything immediately. They will be the ones that learn where automation works, where human judgment remains necessary, and how to build systems that combine both.

For Mittal and Just AI, that is the opportunity: helping companies move from manually managing customer journeys to intelligently optimizing every interaction.

Chapters:
00:17 – Neha’s origin story

00:32Discovering technopreneurship in Singapore
01:23Why Goldman Sachs was not the right fit
02:03Taking a 60% salary cut to join a startup
03:36Moving to Berkeley and exploring edtech
04:33Building City Structure with drone data
05:19Joining Twitter and building its data organization
07:09Experiencing Twitter before and after Elon Musk
08:24The Berkeley connection
09:57The origin of Just AI
12:15What lifecycle marketing actually does
15:53How marketing automation evolved
16:23The “octopus” problem of if-else marketing logic
17:36Getting the right message to the right user
18:07Building AI guardrails for customer-facing content
19:02Localization mistakes and why explainability matters
20:37The new scarcity created by AI
21:26Compressing years of A/B testing into weeks
22:54The hardest enterprise customer objection
24:50How much control should AI users have?
27:06How AI is restructuring marketing teams
28:37Can one marketer run lifecycle marketing?
29:33Which marketing tools AI can replace or reduce
31:07What lifecycle marketing could look like in five years
31:39Measuring revenue lift from lifecycle optimization
33:09Why companies need to experiment with AI now
34:24What Neha changed her mind about
36:20Why one-to-one personalization is misunderstood
37:46Fundraising, staying lean, and when to hire

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Brian Bell (00:00.868) Hey everyone, welcome back to the Ignite podcast. today we are delighted to have N Niha Mittal on the program. She is the co-founder and CEO of Just AI, a portfolio company and fair disclosure of Team Ignite. So we're really excited to sit down and talk to her. Thanks for coming on, Na Neha.

Neha Mittal (00:15.958) Yeah, thanks for having me, Brian.

Brian Bell (00:17.924) Well I'd love to start with your background, what your origin story.

Neha Mittal (00:22.934) yeah. original original, like where where I started my career or All right, let's do it. well

Brian Bell (00:26.264) Yeah, yeah. The whole the whole the whole thing, the whole story.

Neha Mittal (00:32.822) I started, so I did my undergrad in Singapore and actually second year of my college is when somebody mentioned the word technopreneurship. At the time that was a fancy term, which meant you're you're studying technology, but what if you would combine it with business and make sense of it? Which immediately stuck with me. I was like, all right, I want to explore this further. And so I took a six month program in college at the time too, which was a startup program where you would sort of go build your own startup. You're we actually this was in Singapore. where we came here, we went to Chile to understand how the startup scene looks like. Anyway, that's when I caught the startup bug. I was in Singapore for the next few years where I hit up joining Goldman Sachs for a bit. Very quickly realized it's not for me, taught me a ton, but also you know, I was itching to do something more, get up with the red tape. Yeah, yeah, yeah.

Brian Bell (01:23.012) Dodged a bullet there, dodged a bullet. Yeah. I had the same experience 20 years ago. I don't recall if if you recall that. But yeah, like I I worked really hard to get to Wall Street and then you know, I don't I think I was all gassed up by like the eighties and nineties, like Wall Street movies or whatever. And I grew poor and I wanted to be rich. And so I worked really hard to get to Wall Street and I was like, this is not it. This is not the way to do it. So not for me. Yeah.

Neha Mittal (01:35.756) Yeah.

Neha Mittal (01:40.802) Yeah.

Neha Mittal (01:47.246) That's right. Yeah. Yeah. It's funny 'cause it was like the most prestigious job out of college and everybody's like, Yeah. Yeah. Yeah. Yeah.

Brian Bell (01:52.915) yeah, it's Goldman. Yeah. Your parents are so proud and like your friends are like Goldman, wow, amazing. And then and then what was the reaction when you actually left? You're like, I'm gonna leave.

Neha Mittal (02:03.905) Yeah, no. So it was very interesting because in two to three years I was like so I was so on the edge. At some point one of my colleagues from college he reached out and he was like, We're starting a company, we're a month old, we're two people. Do you wanna join us? by the way, you'll just take like a sixty percent cut in your salary and

Brian Bell (02:26.978) Surprised they could even pay you, right? Yeah.

Neha Mittal (02:28.909) Yeah, yeah. And I'm like, that all sounds great. And so within a day,

Brian Bell (02:32.78) Yeah, that sounds fantastic.

Neha Mittal (02:36.213) I just decided to quit and my friends were like, What did you just do? And I was like, I know this is crazy. We'll we'll see what what this means. And then within a week everybody was like, You're glowing, this is for you. Do what you're doing. you know, and it felt like a difficult decision at the time because like, wait, my bank account looks different. And you're you're starting to think about like what does it mean in terms of where my life is going to be. But you know, in a few weeks I was like, actually this is way better. And all of that was in my head. You know, once you jump f off of that cliff, you're like, Life is actually a lot. Better. So anyway, that was sort of my first first, like you know, just getting a sense for what am I getting into in the startup world. but very quickly from there, I was like, actually, I want to do my own startup and I want to do it in education tech. I was very passionate about that space. explored Singapore, there wasn't that much going on in ed tech. also education tech now I know is a very, very hard space to crack. So that's when I moved here to Silicon Valley. I had a

Brian Bell (03:11.641) Yeah.

Neha Mittal (03:36.16) I had a master's offer from Berkeley and I was like, all right, perfect way for me to get into Berkeley, but also explore the startup scene. so I rejected that funny fun fact and because I wanted to do the startup stuff in Singapore. And then last minute I was like, actually, can you take me back? I'd like to come. So anyway, here I am 10 years later. but yeah, so started there and then went to John Zuckerberg, which is Facebook's education Yeah, yeah. And you know that it had the best of hey blessings from Facebook, but also all ex-founders from the ed tech world. So learned a lot there. but also very quickly realized a little bit more about myself where I love ed tech, but I don't have the patience to wait a generation to see what this would pan up to. I was like, actually, I'm somebody who likes to see results quickly, and so

Brian Bell (04:30.51) Yeah.

Neha Mittal (04:33.832) from there started another company called City Structure with my co-founder who I met in a tech grunge hackathon. and so that was very much she, you know, she called me, we grabbed a coffee in Hays Valley and then we do a hackathon together. She had 20 years of experience in real estate. I knew nothing about it, but she was looking for a product and tech leader. and so long story short, we're like, okay, we're a good good team, let's do it. so yeah we built city structure which was about how do you extend what's possible on your property. Can you add an ADU? Can you turn a room into a bathroom? All that good stuff. And can you do that by collecting drone data? So we were flying drones and we were

Brian Bell (05:19.097) Wow.

Neha Mittal (05:19.993) doing a lot of cool cool stuff. But yeah, did that. And then in the middle of all of this, my colleague at Twitter reached out because he was like, wait, GDPR is hitting us and we would like to understand how we're using users data. And we have very little of anything built out there. Not surprisingly. So it felt like a good zero to one role for me to explore. And so I ended up joining Twitter then. Built out that team from scratch. We were two, three people when I started and by the Time I moved on from that team, it was like 100-200 people. so that was good. And then as I was doing data stuff, the growth team reached out saying, Hey, we need a more data-driven approach to how we're doing growth. so that's how I went into the growth world within Twitter. that's where we started to see the roots of what just AI has become today. it started with we were seeing hey, we need our DAU numbers to go up every quarter, every year, but the way We're just making decisions on growth and marketing were very much white-based. Hey, somebody has an idea, let's go test it out, maybe run an A-B test, wait for results, and then we wouldn't see any. If we see some goodness great, let's double down, right? So there was no systematic way of thinking about it. and we'd started building a software to solve for that problem, which eventually started proving 15-20% movement in the DAU numbers. and so So anyway, we would joke about it. We would joke about, hey, we should put a company around this. This is a cool product. But everybody was very happy with their large salad bowls. And Twitter was a very, very happy place, as you probably know, until until Elon acquires Twitter. We reset the culture altogether.

Brian Bell (07:09.176) But you actually, I mean, this is this is interesting. You're kind of breezing by a lot of cool things. One is like you're at Twitter pre and post Elon, right? What was that transition like? I I do recall talking to you about that briefly when we met, but that must have been like a I don't know, like hunkering down for a tornado or something.

Neha Mittal (07:13.356) Yeah.

Neha Mittal (07:21.942) Yeah.

Neha Mittal (07:28.289) Yeah, I feel like we all went in so naive. Like we were so naive about the whole acquisition. We're like, it'll be fine, it's fine. You know, it happens all the time. Yeah, yeah.

Brian Bell (07:35.042) Yeah, Elon's a great operator and like it's gonna be fun. Yeah. He's gonna come in and revitalize the company and yeah.

Neha Mittal (07:41.942) And also like who can change the culture for comfy that is it's so hard, right? Even in the startup, I'm like, I want to change one thing, it takes time to get the team around it. But for what it's worth, he reset the culture within two days. it was a different company. so it was a lot, I think, a lot of introspection for me as well in terms of understanding how to sort of separate work and facts from emotion. Like Twitter very much were like, we're a family and everything is all good vibes.

Brian Bell (07:49.752) Yeah.

Brian Bell (07:56.025) Wow.

Neha Mittal (08:11.885) then you know it turned around into now we're a business, let's do business or go home, right? so I don't know, there's a lot I can talk all about for an hour, so I'll spare you that but but

Brian Bell (08:18.372) Right.

Brian Bell (08:24.118) Yeah, that could be an hour in itself. And what was the Berkeley connection? I I I went to Berkeley for for grad school, so I have to ask that.

Neha Mittal (08:30.741) yeah, so I did the information systems in Berkeley. and so yeah, and Kim who also did that program met me for breakfast or something at a you know random just to just a meet up and then that's when she was at Twitter at the time, so she was like, Hey, come join us and so started there.

Brian Bell (08:35.191) Okay, yeah.

Brian Bell (08:52.034) Yeah. Well I love that I love the story before we get to just AI, of this process of self discovery. And for anybody listening who's younger in their twenties and kind of that first career that you thought was it's not turning out the way you thought, don't be afraid to look at yourself in the mirror and kind of realize that and take a plunge towards something that's more interesting. and it's gonna be scary and you're gonna l you know, lose a lot of salary and but You know, what comes out on the other side is much better.

Neha Mittal (09:24.301) It is. And I think now that I don't know, I'm older, I guess. I realize that there's only so few people who are hungry and ambitious and you know, they they want they're curious. And if you're one of those people, you gotta go do it, right? Like if you're the you're the people who are gonna build big things and bring innovation, right? Like so I don't know. And the rest is all the rest doesn't matter as much as we think it does. All the like your bank account numbers and whatnot. so I don't know. Yeah, yeah.

Brian Bell (09:57.615) So tell us the origin story of just AI. You s it sounds like you you had the the embryonic idea at while at Twitter maybe?

Neha Mittal (10:07.605) Yeah, yeah. so Twitter we had a version of what We started with, which was hey, what if we like change copy in our push notifications and do it sort of iteratively and see what we'll get out of it? we didn't obviously have LLMs like we do today. So it started there, and that's how actually in January of 24. Well, there was a little bit after Twitter as well. So after Twitter ended up going to Pinterest, very similar projects and problems are around personalization of marketing messages. that's when I was Like, all right, I can see the patterns come together. It's this is clearly a pain point in large companies, not scaled enough for a non-technical user like a marketer or product manager to use this at scale. so and then because of the whole Elon effect, I guess a lot of my colleagues at Twitter had left and joined other companies too. So, you know, I was meeting up all of them, they're Yeah, we haven't solved this, that I don't know, this company or you're literally every company. And so it's like, all right, the problem. We're yet to find out what AI can do, but it sounds better than crypto at the time, right? And so and so that's when I was like, all right, we gotta like figure this out and take the plunge. And so January of 24 is when we started the company, joined by C. And started with the idea of, hey, the vision is every every company will inevitably need personalization in the AI world because AI's luck is gonna be real. When they do, they they should go through our through just GI models to make sense of it. So every single message will be personalized through our company. So yeah, funny enough, it's the same vision, right? That hasn't changed. right. but when we started, we started with a very small badge. We said, okay, we'll just do copy optimizations.

Brian Bell (11:50.82) Yeah.

Brian Bell (11:54.68) Yeah.

Neha Mittal (12:01.992) on your notifications and do it such that we apply reinforcement learning on one hand so you know what user should get what message but also over time feed it back into LLMs to predict to create a predictive loop for your users.

Brian Bell (12:15.662) Yeah, and paint it paint a picture for a lot of people who don't know about life cycle marketing. What is life cycle marketing? Why is it important? And what were the tools that existed? Because that's what we were geeking out about when we met, because you know, I was a Marketo certified expert 15 years ago, right? And, you know, we used to do all kinds of you know, list, you know, smart lists in in Marketo and and try to segment the audience in such a way that we would kind of fire off these rudimentary,

Neha Mittal (12:28.835) Right.

Brian Bell (12:44.91) targeted emails with just slightly different copy. And it was it was a lot of doing it by hand, frankly, you know. But yeah, to paint a picture for us why this is important.

Neha Mittal (12:54.562) Yeah, yeah. Yeah, yeah. okay, let's let me maybe talk through where life cycle comes in for our business and then let's talk through this tech that has evolved over the last few years. so life cycle it comes through when you're let's say you are Notion or any sort of like PLG company for that matter.

Brian Bell (13:06.445) Yeah.

Neha Mittal (13:16.446) and you're saying, look, I've got an inbound, I've got users coming into my platform, but once once they do, how do I now understand how to activate them? So how do I tell them what is Notion about? How do you use it and whatnot? then you start to think through, okay, they've created a page on Notion, but it's been two weeks, they haven't come back. can I introduce them to new tools? Can I re-engage them? Right? So that's re-engagement. and then you start to think through, all right, they've been using Notion for as a free user. It'd be nice if they pay us, of course, so we can grow our business. right and make them power users and that's where retention, monetization, upset and cross-sell things can start to come in. So in the large funnel that you're looking at, lifecycle comes in not at the top of funnel, but once you've got your user, how do you make them power?

Brian Bell (13:59.715) Yeah. You're trying to activate them basically. You've acquired them, you know, if you remember like Dave McClare's pirate metrics, are, you know. Yeah, what's the first one? It's like acquisition and then it's activation. and then it's what, retention or yeah, retention renewal revenue, something like that. Yeah. So you're trying to a life cycle comes in when you're trying to activate them, you're trying to renew them, cross sell, upsell, those kind of things. Yeah.

Neha Mittal (14:06.902) Yes.

Neha Mittal (14:11.523) Yes. retention. Yeah, yeah, exactly. Exactly. There you go.

Neha Mittal (14:24.12) That's right. That's right. Yeah. Yeah. And other thing about growth is it takes seven X more money and effort to acquire new user than to retain them. So that's why the retention loop is super critical for companies, right?

Brian Bell (14:38.318) This is this is a key thing. It's it costs seven to ten X more money to acquire a new user than just to keep the ones that you have in your database and activate them and cross sell them and renew them. That's huge. That's a big it's a big problem that almost everybody has, especially if you're PLG.

Neha Mittal (14:46.826) It's yeah, yeah, yeah, yeah. yeah.

Neha Mittal (14:57.26) Yeah. Yeah. definitely PLG and to be fair in other emotions as well, right? Like even us we have to start thinking about all right, we've got a X number of users, we wanna continue to educate them on everything we're shipping, right? And things like that. So yeah. and then so now how do you engage them? That's when some some of the channels come in where we talk about emails and push notifications if you have an app or SMS messages and so on. You'll know more than me on this, but I know I know we started with spreadsheets. We're still on spreadsheets. And you know, but we started with, yeah, we just want to send out an email. Here's a list of users and let's send out an email on our calendar based motion, right? Every couple of weeks or every week. And then you're like still

Brian Bell (15:34.423) wow. Impressive.

Neha Mittal (15:53.345) Salesforces of the world came in, right? It's like, okay, can we do something a little bit more interesting? Can we throw in user data and then start to figure out how we send those messages across user lists? at the time, personalization was very much hey, can we add first name to our message, right? then it evolved from there, where in the last decade, tools like Braze Intribal came through where they're like, we'll give you a nice WCWIG automation builder where you can.

Brian Bell (16:10.156) Right.

Neha Mittal (16:23.298) Build your journeys and sequences. Then personalization became a little bit more advanced where we started saying, actually, you can build your if-else logic in this workflow builder and say, Okay, if you're a free user, you'll get this message, but if you're a paid user, you get this other message. I think we as users are very good at exploiting these things. So, what we did was we said, All right, we're gonna create lots and lots of those if-else branches. and now what we call we we call that the octave. So now we create an octopus problem for us. We're like, God, we've got hundreds of campaigns. Each one is an octopus of if else logic. And we have no idea how to read this, what's actually performing and what's not. And now how do I go beyond this? I've created 10 FL branches, but I don't know how to do anything beyond that, right? And that's where it's really, really powerful to see AI come in. Because we're saying, wait, can I give you hundreds of user signals and can I tell you what my goal is? And can you just like run with it? right? and so that's sort of the next wave we're looking at. And we know it's coming, it's gonna be who does it, and I'm sure there'll be a lot of players in it. but that's the that's sort of the future of that's

Brian Bell (17:36.611) Yeah, so it's it's finally fulfilling this pro promise that we, you know, all dreamed of, you know, 15, 20 years ago of the right message to the right user at the right time, right? And the way we did it before is creating this octopus. And and now with AI, it it changes the game, where you can you can actually send that right message. So how do you guys really rein in and harness the AI to do that? Right. Because the AI can go off track, it can hallucinate. how do you make sure it's saying the right thing at the right time?

Neha Mittal (18:04.161) Yeah.

Brian Bell (18:06.286) To the right person.

Neha Mittal (18:07.266) Yeah, yeah. We had to build those sort of constraints up front, even like the very beginning, where we were saying, look, we're gonna talk we're gonna work with very, very large companies. how do we make sure their brand is in place? yeah, it's not going in the wild and doing things that it shouldn't. So the first thing we did was to separate out the motion of creating content that's visible to users from actually running any decision engine on it. So what that means is our users would first go and create, let's say, hundreds of variants of an email, they get a chance to review things, they get a chance to put in context into AI. And then once they review and approve it, that's when we say, all right, now AI can go wild with all of this content. Right. So that gives them a chance to sort of save their brand and reputation, right? At the same time autonomously run marketing in the background.

Brian Bell (19:02.936) How have you had any things you've had to do in the platform that were surprising to to make this work?

Neha Mittal (19:11.648) So many things every day. And that's the fun part of it. Well, early on, early on, I think the big things are around. AI is very robotic, right? Like it's it's not there yet. Lots of iterations on improving it. I remember once we were doing a Korean version of a message and it said something like hate Korean men, which if you translate it, and you know, there are things like that where like okay, we want to do localization.

Brian Bell (19:13.089) Yeah.

Neha Mittal (19:41.565) But we need so much context in there. the second piece came down to we want this whole like magical AI world, but actually, when we start using the tool, we care a lot more about visibility and explainability than the whole AI magic. So the fun things we are seeing in the last year or so is sometimes you show great numbers. It's all like green and you're seeing so much lift. but if you're not able to explain why those numbers are showing up. we're not happy. Like nobody's happy, right? on the other hand, even if numbers don't look great, but we have a very clear understanding of why that happened and how do I take the next action to improve it, that's a lot more powerful. which is not very well understood of because you know, p everybody's talking about, yeah, numbers number go up, life's great, but that's actually not true in how we we use tooling and platforms. anyway.

Brian Bell (20:37.186) Where you know, there's this phrase it's kind of funny, and I, you know, I've worked in marketing for a long time and ad tech and sales tech, part tech. You know, marketing ruins everything. You know, what d does, you know, and another another thing that's happening, I think, is, you know, automation and AI create new forms of scarcity.

Neha Mittal (20:48.91) Yeah.

Brian Bell (21:00.932) Where you think the the sc like what where is the scarcity in marketing these days now that you have AI that can you know do that one to one touch that we always dreamed of?

Neha Mittal (21:10.739) see you mean where are the things that we don't need effort in that we did in the past or

Brian Bell (21:16.196) Yeah, like we kinda like it sounds like we've kind of solved this problem with platforms like yours, right? Where we can actually personalize this at at scale. Where where's the scarcity now?

Neha Mittal (21:26.732) Yeah. Yeah. kind of goes back to your previous question on like so what were the surprises when I you know, right? So one of the things that we did was hey, AI can optimize at scale, throw in all your campaigns. We had a company running hundreds of literally hundreds of campaigns and thousands of variants of a message, and AI was like running at scale. and then they were like, Wait, but how do we manage this operationally? I mean, we don't have people to manage it. Also, nobody likes to be in the role of reviewing and managing each thing. That's not fun. Right. And we had to figure that out too. We're like, all right, now there's an operational challenge of scale that we didn't think about because until now we had one content writer for maybe like a whole marketing team. And it was only at a certain scale you could produce content and test it, right? Now we're saying we are shrinking four years worth of A B testing to one month. Who's going to manage it, right? So so some of that is that. But to your other question

Brian Bell (22:23.396) Mm-hmm.

Neha Mittal (22:28.224) What's very obvious is it's not that jobs are going away or whatnot, it's more that you're you're you're reskilling yourself, right? Like it's not even like you're upskilling, you're reskilling, like things that you were doing earlier, you're not gonna do now. so we're seeing a lot of that. It's like, hey, we still work with creative copywriters, right? But they're finessing AI, they're playing with AI more than anybody else right now, right? so yeah, that's what we're gonna see more of.

Brian Bell (22:50.958) Right.

Brian Bell (22:54.956) Yeah. What's the the hardest customer objection that you've had to overcome so far?

Neha Mittal (23:02.22) before they started working with us. all kinds.

Brian Bell (23:04.098) Yeah.

Neha Mittal (23:11.52) I think the I think one that we're still building towards and I think it's a very, very good ask, is I'm a very large company and I have hundreds of very, very, very hard requirements on how things should be customized. They are not deterministic. I cannot give you a list up front. they're not generalizable. That means you cannot just build a SAS software for me. figure it out, right? Like that. So that's and just to put that in like a real example, they might say, look, I need to make sure that I want to send out an onboarding journey, right? So I I want to send out maybe seven emails in the next two weeks. But I need to make sure that whichever email you pick, the the subject line for the first email has to be welcome to, I don't know, a notion, welcome to Corsair. But by the way, if you're targeting a user in San Francisco, Always make sure you give them this discount. And because our product works a certain way in Singapore, make sure that you know you talk about our product a certain way. So there's hundreds of those custom requirements that literally every company has. And nobody would have a bizarre ask like that pre-AI. But now they can. Now it's like we can can AI make us builders? And if it can, can I produce all that custom logic? So that's the one that we've heard enough objections on and I think and like in a good way, right? It's like, all right, let's build towards it. Yeah.

Brian Bell (24:50.532) How do you balance the I don't know the power of the platform versus kind of you know safeguarding the ability to to use it incorrectly or or to fail at it? Like how much power do you give to the users versus how much do you kind of retain in the platform as guardrails?

Neha Mittal (25:10.23) Yeah, yeah. that's actually a really good question because that's where a lot of like product opinions come in. Like, hey, how are we making the product very opinionated rather than do anything and everything or do very deterministic things?

Brian Bell (25:22.424) Right. Well, like Marketo, I I would say back in the day, fifteen years ago, was at the extreme. Like it didn't h hold your hand at all. And you could just I remember one time I ran an infinite loop. I didn't even know what I was doing, you know, but you know, I had a bunch of like smart campaigns and smart list running in a loop. And Marketo reached out and they're like, Hey, you have like millions of events here. Is that intentional? You know, because that's like an that's three orders of magnitude more than you've had in the last thirty days.

Neha Mittal (25:35.8) Yeah.

Neha Mittal (25:40.76) Yeah.

Brian Bell (25:51.532) Right, ooh, yeah, that was not intentional to run millions of events in in the platform.

Neha Mittal (25:56.237) Yeah, yeah. No, for you'd be surprised. That still happens. So once in a while we'd be like, wait, did you mean to do this? we're trying to just like optimize your setup, but we see you're like sending out 20 emails in a day, and they're like, wait, what? so anyway, but to your to your question, there's a ton of godrails we put in with regards to at least two different things. One is again around anything that's visible to your end users, we want to make sure that you've eyeballed everything. Thing. So that still exists. The second one is around data and measurement. So end of the day, we're still a very technical data heavy tool. And we give a lot of guidance on hey, should you be running like 20 variants at a time? If you have a hundred users coming every week, probably not, right? Like so things like that. And then what kind of metrics should you be worrying about? Like if if you're converting. Conversions are maybe once a one conversion a week, not a good idea to run run optimizations on that metric. So things things like that.

Brian Bell (27:06.296) What what does the the marketing team look like now that AI is sort of, you know, infiltrating the department? I mean, I haven't run any marketing teams in, you know, twelve, thirteen years now. And I built mark marketing tools for years, but but yeah, well, how is it changing how marketing is run on the ground?

Neha Mittal (27:25.346) Yeah, there's two very interesting changes I'm seeing. This is from the last three months, so it's gonna you'll probably hear a different answer in three months. the first one is lifecycle teams specifically, so if teams focused on retention and activation, they're moving closer to the product teams. Like literally some marketers are knock-out product managers or they're sort of rolling into product. and that's only to say, hey, like it makes sense, we're automating more workflows, right, within the company. let's product product experiences is what should drive marketing and vice versa, right? So that's that's one in an interesting like change in organizational setups that I'm seeing. The second one is we're seeing more and more where people are following a genderless model where they're like, wait, can we have one marketer who can take more scope now? And can they use your tool to run all of lifecycle marketing, even though that's only let's say 20% of their job. and that's very close to the vision that we, you know, we we had a year ago where it's like, as a one or two people team, you should be able to run all of your life cycle marketing with this tool and then show clear movement on revenue.

Brian Bell (28:37.698) Yeah, I remember running lifecycle marketing at a thirty million dollar startup ri in revenue. You know, kind of, you know, mid stage kind of startup. And we we I must have had a team of six or seven people working on lifecycle in various forms. A couple of content writers, a couple of marketing managers, you know, a designer, you know, this cross functional team. Is that sort of is that dream fulfilled or is that sort of still kind of more like a vision where, you know,

Neha Mittal (28:51.254) Well yeah. Yeah. Yeah. Yeah.

Brian Bell (29:07.424) I'm a I'm a founder and I'm hiring my first marketing hire. Can they plug into just AI and and really make this thing scale a little bit before I have to really scale out the team to like ten people?

Neha Mittal (29:17.824) Absolutely, yeah. And literally everybody who's starting it from scratch or ready to sort of rebuild it, that's exactly how they're thinking through it. There's still some companies that are following sort of the more traditional approach of like all different departments, but that's the long tail, right? Like you when you have a very complex setup, it's gonna do it. Yeah. Yeah.

Brian Bell (29:33.452) Yeah. And and does it when you onboard adjust AI, are there any tools that you're kind of taking out or is it kind of building on top of the tools that are already there?

Neha Mittal (29:45.079) Yeah, no, we talked about taking out both tools and work, right? So work is basically how many people do you need to run marketing and then a bunch of different tools. so it's interesting, there's sort of three levels in which we think about. The first one is hey, what is the exact tooling we can totally like remove? Like for example, of course you don't need to like I don't know, you don't need to have a copy tool to write your variance. you don't need to have a separate Experimentation tool, we'll redo that in our tool, things like that. the second level is where we say there's going to be an overlap or of functionality. So, yes, we're running decision engines, you probably still have a marketer-like tool to run your like simple marketing rules, and there's going to be a little bit of an overlap until you take that out. and the third one is where we're saying we we're gonna give you functionality where you would reduce seats on other tools, even if you don't take them. So for example, these are your measurement tools like Amplitude and Mix Panel and so on. So you can probably see all of those data analytics and stuff in our tool. those are company wide tools though, probably a different buyer, right? All that good stuff. You probably will still use them, but does the marketing team need it? Not really. So marketing experience becomes really consolidated.

Brian Bell (31:07.694) So if just AI fully works, what does life cycle marketing look like in five or ten years?

Neha Mittal (31:13.942) Yeah. The first thing is it's going to be a rev a very clear revenue generating orgs. It's not gonna be, hey, we just like run your emails and campaigns. It's gonna be no, we contribute fifteen percent to your top line revenue. so that's one. and the second one is what what you pointed out, it should be either one person or half a person running running your lifecycle marketing.

Brian Bell (31:39.481) So it's hard to it's hard to put a number on things, but let's take two cases. One case is a pretty sophisticated team that does like lifecycle marketing. What kind of lift can they expect? I know it's hard to speak in generalities versus maybe a startup who is just di getting into lifecycle marketing. What's the lift to each of those kind of target audio users?

Neha Mittal (31:59.683) Yeah. Yeah. I have some numbers from what we've seen in the past a couple of years.

Brian Bell (32:06.329) Yeah.

Neha Mittal (32:07.116) Generally, lifecycle contributes roughly 10 to 15% to top line. we're able to show a a dent of another 15 to 20 percent on top of it. So that means we're seeing a three to four percent lift on the top line with all of this tooling. right? And so that's just the revenue lift itself. We're not talking about efficiencies, but in terms of efficiencies, they're able to, for example, the kind of things we've heard from our customers is hey, we've taken down three job postings, because now we can do this with your tool, right? So we just nice and then the other pieces hey we can of of course run lots of optimizations in a small amount of time

Brian Bell (32:44.12) Yeah. So you're talking on the low end, you know, twenty five percent, twenty, twenty five percent, and it could be as high as, you know, a fifty percent lift to your revenue, which is a significant lift.

Neha Mittal (32:56.29) Yeah. Yeah. And that's just the tip of the iceberg, right? Right? Because this is we've we've only targeted so few channels. We're only getting started with all kinds of optimizations we can do. So I don't know. There's a lot there.

Brian Bell (33:09.09) Yeah, anything else you'd like to say to to any potential customers listening out there? anything else they should know?

Neha Mittal (33:16.212) if you haven't tried AI tools, you gotta get there. And by AI tools I don't just mean your of course clubs and open AIs. the the beauty of trying AI tools, and I do this in our company too, is you learn a lot about use cases that you wouldn't have thought about. and so that is a big, big learning opportunity that is so critical if you wanna be at the cutting edge of if you wanna survive as a company, honestly. Yeah, yeah.

Brian Bell (33:41.625) Yeah. Yeah, it's it's it's another way I hear people say it in the industry is AI won't replace you, but the person using AI will. And so try to use it in everything that you do. And even if it feels slower at first, just try to use it every and sometimes I'm just like, I could have just done that manually much faster. But over time I get faster at using AI and then it does make me two to three times faster. but at first it can feel kind of slower.

Neha Mittal (33:52.492) Yeah, yeah.

Neha Mittal (34:08.46) Yeah, that's exactly. That's I think that's everybody's experience. And so don't also don't get frustrated when yeah it doesn't work the first or second time. That's also a learning moment for you to know what where it doesn't work. And that's okay. but yeah.

Brian Bell (34:24.452) We'll also wrap up with some some rapid fire questions. what's something you believed with total conviction five years ago that you no longer do?

Neha Mittal (34:33.196) Hmm,

Neha Mittal (34:39.574) I didn't think I didn't think I'm going in the AI world but AI is gonna move at the pace it is and that ninety five five percent of the code will be truly written by AI. So so yeah, yeah, I think I was underestimating how quickly.

Brian Bell (34:54.308) Yeah.

Brian Bell (34:58.956) Yeah. And I think people misunderstand AI because they haven't really thought about I mean, we were just getting used to Moore's Law, right? Computers were getting two X better every year. And we were just getting used to that. and now AI gets like an order of magnitude better every year, which is 10X. and we're just not used to that pace of change. I think if you're like a singulitarian like I am, you know, with the singularity is near, you know, tw 20 years ago now.

Neha Mittal (35:17.314) Yeah.

Neha Mittal (35:20.963) Yeah.

Brian Bell (35:27.32) I've been I've been you know watching this ramp of and I've been working at AI for 15 years. So I've been kind of watching this ramp happen and it's not surprising at all. And it I can see the day three to five, seven years in the future, where there are just solo founders who run everything with AI. You know, and you're literally just talking to your CMO agent and your C CPO agent and your CTO agent. And and you're just managing this like swarm of agents that manages another underlying swarm of agents. Which sounds just completely sci-fi. yeah.

Neha Mittal (35:56.888) Yeah.

Neha Mittal (36:02.2) Do you believe that? And then what's your timeline again for it?

Brian Bell (36:07.374) I would say on fast would be the next three years and slow would be ten years. So somewhere in there, three to five years.

Neha Mittal (36:14.904) Yeah, no. I I think like yeah, the next generations will have a very different experience compared to what we're doing. Yeah, yeah.

Brian Bell (36:20.994) Yeah. Yeah. What's a what's a popular idea in growth or life cycle marketing that you think is quietly wrong?

Neha Mittal (36:31.438) the idea this is weird that I'm saying this, but the I the idea of one-to-one personalization is not well understood. It's not wrong, but it's what people understand is not rooted in how technology works sometimes and that needs to be corrected.

Brian Bell (36:52.192) maybe pull on that thread a little bit. What do you mean?

Neha Mittal (36:55.404) Yeah, so people assume one to one personalization today is gonna be given a user, I'm gonna figure out the best message, right? Like that's that's sort of their understanding. they they assume sometimes it's a singular motion where it's like, given Neha is coming to, I don't know, my website, Neha, I know what to send. But actually, you wouldn't know what Neha wants until you look at your entire population, right? You gotta get context on what people like Neha are thinking to know what Neha want to see. if you just look at Neha's trajectory in your in your platform, you're not gonna be able to understand. she means. So it's actually the the way to get to one-to-one personalization is by looking at your entire population over and over. I don't know. It's a very nuanced thing, but a lot of people don't like get the depth of what it means to get there.

Brian Bell (37:46.872) What advice do people constantly give founders and AI builders that you think is wrong?

Neha Mittal (37:52.194) what advice that sorry I missed that. Can you repeat that question?

Brian Bell (37:56.173) Yeah, what advice do people constantly give founders and people building AI that you think is wrong?

Neha Mittal (38:03.166) good one. let's see. I mean the obvious ones are just like fundraising. Actually I'll give you maybe on fundraising, I'll give you two perspectives there. so I think Vice's advice on race small early is spot on. we got so much goodness from having less money up front and proving out our our PNF. and so, you know, just like using s funding milestones as a way to like track success probably very off. money has a way to get spent, which is a problem in different ways.

Brian Bell (38:42.232) Yeah, and you can buy your a fake semblance of product market fit with a lot of money, right? If you raise a two million seed, you really gotta be careful and get to, you know, product market fit. But if you raise a ten million seed, you're like I can just like throw some money at this thing and and make it look like product market fit.

Neha Mittal (38:51.757) Yeah.

Neha Mittal (38:55.534) Yeah. And it's not yeah, and if you think about execution, you're thinking, well, how am I gonna deploy this money? let's hire a team. Okay, nothing wrong, but now you've got a ten people team, maybe more, and now you're figuring out communication gaps. Everything moves slower. It already for us as we scale our team. I was literally talking to my co-founder, I was like, we used to build much faster two years ago as a two people team would happen, right? So it's all of those things when you've not figured out where you're going and you have a large team, good luck creating focus and clarity.

Brian Bell (39:19.118) Right.

Brian Bell (39:27.352) Right, right.

Neha Mittal (39:27.472) So, anyway, so that's that. but there's a flip side to it too, which we learned as we sort of did our journey after VyC, where it's probably not a good idea to wait too long to like hire and scale your team. So VyC was very like against don't don't hire too quickly, right? To a fault. And I think we struggled with the other side of it where AI is going so quickly that we don't have the luxury of, you know, like waiting and hiring. And I wish we had done. Done that better in terms of sort of getting a gut check on, like, okay, when should we scale and how quickly should we start scaling.

Brian Bell (39:58.573) Mm.

Brian Bell (40:06.945) what is a failure you're grateful for now but you hated at the time?

Neha Mittal (40:12.566) These are good questions.

Brian Bell (40:16.622) So my not my first podcast.

Neha Mittal (40:18.478) I think our first few customer terms were extremely painful. partly because we built relationships, we're given it all, right? It's your first customer, your second customer, you you're putting in your life there and And when when they didn't work out, I call it the NDEs, we've we have it we've had a bunch of near-death experiences. It felt like, all right, my god, this is the worst and what are we doing? What what are we doing wrong here, right? but if they didn't happen, we wouldn't be here, right? Like 'Cause then there's you're always telling yourself, Okay, life's okay, but you know it's not. so I almost appreciate those big downfalls more than like sitting in a silent store. Like you know. so yeah, I I think those are the big ones. Like that that's one and yeah, I mean like I also I don't know if I shared this with you, but during YC I had a co founder breakup and that was a lot because I was running the ship so for a second time. in hindsight that I think that taught me a lot.

Brian Bell (41:26.638) Wow.

Brian Bell (41:31.95) Yeah, what what was that like? I mean, yeah, that's gotta be tough, right? because that's your other half. It's like a almost like a the divorce. They should it should call it founder divorces, basically. That's yeah.

Neha Mittal (41:39.788) Yep. Yep. Yeah, I sometimes I feel it's worse than that. but but yeah, no. And it was a lot like it was it came down to listening to your gut, decision making when nothing makes sense, knowing what you want, all the good stuff, right? But also just like I've I had struggled with believing in myself by by and just like running with it. After that experience, it was like I don't have an option. I left everything else, I'm gonna do this. And it was a realisation that I can do it actually. It's not you know

Brian Bell (42:12.3) Right. Yeah, the the bur the the the boats were burning in the harbor and you can't get back on those boats, right? So better make this better make this work out.

Neha Mittal (42:12.962) Yeah, yeah. And so that was

Neha Mittal (42:20.448) Yeah, exactly. Yeah. And then and by the way Yeah, yeah. And the everybody else is gonna be like the optics look bad, na na na not today, of course, for the right reasons, right? And you gotta also like shut down all of that and just focus on and the the right things, like build a good product, get your first customers, all that good stuff. Yeah.

Brian Bell (42:40.854) Yeah. What are you excited about over the you know, the coming years?

Neha Mittal (42:47.054) I feel extremely grateful and actually very privileged to be in the heart of San Francisco and building an AI company at the right time. I feel like five years later people are gonna look at us and be like, you were part of that, that era. Yeah, yeah. So yeah, yeah. And I felt that exactly, yeah, yeah, yeah. So

Brian Bell (43:02.69) Yeah. That new wave, yeah. Yeah, right place, right time. Yeah. Like building a mobile app in two thousand nine, you know? Yeah. Absolutely.

Neha Mittal (43:12.588) Yeah, I tell that to my team too. I'm like if you can do it, if you have the luxury too, because I know not everybody has the privilege to be here and do this the way we are, you gotta do it. Yeah. Yeah, come with it with us by the way. We should hire you. Yeah.

Brian Bell (43:20.43) Yeah. Yeah. But anybody can come. We're very welcoming here in California. Come and come and build something. Yeah. What do you want to be remembered for?

Neha Mittal (43:36.282) I wanna I wanna I talk to my team about it too. but I wanna I wanna leave a legacy behind. If I get run over by a bus ride at some point, I would like the company to stay and do great things. and that's very exciting to me. And it doesn't mean that it has to be a Facebook or whatnot. It could be serving a village of 100 people, but if it's serving them right, I think that's very powerful to me.

Brian Bell (44:05.368) Yeah. Well, really enjoyed the conversation. it's always good when I sit down with one of my founders and I'm glad I made the investment. so yeah, very happy to be on the journey with you. Where can folks find you online?

Neha Mittal (44:17.97) yeah, well you can find me on LinkedIn. I'm there quite a lot. but also if you go to our website, getjust.ai and hit the the first CTA that you see, it'll take you directly to my calendar. it's getjust.ai. We tried, we tried.

Brian Bell (44:31.736) You got just dot AI? Get just. I like, wow, you have just a that's hard to get the you know, the four letter domains, you know. But yeah, get just dot AI. Thanks so much, Nayha. I really really appreciate it.

Neha Mittal (44:46.735) Awesome. Thanks, Brian. Thanks for having me.

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