Building Real-Time Data Streaming for AI | Jacqueline Cheong, Co-founder and CEO of Artie

Summary

Podcast Summary: The Peel with Turner Novak

Episode: Building Real-Time Data Streaming for AI

Guest: Jacqueline Chong, Co-founder & CEO of Arty
Date: January 28, 2026
Host: Turner Novak

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Overview: The Importance of Real-Time Data in the AI Era

In this episode, Turner Novak sits down with Jacqueline Chong, co-founder and CEO of Arty, for an in-depth exploration of challenges and breakthroughs in real-time data streaming. The discussion highlights how modern enterprises increasingly require instantaneous data movement to power AI and analytical workloads, why building robust data streaming infrastructure is much harder than it looks, and how Arty's approach differs from competitors. Jacqueline also shares insights from Arty’s early days, their unique sales playbook, experience with YC and Standard Capital, and practical advice for founders.

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Key Discussion Points & Insights

1. What is Arty and Why Real-Time Data is Hard

• Arty’s Mission: Arty offers a real-time data streaming platform that lets companies transfer data seamlessly between operational and analytical data stores (e.g., Postgres to Snowflake). [04:09]
• Data Fragmentation: Organizations typically operate multiple databases—transactional for operations and analytical for reporting, machine learning, and dashboards. Data needs to be moved, joined, and kept up-to-date for meaningful analysis. [04:32-05:12]
• Technical Hurdles: Transferring data is far from “copy-paste” due to mismatched data types, schema evolution, data ordering, and the risk of data loss or corruption, especially at scale (billions of rows monthly). [05:33-07:36]
  • Quote: "You have all your transactional data that lands in your operational database, but maybe you want to join it with other data, you want to analyze it, run machine learning models on it, build customer facing analytical products..." – Jacqueline [04:32]

2. The Evolution of Data Streaming & Real-Time Demand

• Historical Context: Data transfers were once manual, infrequent, and limited by technical constraints ("once a month on Teradata with a floppy disk" [11:12]).
• Move to Real-Time: Cloud data warehouses (e.g., Redshift, Snowflake) prompted more frequent—but still delayed—data flows. The rise of AI and "agentic systems" (autonomous software) necessitates live, real-time data to make correct, context-aware decisions. [13:35-14:02]
  • Quote: "If you want it to be pushed beyond a demo or a pilot… for it to be running mostly autonomously, it needs to have live context into everything that happened as quickly as possible." – Jacqueline [13:44]

3. Why Real-Time Data Streaming Is Rare and Difficult

• Prevailing Challenges: Even major tech companies (Netflix, DoorDash, Instacart) spend years and substantial engineering resources building, maintaining, and debugging in-house streaming pipelines. [15:45]
• Low Success Rate: Research (cited by Confluent) shows fewer than 5% of real-time streaming projects succeed. Productionizing at scale introduces endless “edge cases”—data drops, ordering issues, schema drift, sharding, failure recovery, and more. [17:13]
  • Quote: "You make a plan... and it might take you a month or two to get a demo... then you try to go to production and it's a whole different animal." – Jacqueline [17:29]

4. Why Customers Switch to Arty

• Never-Ending Maintenance: Even after building in-house, companies face mounting technical debt due to continuous edge cases, growing data scale, and ever-changing requirements. It becomes a distraction from core product development. [19:00]
  • Quote: "The build is not the end of the project. You have to actively maintain it... and your use cases will change. And... as your data scales and increases by 10x, the same system that worked may not work anymore because scale will also break the system." – Jacqueline [19:00]

5. Trusting a Startup With Mission-Critical Data

• Initial Skepticism: Customers are naturally hesitant to trust foundational data infrastructure to a startup, especially when mission-critical workflows depend on real-time data. [21:25]
• Arty’s Edge: With exposure to more edge cases than in-house teams—thanks to serving diverse customers—Arty’s software is more robust and adaptive. [21:51]

6. Competitive Market and Differentiation

• Perceived Crowding: Though the data tools space appears crowded, most existing solutions offer "raw infrastructure" (the cake ingredients), not turnkey, production-grade outcomes (the cake itself). [23:07-25:36]
  • Quote: "The analogy I like to use is... you wanted a wedding cake and what exists in the streaming market is... different types of flour... And then... do you have a baker? ...our whole thing is... here's the cake... Just go and eat it." – Jacqueline [24:33-26:08]

7. Product Decisions and Go-To-Market Insights

• Relentless Focus: Initially, Arty focused solely on databases as a data source to perfect quality/reliability before expanding into events and other integrations. [26:34]
• Saying No: Chose not to chase early customer requests for features outside this focus, despite short-term temptation. [28:15]
• Product That “Just Works”: Overcoming skepticism through performance—engineers attempted to "break" Arty with difficult data and were surprised by the robustness. [29:24]
  • Quote: "I always tell people all those things that broke from before. Just give us your hardest tables and your most problematic data. Try to break arty with it." – Jacqueline [30:26]

8. Early Customer Acquisition and Sales Playbook

• Outbound-First Approach: Unable to leverage the traditional YC playbook (selling to batch-mates, who often lacked data at scale), Arty won its first customers through personalized cold emails—Substack was the first major win. [30:50-32:29]
• “Implementation by Google Sheet”: Early onboarding was manually tracked in spreadsheets before a UI/dashboard existed, with close collaboration improving both product and speed for later customers. [33:02-34:12]
• Hands-On Enterprise Support: For compliance-sensitive customers (e.g., fintechs), Arty engineers worked on-site to navigate complex security, networking, and BYOC (Bring Your Own Cloud) deployments—then operationalized/automated the process. [36:24-39:02]

9. Automating Revenue and Growth Operations

• No BDRs: Automated "go-to-market pipeline" using tools like Clay and internally built solutions to prospect, research, and follow-up—eliminating the need for an outbound sales team. [44:14-45:07]
• Growth Reports to CTO: Treating business operations like an engineering problem, optimizing and automating sales/ops processes, gave Arty a unique efficiency edge. [40:08-43:53]
  • Quote: "It’s a superpower if you treat it as an engineering problem... having someone who’s naturally obsessed about optimizing something run business operations has been the biggest hack for us." – Jacqueline [41:22-43:53]

10. Personal Sales Lessons and Building Trust With Technical Buyers

• Discovery Over Demo: The first call is all about deeply understanding the customer's pain and goals—never jumping straight to features or a demo. This builds trust and uncovers deeper customer needs. [63:23-65:09]
  • Quote: "The rule of thumb is... you should always find out something new about your customer." – Jacqueline [66:18]
• Consultative Selling: Providing customer insights—even beyond Arty’s product scope—positions Arty as a trusted advisor. [68:01-69:51]

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Notable Quotes & Memorable Moments

• [14:02] Turner: “To build good AI products, you need instant data being collected and used.”
• [17:13] Jacqueline: “Research shows real-time streaming projects have under a 5% success rate.”
• [25:36] Jacqueline: “Companies want the outcome that a streaming system provides… they don’t actually care about the infrastructure. Here’s the cake, just eat it.”
• [29:24] Jacqueline: "I'm very skeptical because I've tried, like, tool 1, 2, and 3... I've been burned by all of them."
• [32:40] Jacqueline: "Our first customer, Substack, wanted to stream a couple billion rows... terrifying. But that rigor really helped us make our product better."
• [38:50] Jacqueline: “We just go, figure out the minimum networking permissions, tell you exactly what we need, and be next to you. Instead of dragging out for a month, we make it happen in days.”
• [41:22] Jacqueline: "It’s a superpower if you treat [business ops] as an engineering problem."
• [50:29] Jacqueline: "AWS has great support and they’re massive... if you intentionally choose to prioritize this, you can make it happen."
• [63:23] Jacqueline: “Mostly what I learned was how to sell to a technical audience that doesn’t like to be sold to.”
• [83:24] Jacqueline: (On her favorite book) “If you just focus on all the inputs that you can control… you don’t have to care about the outcome; the outcome will just happen.”

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Important Segments & Timestamps

• [04:09] – What is Arty? Intro to real-time data streaming
• [10:40-13:44] – Evolution of warehousing; why real-time matters now in the context of AI
• [15:45-17:23] – Why building this infrastructure is so hard & why companies fail
• [19:00-20:49] – The pain of in-house pipelines and maintenance burden
• [23:07-26:26] – Market differentiation: “cake” analogy and product focus
• [30:50-34:12] – How Arty got its first customers through cold outbound, initial product hacks
• [39:02-43:53] – Automating sales ops, biz ops reporting to CTO
• [44:14-45:53] – Using engineering for go-to-market, eliminating BDRs/SDRs
• [53:05-54:04] – Co-founder Robin’s experience and the kernel for Arty
• [59:33-63:10] – YC application, batch experience, and sales learning journey
• [66:18-69:51] – Enterprise discovery and consultative sales deep dive
• [70:25-78:59] – Arty’s Series A fundraise with Standard Capital; comparison to traditional VC
• [83:11-85:49] – Favorite business book: The Score Takes Care of Itself by Bill Walsh and applying high-performance mindsets

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Structure & Tone

The episode is conversational, open, and actionably detailed, with Jacqueline providing candid insights about technical tradeoffs, founder challenges, hard-won sales lessons, and her standards for product reliability and customer experience. The tone is simultaneously technical and practical, providing guidance both for operators and for founders looking to build and sell high-stakes software in AI-centric environments.

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Conclusion

This wide-ranging episode offers a rare window into the actual complexity of building robust real-time data streaming solutions, why such problems persist despite market saturation, and how focus, relentless customer empathy, and automation-driven execution have been key for Arty. Turner and Jacqueline’s discussion is packed with tactical advice for founders, along with a refreshingly honest assessment of startup highs and lows—from pen-and-paper "dashboards" to nailing enterprise contracts and surviving the gauntlet of modern fundraising.

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Transcript

[Turner Novak]

Welcome to the Peel.

I'm your host Turner Novak, founder of Banana Capital. Today's guest is Jacqueline Chong, co founder

and CEO of Arty.

Arty moves data across your systems in real time. We talk about why that's so important in the age of AI. It's a lot harder than you'd think as less than 5% of real time data streaming projects are actually successful. I talked to a dozen people to prepare for this conversation, including Jared Friedman, who worked with Jacqueline during yc, her sales coach Roz from numerous Arty employees. Like any Rudd, Ryan, Sarah, Sheng Bing and Jacqueline's co founder Robin. We talked through how Robin built real time data streaming at Opendoor and Zendesk before they started Arty and Jacqueline shares the sales playbook she learned going from hedge fund analyst to software CEO, including asking customers for their hardest problem and then solving it with the product. Arty just announced a $12 million Series A A few weeks before we published this episode. Talk about how they landed all of their early enterprise customers through Cold Outbound, how they structure and automate their prospecting with AI so that they have no BDRs, why the growth team reports to the CTO, and why they're seeing customers switch to RD even after building their own multimillion dollar real time data streaming projects in house. I also asked Jacqueline what it's like working with Standard Capital. They're a new fund started by a group of YC partners and it was fascinating to hear about all the things they borrowed from YC when investing at Series A Stage. A Full Disclosure I am an investor in RD and nothing in this episode of conversation is investment advice. A reminder that I publish two episodes

of the Peel every week exploring the

world's greatest startup stories just like this one. Check out the back catalog of over 100 episodes, including recent conversations with Nathan Benesh, author of the State of AI and founder of airstreet Capital, Marcelo Lebre, co founder of European Unicorn Remote and Kevin Hartz, co founder of Eventbrite and seen investor in PayPal. Tune in over the next few weeks for guests like Gary Tan at yc, Jason Putagunta at Benchmark, Jake Stouch at servl, Mike and Akil at Footwork and Duo Security co founders Doug Song and John Oberheide. Let's talk to Jacqueline after a quick word from numerl and Flex. This episode is brought to you by numerl. Numerl is the fastest, easiest way to stay compliant with US Sales tax and global vat. It's easy to set up and they automatically handle all registrations, ongoing filings, and their API provides sales tax rates wherever you need them with all the integrations you need. Numero sports over 2,000 customers in both

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Jacqueline, welcome to the show.

[Jacqueline Chong]

Yeah, thanks for having me.

[Turner Novak]

Real quick, for people who don't know, on your shirt it says arty, but what is arty?

[Jacqueline Chong]

Yeah. So Arty is a real time data streaming platform. So in a nutshell, what we do is we help companies move data across their systems in real time. And a very simple example is like moving data from postgres to Snowflake.

[Turner Novak]

And why is that a big deal? Like that seems like not something that seems like that consequential. Why is it so such a big deal?

[Jacqueline Chong]

Yeah, so I mean, companies have different data stores. There's like operational data stores and then there's analytical data stores and they were built for very different use cases. So you have all your transactional data that lands in your operational database, but maybe you want to join it with other data, you want to analyze it, run machine learning models on it, build customer facing analytical products and query that data that belongs in a analytical data store. So how do you get data into the analytical data store? That's the problem that we solve.

[Turner Novak]

Can't you just switch up how the data is stored initially? And that's again it seems like maybe, like why is this such a big deal?

[Jacqueline Chong]

Like no one's built this or it feels like a deceptively simple thing. Yeah, like can I just copy and paste files into. Yeah, it seems like it.

[Turner Novak]

It's just data. Copy and paste it.

[Jacqueline Chong]

Yeah. I think in like why it's so hard is fundamentally you're moving data between like heterogeneous systems. So a postgres database is built differently than a Snowflake.

[Turner Novak]

So you cannot just copy and paste it?

[Jacqueline Chong]

No, no, just the different data types that they allow. There are restrictions on like character length. And then so when you're moving that data, there's like very small conversions that actually have to happen such that you can land that data into Snowflake and have it still be usable. So there's a lot of like complications. And then as you build this system out, it's how do I make sure the data that's copied over is accurate? How do I make sure there's no missing data that was dropped?

[Turner Novak]

How do you get missing data? It's just data that gets dropped in a copy paste or maybe.

[Jacqueline Chong]

Yeah, you skipped it. You skipped it because it's not a data type that Snowflake allows. You don't know what to do with it and the system drops it or you didn't pick it up properly or it was written out of order. And then as you scale it out in production, the edge cases continue to build on each other.

[Turner Novak]

So how big is this system that someone might be using? Is there like 100 rows in a spreadsheet? And that's like how big?

[Jacqueline Chong]

I mean transactional systems can be, can be like tens and hundreds of terabytes, can be like petabytes big. And when we talk about the data that's moving, we're only moving the data that has changed. So it's not the entire database or the entire tables. But we of course do an original backfill. But afterwards we're just taking the data that has changed. And even then we're talking about like a billion, multi billion rows every single month. That's not uncommon for transactional systems.

[Turner Novak]

Oh wow.

[Jacqueline Chong]

Like think about every time you call an Uber ride, every time you check into a hotel, every time you buy a flight, even when you open an

[Turner Novak]

app, isn't that getting logged?

[Jacqueline Chong]

Yep, everything is getting logged. Every email you send, every message you send, everything is logged. And so think about how big these transactional systems can be.

[Turner Novak]

So if I'm Uber, I probably have how many different databases and how many different rows might I have if I'm that scale?

[Jacqueline Chong]

Yeah. Imagine Uber's like rides table.

[Turner Novak]

Okay.

[Jacqueline Chong]

Across like the US globally, the rides table. Like each, each ride that exists in its system, that's the rides table. That must be massive.

[Turner Novak]

And then would they have a different, like Uber eats table?

[Jacqueline Chong]

Yeah.

[Turner Novak]

Okay. There's like a different database. Would they have like, is it all rides globally in one spreadsheet or in one database?

[Jacqueline Chong]

Or is it like depends how they architected it. Yeah. It can be split up. As your data gets bigger and bigger. You, you know, some people start sharding their databases so rides can be sharded into every single month. Per region is a different shard. And then you can see how this gets even more complicated when you're talking about streaming that data into a different store for a different use case, let alone in real time.

[Turner Novak]

So there may be some cases where you have hundreds of different databases that are all dumping into your central data warehouse.

[Jacqueline Chong]

Yeah.

[Turner Novak]

Do people ever have more than one data warehouse?

[Jacqueline Chong]

Yes. It is not that common because by default it's supposed to be your 1 centralized data store, but you might have snowflake for running some AI or ML models. Customer facing, maybe not customer facing dashboard, Maybe it's like AI ML models, your BI reports, your ops team can query that data marketing sales will run off of that data. And then you might also use a clickhouse for observability and logs and hosting your customer facing analytical dashboard. So even within warehouses, there can be different warehouses for different use cases that they're better fit for. We also see customers like they might have a snowflake for their more like BI reporting system. And then they also have a databricks because their data science team is running ML models there.

[Turner Novak]

So how did this market kind of evolve to the point of you have all these different types of databases and data warehouses and also to the point where you guys do real time data streaming? Like I remember that was like, kind of like a big thing when you started it. How did it kind of get to that point, like up to when you, when I think, when I, when you, when you did YC, it was like summer of 23.

[Jacqueline Chong]

Yeah.

[Turner Novak]

Started the company. So like how did the market kind of evolve and play out?

[Jacqueline Chong]

So this is like a very old market. Like the, let's say some of these timelines are a little bit more made up. But like 30 years ago, when it was mostly people would move data between their like transactional databases and, and then move it into teradata once a month. It's like a really old warehouse.

[Turner Novak]

This is pre Internet, right. So you're probably like taking a floppy disk and like putting in a different computer and like dumping it in or something.

[Jacqueline Chong]

Honestly, I don't even probably, I don't even know the mechanics of that. But it was like, yeah, physically like moving data from one system, like downloading it and then moving it over once a month. And then maybe your executives would look at it every quarter, reporting would be done there. And then when warehouses went into the cloud, basically technology got better. You could do more things with the data and then you could also store more data now that it was in the cloud. And so because you could do more things, there was more use cases and people started moving that data into, let's say redshift.

[Turner Novak]

Oh, what's right, that's at Amazon's, Amazon's warehouse.

[Jacqueline Chong]

It was like the first data cloud data warehouse. And let's say it was like 10, 15 years ago, it was once a week. You know, that's probably like good enough. Fast forward to today. It's not uncommon for a company to say, hey, we're moving data from all these different systems into our warehouse once every hour. Like that's not so surprising anymore because again, technology got better. There are more use cases. People have more data to work with as well. And the use cases have also become more common. So it's once an hour. But then the trend has been very consistent. It's consistently going down. And I think what's happened over the last couple of years is with this, everyone's trying to figure out what to do with AI and when you're building AI or agentic systems, basically systems where software is making decisions on their own autonomously or like automating workflows or maybe like even interacting with your users without a human in the loop. A lot of companies are now realizing, oh wow, like I need to be able to feed live data into these systems. Because without a human in the loop, there's no human to be like, oh, I know this something happened in the last 30 minutes that you don't have context to. Let's not push this decision out yet. It's just going to make the wrong decisions.

[Turner Novak]

So you basically, in order to build good AI products, you need instant data being collected and used a hundred percent.

[Jacqueline Chong]

If you want it to be pushed beyond a demo or a pilot, you want it to be pushed into production, and for it to be running mostly autonomously, it needs to have live context into everything that happened as quickly as possible.

[Turner Novak]

And why didn't it? When you kind of think about how the market kind of evolved over time, I remember when I met you, I honestly didn't believe you when you told me real time data isn't actually really a thing. Why did it not happen? Was it because, actually thinking back, we did have LLMs being pretty predominant. It was the summer of 2023. Right. Or was it 22?

[Jacqueline Chong]

Yeah, that's when everything really started going. I don't think AI was pushing any of this real time stuff then. I think it was more like companies that have fraud models.

[Turner Novak]

So you had a lot of fintech use cases.

[Jacqueline Chong]

Fintech use cases or customer facing dashboards. Imagine you as a customer using a product and there's an analytical dashboard that you can see based off of previous actions. You've done a bunch of stuff, reconfigured things, you've launched a new campaign and then this dashboard shows you nothing. Like it's an hour old. Bad user experience. And not just bad user experience, but like usually in those cases the customer thinks the product is broken. And so those use cases were the ones that were dominant. And I think what was. And the AI stuff is more like in the recent, I say like 12 to 18 months. But why it hasn't, why it's not democratized basically is a really hard problem to solve. It's not like it didn't exist. So there are companies like Netflix, like Doordash and Instacart, they've basically spent many years building out this system in house.

[Turner Novak]

Oh, so they have this technology.

[Jacqueline Chong]

Yeah, it's like you could have streamed data like 10 years ago. It's just really hard. And they usually have a team, maybe like five to 10 engineers. Some of them have distributed systems knowledge, maybe they've done it before. They really understand how to scale this in production. And then you also, after you build the actual data movement pipeline, you have to build the observability, the alerting systems, the monitoring to make sure it's actually robust. And then failures always happen. Right. Like this is software. How do you recover from failure seamlessly? So all of these little things, they've built out over many, many years. But this is a very painful path. And that was the realization, Right. For someone that's starting today, that's like, I need to get real time data into whatever downstream system. Maybe it's a warehouse, maybe it's a lake, maybe it's another database. Whatever it is, if I want to do that, do I really need to spend two years, well before two years, hire a team, at least a few with distributed systems experience, and then spend like two years building. And by the way, the success rate is actually not that high.

[Turner Novak]

Yeah, what's the success rate? I remember you told me this.

[Jacqueline Chong]

There was like a research paper that like actually confluent put out and it was like the success rate of streaming projects is like under 5%.

[Turner Novak]

How is it so low? It's just, you should just be able to like write some code and like connect the connectors and it works.

[Jacqueline Chong]

Yeah, that's what everybody thinks. Like that's literally what my, my co founder experienced back at like his previous companies. It was like, you make a plan, you're like, oh, it's going to take two to three months because we just need these three or four core components and we have a plan and we're just going to get it set up and it might take you a month or two to get a demo, like a pilot setup and it might work. And then you try to go to production and it's a whole different animal. Where all those edge cases that I talked about in the beginning. Oh, no 1% of the data is dropping. Oh, we read them out of order. Oh, now we have shards. Schema changes have happened to half the shards, but not the other half yet. But we need to reconcile that because we're merging into one unified table in Snowflake. It's just really complicated stuff and then it never ends. It's been almost three years and we are still finding edge cases in different systems as we onboard, like different customers. And everybody has messy data, you know, like a. How can a month, how can a month be 49?

[Turner Novak]

You mentioned that you've had some teams that have built something in house that have switched over. So I mean that's kind of an interesting, I guess, like development. But like why people have already built this, why would they switch over?

[Jacqueline Chong]

Because it doesn't end. The build is not the end of the project. You have to actively maintain it. You'll find even more edge cases and then you'll have to patch it and then you'll have to build the monitors to prevent or observe those edge cases. So you can. And then you have to build the failure recovery modes. It's unending. And then your use cases will change. And by the way, as your data scales and increases by 10x, the same system that worked may not work anymore because scale will also break the system. And so what tends to happen with those teams is like they've built it and they're like, okay, maybe I can sit back. And they're realizing there's someone constantly maintaining and running these pipelines. Maybe then they onboard another database. They basically have to rebuild it to support that new database. Even adding new tables within the same database is not always automatic. There are manual scripts that companies have to run and then schema changes. So a lot of this stuff is not automated. And those companies that have switched over, it's basically at that point they're like, my whole, my team is spending like 30, 40% of their time on maintaining and running this pipeline. And we have customer requests like features, like products that we need to build that's being like pushed out. And so they're just like trying to take their time back. Like this is not their core product. And it is actually something that should be commoditized.

[Turner Novak]

Should be commoditized because everyone ultimately is building and using the same connections and data sources that they're using.

[Jacqueline Chong]

Like if you're moving data from like again like postgres to Snowflake, just using. Just because so many people are using postgres and Snowflake, does every single company need to figure out that like and build that same pipeline? It just seems a little bit backwards,

[Turner Novak]

but you still get people that hesitate on, should I actually use a third party provider for this? What's kind of the biggest hesitations that you get?

[Jacqueline Chong]

Yeah, that's a big one. I mean with any system that's real time, by definition, it's mission critical. And so the typical thought is, wow, this is mission critical. Especially in the early days when it was like two of us, no employees.

[Turner Novak]

Are we gonna trust this?

[Jacqueline Chong]

Yeah. How can we trust this startup to power our foundational data infrastructure?

[Turner Novak]

Because if it goes down, their product can't work, Right?

[Jacqueline Chong]

Yeah, yeah, yeah. And so that is a very rational fear that people have. And I think, but when you think about it is what we benefit from is one, this is like our full time, this is our product, this is our core product. And we benefit from working with, we benefit from scale. So like working with many different customers, with seeing like more edge cases than any singular company could and then dealing with like a lot of data complexities in the day to day. So it's basically like we've seen more and we're able. Our software has accommodated for likely more edge cases than any one company can solve themselves. And that's why it's actually a safer option and a faster option to get up and running.

[Turner Novak]

I feel like some people might argue that it's a crowded market. There's just a lot of different data tools and pipeline connectors, et cetera out there. What like. But you still did it anyways, so why.

[Jacqueline Chong]

I think on the surface it looks like a very crowded market. There's so many data integration tools. But when we are talking about the streaming space and my co founder actually went through this when he wanted real time data in Snowflake, he actually went out to try to buy something first. He initially tried a bunch of the batch players, but obviously it didn't meet his latency requirements. And the other thing is he was working at Opendoor and Zendesk. So the scale of data was really big and batch systems just can't keep up in those situations.

[Turner Novak]

This is where you dump the entire database in and it syncs.

[Jacqueline Chong]

No, this is even like, hey, every hour we're going to go and find any changes that have happened in the last hour and then move it over. But imagine now that your company is so big that the amount of transactions that have happened in the last hour is massive. The time it takes to move that data continues to increase at every interval. So that's what I mean by batch systems just can't keep up at scale. But then he's like, okay, let me buy a streaming product. And you know what exists in the market with streaming in particular is like a lot of raw infrastructure.

[Turner Novak]

What does that mean?

[Jacqueline Chong]

Yeah, the analogy I like to use is imagine you wanted a wedding cake and what exists in the streaming market is like there's different types of flour, there's different types of salt and there's vanilla extract. And icing, different types of icing. And then based off, and they're like, okay, now you go based off of how many team members you have. Do you have a baker amongst your team? Have they baked a wedding cake before? And then across a period of time, whatever you guys have baked, like the quality of that can really vary. Or maybe the wedding cake doesn't even like it completely topples over because you don't have the right team or you don't have the right experience. And that's the streaming market. A lot of raw ingredients.

[Turner Novak]

So you still have to build it.

[Jacqueline Chong]

Exactly. You have raw ingredients, but you have to build it yourself. And so we kind of approached it in a very different place.

[Turner Novak]

Place.

[Jacqueline Chong]

Our assumption is that companies want the outcome that a streaming system provides, which is I want data faster to pump data into a risk system. I want data faster so my AI agents can get the context they need, but they don't actually care about the infrastructure. So our whole thing is we're coming in and we're like, forget all the raw ingredients. Here's the cake. This is a cake that's completely done. It's already baked. Just go and eat it.

[Turner Novak]

That is actually what most people would prefer on their wedding day is just, hey, bake your wedding cake.

[Jacqueline Chong]

I don't think anyone bakes.

[Turner Novak]

Nobody bakes their own wedding cake.

[Jacqueline Chong]

Yeah. And like that's. So it was like taking a very different angle to this space and it seemed to make sense at the time. We were like, why wouldn't people want this?

[Turner Novak]

Yeah. What were some of the biggest product decisions that you've made over time, like early or even more recently?

[Jacqueline Chong]

I think one very opinionated thing that we did in the beginning was we only focused on databases as a source. We've since branched out. Like, we have an event. We can ingest events now and stream it into downstream systems.

[Turner Novak]

This is when it happens in the product, it doesn't go to the database, it goes straight to the data warehouse.

[Jacqueline Chong]

Yeah. Like it hits our events API. And this is usually more like web events, click streams. Like this type of data that doesn't need to hit a transactional database. But in the beginning it was like, we're not going to touch any other source. We're only going to do databases.

[Turner Novak]

Yeah.

Why'd you do that?

[Jacqueline Chong]

So that we could be really, really focused. Because there are maybe, maybe like 10, like really important transactional databases that exist today. And we were like, if we only focus on these very few sources, we can build really, really in depth, focus a ton of time on dealing with these edge cases that typically break the pipelines. And then we can build the best product, the most reliable product. But if we get spread too thin, by definition, you're like trading off on like quality. So we were like, we're not going to do anything else. And we, we did have, you know, prospects that come in. They're like, hey, if you build an integration into Salesforce or Workday, we'll buy your product. And that was really, like, you know, when you had, like, zero revenue, it's, like, hard to say no.

[Turner Novak]

Did you. Did you chase any of those or do any of those.

[Jacqueline Chong]

No, we said no. And it was, like, painful. But I think it was the right decision because for the first two, two and a half years, that really was our strength. It was our secret sauce. And for people who tried our product, they were like, whoa. And this is kind of funny, because you would think products would work, but they're like, whoa, what you said in the demo, it actually works.

[Turner Novak]

Yeah. I remember when I invested, when I first met you guys, some of the diligence from engineers was, like, they had very strong feelings about other products. Strong, negative feelings, like, you know, specific words to describe them. But, yeah, arty just worked really well. And I, like, actually liked using it. So it's, like, interesting that that's kind of like the NPS score of the market is these, like, visceral reactions from the engineers of not wanting to use the product.

[Jacqueline Chong]

Yeah, I mean, it's actually. I didn't even know this before because I wasn't in the data space before this, but it's crazy, the amount of skepticism that comes in, because they're like, it just works.

[Turner Novak]

Like, come on, that can't be possible that this thing just works.

[Jacqueline Chong]

Yeah, actually, they'll come in. They're like, by the way, this is like the beginning of a discovery call. They're like, hey, I just want to let you know I'm very skeptical because I've tried, like, tool 1, 2, and 3, and. And I've been burned by all of them. And so excuse me if I'm a little skeptical on what you can do. And then you show them the demo, and they're impressed, but there's still a lot of skepticism when they try the product. And I always tell people all those things that broke from before. Just give us your hardest tables and your most problematic data. Try to break arty with it. And that's when they get their. That reaction. Like, wow. Like, you actually. You guys weren't kidding. It actually just works.

[Turner Novak]

Yeah, it's kind of like the do hard things, like, solve hard problems. So you basically. You tell your prospects. You're like, try to break it. Like, try to stump us. Try to do something that you don't think we'll be able to handle.

[Jacqueline Chong]

Yeah.

[Turner Novak]

I think another super interesting thing on the customer front, you. When you are doing yc, you. I think, like, there's, like, a common YC playbook is, like, sell to other YC Companies, people in your batch, whatever. Like, that's kind of like the most common YC playbook. Like, that's how you grow really quickly. What you guys did was like the complete opposite of that. So how did you get the first couple customers?

[Jacqueline Chong]

Yeah, and by the way, like, we tend to like to follow YC advice because it's like, very practical and very smart. Our problem was you need to have a decent amount of data for streaming to make sense. And so, like, not like most YC companies, like in our. Especially in our batch, when you're just starting out, like, you don't have data problems. And so we, we literally, we would have loved to sell to our batch.

[Turner Novak]

Yeah.

[Jacqueline Chong]

But no one had data problems, and so we couldn't. And so we had to rely on just cold emails. And so I was just sending. I think during yc, they had this, like, just write personalized emails. And this was, you know, pre. This was in the very beginning of AI, so like GPT 3.5 wasn't even that good. And so I was just like handwriting emails. And I had like a target to write like 20 emails a day.

[Turner Novak]

And it was just like, Netflix, head of data. What's their email be like? Hey, James, just slide in and just say whatever needed to be said.

[Jacqueline Chong]

Yep, exactly like, hey, you know, the email to Substack must have been like, hey, Mike, I noticed that you use Snowflake. You know, Arty moves data into Snowflake in near real time and we handle all the hard stuff like schema evolution and merging and everything. Does this sound interesting? Would you want to chat? And that. That was like the gist of it.

[Turner Novak]

And that was your first big customer, right, with Substack?

[Jacqueline Chong]

Yeah, that was our. Not even our first big customer. It was like our first customer.

[Turner Novak]

And it was a big customer though, too.

[Jacqueline Chong]

Yeah, I mean, that was. It was terrifying. I mean, like, you. We had only tested our pipeline with maybe a couple thousand rows, which in like, database land is very, very, very, very tiny. And the. The moment they wanted to onboard or like in the poc, they were like, yeah, we're going to start streaming like a couple billion rows.

[Turner Novak]

Yeah. So were you guys like, shit, what do we do?

[Jacqueline Chong]

I mean, yeah, effectively. And then we were like, we have to make this work.

[Turner Novak]

Yeah.

[Jacqueline Chong]

And so we obviously there were like a lot of hiccups with onboarding a table that had like tens of billions of rows of data in it. But, you know, we. They were very nice about it. They really wanted the promise of what our product could do at the time. And they really helped us, like, go through the hurdles of. I mean, they had very, very strict requirements of how you could connect to the database, like, how fast you could pull from it, because they wanted to protect their database. It's like, a very reasonable thing to do. That rigor really helped us make our product better. And then actually, the next 10 customers we onboarded after Substack were, like, significantly easier. Like, nowhere near what we had to do to onboard them. But the funny story is, with Substack, like, at that time, I don't even think we really had a ui.

[Turner Novak]

Oh, yeah.

Remember, it was a big deal when

you just, like, there was a dashboard.

[Jacqueline Chong]

Yeah, that was a big deal because we didn't have a dashboard when Substack onboarded.

[Turner Novak]

So they didn't know if it was working. Basically, like, is, like, the product running, but, like, how do we access it?

[Jacqueline Chong]

Yeah, I mean, they could. They could see it after it landed in Snowflake, and then they could, like, query the data and stuff like that.

[Turner Novak]

But there's nothing telling them that it was working.

[Jacqueline Chong]

No. Well, what we did, we had, like, a shared Google Doc or just Google Sheet, and then they listed out the hundreds of tables that they needed synced over. And then we had a status column

[Turner Novak]

that you would just update.

[Jacqueline Chong]

We would just be like, this is backfilling. And then when they completed, we'd be like, now this is streaming. It's done backfilling. And we'd put timestamps on things, but it was just Robin going in there and updating it over multiple days, because

[Turner Novak]

it takes that long to, you know, to upload them.

[Jacqueline Chong]

Yeah, yeah, yeah.

[Turner Novak]

And so they still went through with this, even though, like, that doesn't sound like a good user experience, honestly. But it was worth it to get the. Like, was it? I mean, it sounds like, was it real time? Because Robin was manually doing this stuff.

[Jacqueline Chong]

Oh. Oh. I mean, the onboarding was absolutely not real time, but once data was streaming.

[Turner Novak]

Oh, so this was actually like the hookup, the onboarding process.

[Jacqueline Chong]

Yes. This is like the implementation and doing the historical backfill. But then once it caught up, then it was just like, streaming. And so the backend infrastructure of all of that actually worked really well. But the UI experience was definitely not there. I mean, it didn't exist.

[Turner Novak]

And then you obviously built, like, almost like scaffolding observability alert systems all around that.

[Jacqueline Chong]

Yep.

[Turner Novak]

So then what did you learn about just, like, implementation? Because I know there's one customer. It's actually. I think it was Kind of hilarious. Like dedicated listeners of the show will remember there's an episode with Tommy, the CEO of Alloy, which is like one of your customers. You were you actually in the office implementing arty while we were recording. And I think there's maybe there's like a couple of people that actually remember us having that conversation. And like we brought you guys up, you were in the office and that was like the first time, I think you did like hands on. You went to the office and did the onboarding. So how did that come about starting to do that?

[Jacqueline Chong]

Yeah, that was different because it was our either second or third implementation of our BYOC offering. So basically we have our cloud solution where RD cloud processes your data and then moves it from your databases into your warehouse or data lake. There are a ton of fintech, govtech or just heavy compliance companies where they're like, our data cannot leave our AWS Azure GCP environment. And so with those, we implement the entire data plane in our customer's environment so that data processing doesn't have to leave. And this can actually, we actually do a lot of this like remotely now because we've done it enough times. But I think it was more like it was early. We just wanted to make sure that we had like live communication because if we do, I think, you know, like we went there, we sat next to their like security and networking team. It wasn't even actually the team that would use us day to day. It was like, let's get security, security and networking, figure it out.

[Turner Novak]

Because there's a big compliance issue.

[Jacqueline Chong]

It was a compliance thing. And so it was like, how do we figure out the minimum amount of networking permissions that our data plane that's sitting in their environment needed to have to connect. So it was really like honing in on these things and then also making sure the team was comfortable and had someone live to talk to whenever they ran into issues. So we could move a lot faster.

[Turner Novak]

Because you ran into an issue where like their security team just said, no, we can't do this. And like we have to kill the deal or something like that.

[Jacqueline Chong]

Yeah, yeah. I mean it would never went to the point where they were like, we're going to kill the deal. But it was like, hey, we can't do this, we can't do this, we can't do this. So we're like, you know what? And this was like over zoom and like over slack. We're like, you know what? We're just going to come there and then we'll just like go into a conference room together over a couple of days and then we'll tell you exactly what we need and then you can run the command, we can be next to you and figure all of that out in a couple days instead of, you could imagine that dragging out for a month. But we really wanted to make sure that the team that needed this could get up and running as soon as possible.

[Turner Novak]

Should do a lot of this just manual hard product fixes and then you kind of automate them essentially. Seems like a big. Yeah, that's like a big part of it.

[Jacqueline Chong]

Yeah, yeah. And we do this for actually everything. Like it's not just engineering, but even across like marketing or sales. Like if it's the first couple times we're doing something oftentimes like I will like personally just manually do something once we realize that it works like we know what the SOP or like standard performances, then we'll create a runbook, automate it and then it's like much easier for the next either customer or the next people that need to do this.

[Turner Novak]

So I think even now one of the more interesting things like your. Did you hire ahead of growth yet? No, but you're looking to, you're trying to make a growth hire.

[Jacqueline Chong]

We do not have a growth hire unless we do have a marketing hire.

[Turner Novak]

Okay. And they report to Robin, the cto, right?

[Jacqueline Chong]

Oh, you're talking about our biz ops.

[Turner Novak]

Okay. Yes, yes. So like I would think a marketing person like attached to revenue, they would report to the CEO, but they report to Robin.

[Jacqueline Chong]

Yes.

[Turner Novak]

Cto, yes. Why did you do that?

[Jacqueline Chong]

It's a hack like so business operations, it's a pretty loose. Depending on the organization, it can mean different things. But it's about scaling and optimizing processes. And I think especially today with all of what you can do with AI and even on the go to market side automating with clay tables and agentic flows, that is a very technical problem. They're just not to confuse with data pipelines, but a different type of pipeline that you would run internally. And so I actually think, and one of the advice that we got is it's a superpower if you treat it as an engineering problem. And so you know, especially with like technical leaders, I think their brains are, their brains are wired to always optimize.

[Turner Novak]

Like always try to automate.

[Jacqueline Chong]

Yeah, I think it's an engineering thing. Like all the best engineers that I know, they're just constantly thinking like they'll walk into a restaurant, like my co founder will walk into a restaurant. They're like, eh, they really shouldn't put the seating like this. I was like, we're just. Yeah, that's awesome. They're like, he's like, if they can rearrange this this way, they could put like 20% more seats.

[Turner Novak]

Yeah, honestly. So sometimes I'll have that conversation with my wife and she's like, who cares? Yeah.

[Jacqueline Chong]

I was just like, we're just, we're just having dinner.

[Turner Novak]

Well, actually, I remember one time it was. I think you had. Oh, it was a new office party. I was talking to Robin once and he was telling me like it was some sort of like, yeah, I've like optimized all these things and like, I don't have to do any work anymore. Like, if I chose to, like, I wouldn't have to do anything because I've automated everything. And I was like, oh, that's pretty cool. Obviously I think he told me, like, it's done. Like if I, like, I don't have to work anymore because I've automated every single thing. And like, obviously that's not true anymore, but it was like a fun, like, story. I just remember him telling me he's like, automated everything about the job. Like, Yep. I think he like, automated fixing bugs, like when there's like a issue with one of the pipelines. You probably automated a process of doing that.

[Jacqueline Chong]

Yeah, yeah, we automated like a DevOps process. Like every. Every quarter or every half a year we have to like upgrade Kubernetes and like a bunch of other stuff across all our data planes. And at this point between our data planes and all our customers, like BYOC data planes, we have like quite a few. So it would literally take someone like a week or like two weeks every

[Turner Novak]

quarter to upgrade everything of just going into all these different systems and clicking on.

[Jacqueline Chong]

Literally just upgrading. Yeah, like following a runbook. And so we built recently an agentic flow where you just have to. It's the same process. Right. And AI is great with following runbooks. If you can build something into a runbook now, someone just like presses like start and it will create PRs. You're like, okay, great, do the next step. And you just watch it. Do the next step. Like, wait 15 minutes, see, it's okay, and then do the next step. So we've automated that. But yeah, like having someone who's like naturally like obsessed about optimizing something run business operations, I think has been the biggest hack for us.

[Turner Novak]

Interesting. So you have the ops team report to the CTO because they're automating a lot of things.

[Jacqueline Chong]

Yeah.

[Turner Novak]

So do you still do a lot of just outbound, like a lot of cold outbounding at this point or is it like you have a bunch of BDRs, maybe? Is it a lot of warm stuff? What's the process still look like?

[Jacqueline Chong]

It's a pipeline. So. So we still do some. There's still some personalized outreach that we're doing. It's highly researched. There are accounts that we think are. We know they're dealing with problems that we solve and you know, it's like deep research, you know, learning about the systems that they have, the technographics, the firmographics and whatnot. But we also have optimized and like built a go to market engineering pipeline with the tools like clay and a bunch of other tools that you've stitched together. And it kind of understands our ICP the right buyer Persona. And this thing is just running autonomously. So we've basically built a pipeline to replace BDRs and SDRs and we only have AES at RD.

[Turner Novak]

Isn't there some of these AI tools like AI BDR? Do you use some of them or you build your own?

[Jacqueline Chong]

We do not use those. And maybe this is like we should test it because AI is improving so quickly. Maybe we're missing out by not testing it, but we basically built our own internally. And the great thing about this is it's so much easier to train because with with a lot of like SDR BDR functions, there's a lot of like, hey, let's make sure we describe arty properly. Let's make sure there is no typos, grammar is correct. Let's make sure to remember to follow

[Turner Novak]

up to a prospect like exactly 24 hours after the call or whatever. Or like a certain drip scenario.

[Jacqueline Chong]

A drip, drip scenario. And like that stuff AI is great at.

[Turner Novak]

It will never drop, It'll never get that wrong.

[Jacqueline Chong]

Yeah, yeah. And it will never misspell words or forget how to describe RD because they understand our messaging framework and our positioning. So you're really dealing with tweaking the language rather than training more basic stuff.

[Turner Novak]

And actually this is maybe one of my friends who's probably one of the best founder founders at sales I've come across. When you're talking with no typos. He actually showed me that his biggest hack for emailing people is like lowercase subject line in the email and also maybe a typo also because it shows it's not AI. Like it shows it's actually a real person sending the email. I think the Other interesting hack that he has is you get people on imessage as soon as possible. Because he's like, deals actually close on imessage, not on email. Which is kind of interesting maybe like especially when you're doing hand to hand combat sales. He does pretty, I mean it'd be like high ticket acv, like pretty big deals that he's closing. And he's very much like an imessage. Like deals close on imessage. Have you found that much or.

[Jacqueline Chong]

Yeah, I mean our equivalent is actually just like Slack or teams like direct a channel between and we'll pull our engineers into it. And the whole idea is like when you reach out with a question or when there's an error, you're not reaching out to a customer support person that might just be going to the docs and copy and pasting answers and then just giving that answer out to you. You're reaching an engineer who can fix it, who can fix it and also not only tell you that it's fixed, but, but explain exactly what went wrong, what we discovered and then how we fixed it. And I think that's one of the, actually the best selling points when we were working with prospects. They're like, there's a lot of transparency. You guys are actually helpful because software is not perfect. There are errors here and there and it's how you deal with them and how much transparency you give your customers.

[Turner Novak]

Yeah, it's also too like, you know, if you're emailing it almost feels like too corporate. Like, hi Jacqueline. You know it was great meeting yesterday. I'm following up. Blah, blah, blah versus Slack. It's like, oh no, this broke. Can you fix it?

[Jacqueline Chong]

Or whatever.

[Turner Novak]

Like it's just like more to the point, like a little more casual.

[Jacqueline Chong]

I think the thing actually what it does is like it makes them feel like we're an extension of their team because then it like branches out. They're like, hey, we've decided to build this new feature and we're curious about what is the best architecture so that we can serve it to our customers for this use case and they'll describe it and I guess it's outside our scope normally, but then we will actually go in and maybe do a quick zoom call or a huddle or something and think through or we can share. Hey, we have another customer that did exactly this thing and this was the best architectural pattern to achieve this. And so we're, we're actually, yeah, we're very much an extension of their engineering team.

[Turner Novak]

Yeah, I feel like one of the advantages startups have is you can over support the customer. Like, you can just like you can't be. There's. It's impossible to over invest in the customer experience. I mean, maybe it's possible, but like just going above and beyond and making people feel good, making it, you know, easy to work with you. And like the different ways to do that, whether it's you go to their office or you have the slack connection or you have their imessage or like you have dinner with them more often, just. Or the immediate response, like instant response, like they slack you and within two seconds the bubbles pop up that you're responding like, or the name, like Jacqueline's typing. Like, like all of those things. It just like makes people trust you more and want to work with you.

[Jacqueline Chong]

And like, you know, people have been telling us for a long time like, hey, this isn't sustainable. You know, at some point you're, you know, you're gonna, you're gonna become a bigger company and like you can't provide this level of support to your customers anymore. And I actually don't believe that because AWS has great support and they're massive.

[Turner Novak]

One of the biggest companies in the world.

[Jacqueline Chong]

Exactly. I think it's just if you intentionally choose to prioritize this, like if this is a core, I think this is like a core part of our product actually. And if you believe that and you intentionally try to keep it, you can make it happen. Because like AWS has.

[Turner Novak]

I've been having the worst experience lately with my health insurance. This is like a shout out. Like I'm selfishly saying this. I feel like there needs to be like a better startup, friendly version of health insurance or insurance. We use this provider in Michigan and I don't know, we mess up the password on our account. And I've been trying to pay the bill for a month and I can't log in. And we've sat on the helpline multiple times for hours at a time. They can't reset our password and get us into our account to pay. I'm like, this is so. It's like 20% of GDP run through health insurance. Basically.

[Jacqueline Chong]

If you care about those things as a company, it's such a, it's such a hack. Like you could win. Your win rate must like triple just from responding to you to help you reset your password. It's such an easy thing to do.

[Turner Novak]

So if you are making better health insurance, not only will I probably try to buy it, like I want to invest in it, I feel like it's a, maybe it's like a terrible category. I have no idea. But I'm like, I probably, I have

[Jacqueline Chong]

no understanding of the health insurance, but it's big.

[Turner Novak]

I think it's like a, it's like an ingredient. It's like you need a big market. Healthcare is like a massive chunk of gdp. There's also the, the other angle of like going direct and going around health insurance and just go directly to consumers building like a better product that doesn't open insurance.

[Jacqueline Chong]

Like it might have been Dalton Caldwell or like Michael Seibel that said this to somebody, like to our batch. But it's like if you're pivoting, an easy way to figure out what to do is find a really, really big incumbent that's doing something and all their customers hate them and then just build a better that

[Turner Novak]

find customers who are not happy to have a problem and fix the problem.

[Jacqueline Chong]

Problem. Yeah, because clearly there's still. It's a painful enough problem that they're still.

[Turner Novak]

Yeah, that's kind of how you started already, maybe initially. Going back to. Yes, going back to the beginning. So like, so how did, how did that go going back to the beginning? Like what, what's the story there?

[Jacqueline Chong]

Oh gosh, I guess we have to go like six, seven years back.

[Turner Novak]

Yeah.

[Jacqueline Chong]

But my co founder, who's also my husband, he, he used to work at this marketing automation 5 person yc startup and they were a really small company but because of the nature of marketing automation they were dealing with immense scale. So he was figuring out how to build databases that could store billions of rows of data on a daily basis. And then that startup ultimately got acquired by Zendesk. At Zendesk, that's when he really learned and used cdc because Zendesk had so many different products.

[Turner Novak]

So CDC is change Data Capture.

[Jacqueline Chong]

Change data capture, which means, which is the way you can grab database changes and get them out of databases.

[Turner Novak]

So instead of taking the entire database and copy and paste it, you just say what has changed and what changes?

[Jacqueline Chong]

Exactly.

[Turner Novak]

Copy what's changed and paste that in.

[Jacqueline Chong]

Exactly. And because Zendesk had so many products, they were basically using CDC to grab all the changes from all the different products and building a unified experience for their customers. So that was really important there. And the scale of which Zendesk was operating at is also important in his history of this. Then he went to Open Door and that's when they really could benefit from getting real time data into Snowflake because they were such an ops heavy company. They were like buying and selling homes. But every time they bought a home, you have to schedule an inspection. You have to do maybe repainting the walls and then relisting and stuff like that. All the ops people, obviously you can't have access to the database. All the ops people worked off of Snowflake data. And so the faster that you could get data in, you can imagine like one of their key metrics was like days on market of the homes. So there's a bunch of different factors that go into that, but one of the things is if we can get data in faster and like our ops was more efficient, then days on market could theoretically go down and make more money. Exactly. And so this was an important project. And then he ended up. I kind of like described it a little bit in the beginning, but what he ended up having to do was like bake the cake, get all these tools. It was like seven or eight engineers at the time, building for 11 months to a year, and it was still not fully ready to go into production because of all the edge cases, the schema drift and all that stuff they had to handle. And that's when he was like, wow, I am moving data from Postgres to Snowflake. How many other companies need to do this? Do they all have to hire a team and use these tools to build it out? And do they all spend a year or two doing this? What if this could be a product where I could deploy in 15 minutes? That's what he wanted. And so that was the idea. And when he told me about it, actually, I was initially like you. I was like, are you sure?

[Turner Novak]

Like, like that just kind of sucks.

[Jacqueline Chong]

Not everybody has real time data.

[Turner Novak]

Yeah, that's. Why is that even a problem? Like, it should have been fixed.

[Jacqueline Chong]

Yeah. And I trusted him, but I also wanted to verify. So I went out and I talked to a bunch of my friends that worked at different, like tech companies in the Bay Area. And I found out that, wow, it wasn't like a Zendesk specific problem or open door specific problem. This is just how things worked. And then that's when I got really excited because it seemed to be a structural problem with how streaming worked today. And there seemed to be this gap that we could fill if we could build this product. So we decided to jump in and see what we could see what would happen.

[Turner Novak]

And what were you doing at the time?

[Jacqueline Chong]

I was working at a hedge fund called Balyasney. I'd been there just over three years, I believe, but I was investing. It was a long Short fund. I was investing in enterprise software companies.

[Turner Novak]

And so you kind of understood how software worked as much as a public market investor could understand or correct?

[Jacqueline Chong]

Yeah, as much. I mean, now I look back and I'm like, wow, I didn't, I really didn't know. I really didn't understand, like the technology. But yes, I learned a lot about like the business model and like how to, you know, like, what, what software companies did, like, what, what great looks like in terms of like financial metrics and stuff like that.

[Turner Novak]

You knew what a good software company looked like from the outside, but you didn't know how to make it.

[Jacqueline Chong]

Yeah.

[Turner Novak]

Yeah. Okay.

[Jacqueline Chong]

Yeah.

[Turner Novak]

So what was the decision to actually start a company? Like, when Robin was like, I'm gonna make this company where you're like, cool, go. I found where you're like, oh man, I kind of want to join. Like, this seems fun. Like, how did that evolve?

[Jacqueline Chong]

Yeah, it kind of started off like, I will help you do. It was. It was very simple. Like, it. I think part of it was that we were a little naive at the time because it was like, hey, we're going to get your data in faster and it's going to be the easiest thing you've ever deployed to achieve this. And from a TCO perspective, it's going to be a lot cheaper. It sounded like a no brainer. So our initial thought was like, hey, we're just going to build it and we'll tell people we're building this and like, people would just buy it. Like, that was, that was the thought. So I'm like, hey, I'm gonna help you, like set up the company. I'll do the sales conversations.

[Turner Novak]

And so it was almost like a side project. Almost as like you were thinking about it or was it like, we're gonna quit our jobs and start working on.

[Jacqueline Chong]

We quit our jobs. Like, we were like, let's. We'll quit our jobs, we'll do this. We just thought it would be easier than it was.

[Turner Novak]

The classic just not. I mean, nothing's ever as easy as you think.

[Jacqueline Chong]

Yeah, yeah, yeah.

[Turner Novak]

And you guys did do yc, did you? Was it like right in the beginning, like, we're starting a company, let's apply to yc or at what point did YC come into the picture?

[Jacqueline Chong]

No. So funny enough, for whatever reason, like, I had, I didn't know about YC at all. Like, had never heard of them. I was like, really not in the startup space. I happened to. So we, we quit our jobs. Robin started building. I was like, I Need to buy a book to, like, learn, like, what sales, like, how do people do sales? So I was like, reading, I think it was like, founding sales. That was a book by like, Pete Kazanji. Really good book for someone that had, like, absolutely, like, no understanding of how to do sales. So I was reading that book and I was like, getting the business, you know, the admin stuff, like, set up and it's. I tried to email a bunch of people to have conversations to see if they would, you know, be interested in the product. And that's what I was doing. And I didn't know what the ICP was or the buyer Persona. So it was like a really broad category of people that I was going after. So what I'm trying to say is, like, I really didn't know what I was doing. And then at the time I happened to see, see one of my college friends post on LinkedIn that he was doing YC and how he was starting his own company. So I actually just reached out to grab lunch and to learn more. And at lunch he's like, oh, you should apply to yc. And he's like, if you apply and you get in and if you don't do it, you're an idiot.

[Turner Novak]

That's how he described it.

[Jacqueline Chong]

Yeah.

[Turner Novak]

Okay.

[Jacqueline Chong]

That's just how he was. And he's like, I will, like, you should apply. And then if you get an interview, like, I will text me and I'll. I'll tell you how the interview goes.

[Turner Novak]

Okay.

[Jacqueline Chong]

So, yeah, so then I just did it and I was like, well, like, who knows if we'll get in? Because he was like, the septum rate is really low.

[Turner Novak]

Yeah, it's 1%. I just had. I was talking to Gary. The. I talked to him yesterday. The episode will come out in a couple weeks after people are hearing this probably. But yeah, he's like 1% of application approval rate.

[Jacqueline Chong]

Yeah. So I was just like, okay, whatever. We'll just like submit an application and we'll decide if, like, if we, if we get it. And this is a very YC thing, but, like, when they accept you, they ask you on the call if you're

[Turner Novak]

gonna take it, right?

[Jacqueline Chong]

Yeah. And like, obviously you're like, it's a little bit of a knee jerk reaction. But I was like, yes.

[Turner Novak]

Because your friend was like, you're an idiot if you don't do this.

[Jacqueline Chong]

Yeah. And I was just like, okay. I just said yes. And then after the call, I rem. I turned around and I was like, robin, was that okay? I said, yes. So I guess we're doing this and he's like, yeah, it's fine.

[Turner Novak]

Okay. And then how did that go? The whole process of doing YC for

[Jacqueline Chong]

somebody who's never done it before, it was great. It was great. It was such a great learning experience. I spent the whole batch just learning to sell. And because we had spent like six months before roughly just building the product, we had again, data infrastructure tools. It takes a bit of time to build. So thank God we actually had that six month lead time. And I spent the whole batch really just selling the product, which I think helped a lot.

[Turner Novak]

You didn't have to go like 0 to 1 on the first sale in the 13 week YC batch, you were able to go 0 to 0.8 or something and then you go 0.8 to 1 through the course of YC.

[Jacqueline Chong]

Yeah, yeah, yeah, exactly.

[Turner Novak]

So you said you spent the whole time learning sales. How did you evolve your thinking? Like, what did you learn through YC or even like through today? Like, what have you learned about sales?

[Jacqueline Chong]

So I guess it can be like very, it's very specific depending on your ICP and the buyers that you're selling to. But mostly what I learned was how to sell to a technical audience that doesn't like to be sold to, tends to be more skeptical. And how do you win their trust? I think that's the big lesson. And then this might sound like really basic stuff, but like, how do you do a great discovery call? How do you, I mean, like, I think there's a bunch of frameworks out there, but how do you make sure you're like asking the right questions? And that depends on your product. But the goal of the discovery call is like, do they have pain? Like a very simplistic thing is like, do they have pain? And is already appropriate or overkill for what they want to do with their data.

[Turner Novak]

And you're basically finding out, is this someone who might become a customer relatively soon?

[Jacqueline Chong]

Yeah. And when they become a customer, are they going to be happy? And I think you can gauge that within the first conversation. But it's learning to actually not say sell and ask a ton of questions and understanding where to dig deeper to unveil either pain or what outcomes they really want.

[Turner Novak]

So it's a discovery call or discovery portion. How would you take it a step further?

[Jacqueline Chong]

And then you do your demo call

[Turner Novak]

and you don't demo on the first call.

[Jacqueline Chong]

We don't, we kind of like explain what the product does on a high level, but the first call is really Just to understand their situation, their challenges and their pain. And I think it might feel a little bit weird, but it is very much for us to like truly understand them such that the rest of the process, they can learn to trust us a little bit more because then everything else is catered from that. And you never stop doing discovery. Like when you do your demo, you do deeper discovery when you meet again. Every time you meet, the rule of thumb is like, you should always find out something new about your customer, but then it's building out the rest of the process. After the demo, do we do a technical deep dive with their DevOps and platform and infra teams before we do a PoC? How do you run a pocket? What do they need? Like, what type of checkpoints do they need? Like mid and end of trial? How do you talk about pricing,

[Turner Novak]

all

[Jacqueline Chong]

of that and how to multi thread across different stakeholders and when to do that and when not to do that? Those are all things that I've had to learn over the last few years.

[Turner Novak]

What was the one of all those areas? What was the area you sucked at the most or the place you feel like you've improved the most and like

[Jacqueline Chong]

learned the most on probably the discovery call.

[Turner Novak]

Oh, really?

[Jacqueline Chong]

I think that's the most important call.

[Turner Novak]

So did you just not do that? Did you just jump in too quickly and not get to know what they were thinking about?

[Jacqueline Chong]

Probably in the first. Yeah, definitely in the first couple of months. But it's about like the first 20 minutes you meet someone. Maybe it's actually even less like the first like five to ten minutes of meeting someone. How do you effectively make them trust you? And they'll trust you in that moment if they feel like you understood them. And the only way you can understand them is to ask good questions and then dig deeper in areas that they care about. I think that takes a lot of knowledge around the technicals, knowing the nuances. And when someone, you know, someone can tell you like a hundred different things, how do you narrow down to like the five things that actually matter and dig deeper? I think that takes like, it took me a while to get to that point. And then you know what, like the other thing is, how do you provide an insight that they didn't even ask for, for after hearing everything they want to achieve?

[Turner Novak]

So why is that important, providing an insight like just a mission?

[Jacqueline Chong]

Because you can be like a thought partner to them and it helps with the trust. It's like, hey, not only did you take the time to fully understand the challenges that I'm facing now you've understood it well enough and then have thought of something else that I haven't thought of before. And I think you could actually help me as I think about this new use case that I'm going to do. So for example, someone's like, hey, I am currently using another, you know, I have a postgres database and I'm using another Postgres database as an analytical warehouse use case. It's not meant for it, but I am doing that for now because it was an easy decision at the time. And one thing, instead of just focusing on, yeah, we can move data between postgres and postgres, we could do this. This is how you set up rd. What else do you have? It's like, oh, well, I know this works fine for now. If your data 100xs or a thousandxs over the course of the next year or two. Have you thought of what you're going to do? Because that warehouse is not going to hold. Where are you going to migrate it? What are your use cases? Is Snowflake the best one? Is databricks the best one for your use case or should you actually be upgrading to a Clickhouse and helping them walk through it wasn't even what they asked, but it's helping them walk through what the future could look like, you

[Turner Novak]

know, so it's almost like consulting, like help them with problems even if it's unrelated already. Sounds like.

[Jacqueline Chong]

Yep. I think it's all, it's, it's, it's all enterprise sales is like consultative and getting to that level where you can have a really good understanding and then consult takes a lot more experience.

[Turner Novak]

And you. We haven't even talked about this until now. You just announced as of. Well, we're recording this, you're going to announce it tomorrow, but when this came out, it'll be a couple of days ago. You just announced you raised Series A. So what did you just announce and what are you doing now?

[Jacqueline Chong]

Yeah, so we raised a Series A led by Standard Capital. So it's Dalton Caldwell, Paul Buchh and Brian Bergs Series A fund and they

[Turner Novak]

were partners at YC for a very long time.

[Jacqueline Chong]

Yes, for a very, very long time. And they have a very deep understanding of actually developer tools. So we're really, really excited to partner with them and for them to be a thought partner as we grow and scale our go to market. But with the funding we're really excited because we are going to invest even deeper into the core infrastructure. We're going to. We already Started, but we're going to, you know, expand our integrations to like other sources and destinations where real time matters and then obviously growing the team. So we're hiring across like engineering, sales, biz, ops, support, marketing, like across the company to help us like achieve our mission faster.

[Turner Novak]

So I think it might be interesting for people to hear more about standard capital. I feel like at this point there's been like a couple announcements. People are like, huh, interesting. Like. Cause I feel like when they first came out with it, there was like a. I wonder if any founder will work with them or like what type of founders will work with really. Okay, well there was just like a. It was a new model, right?

[Jacqueline Chong]

Yes, yes.

[Turner Novak]

So like what's the model that they do that's like kind of different from maybe like the other series A funds that you talk to?

[Jacqueline Chong]

Yeah. So number one, it's an application. So it's very similar to yc, very different questions. But it's an online vibe kind of. Yeah. Like all the questions are online. You put in your application. You have to tell them how much you want to raise from them at what valuation. And then they asked you a series of more post product market fit questions. It was very different from the YC questions. And the whole thing I think literally lasted just under two weeks. So you submit the application by whatever deadline they have. You get a first interview that's 20 minutes, which is double. Like YC was 10 minutes. So this is like double the length. And then if you make it to the second round, they'll meet you in San Francisco for a 45 minute second round meeting where you'll go like much more in depth into your business and how you think about certain things. And you meet the three of them with your co founder and then literally I think it was the next day or the day after you find out if you got in and they just

[Turner Novak]

give you a yes or no. Basically.

[Jacqueline Chong]

Pretty much, yeah. Yeah.

[Turner Novak]

Because it's definitely different from. How would you describe the series A process with other funds you were talking to? How was that so much different?

[Jacqueline Chong]

Well, I was told the average series A process can be like three to six months. Is that right?

[Turner Novak]

Maybe. Yeah. It just depends.

[Jacqueline Chong]

Yeah, it depends. And you have to do, you know, like get warm intros.

[Turner Novak]

Yeah, they won't talk to you unless you get introduced by someone.

[Jacqueline Chong]

It is a little bit weird.

[Turner Novak]

Yeah.

[Jacqueline Chong]

Yeah. You have to do a warm intro. I was actually sitting there, I was like, do I need a warm intro to someone that I already know

[Turner Novak]

that

series A investor that you're meeting. They don't have an application, but there's 50 people that they might meet. And they get. We'll just say in one day, I'm exaggerating this a little bit, but there's 50 potential warm intros. But 20 of them, two people mentioned it, and for four of them, three people. And there's one series, a company that six people were like, hey, have you met blah, blah, blah yet? So when you're just squaring that up and you're doing meetings all day and calls all day, you're like, huh? Six people told me I should meet these guys. This one, only one person. And I highly value these six people. And I've never actually met this one person before, or I met them 18 years ago when we worked at Yahoo together. So when you just kind of think about how they're trying to figure out, I'm gonna spend time on things. Six people that I trust said I should meet this founder. So maybe that's the one that I will pick and prioritize.

[Jacqueline Chong]

Yeah.

[Turner Novak]

So it can be really hard to fight through the noise.

[Jacqueline Chong]

So, yeah, you spend basically a little bit of time. You have a spreadsheet, and you're like this investor who are all the people that know them. And then you go through each one, you're like, which one is the best one to ask for a warm intro? And then, anyway, you finally get the warm intro, and maybe you do a

[Turner Novak]

coffee chat, and they might say, jacqueline,

great to meet you.

Adding Anna to find time to chat next week or in two weeks or something.

[Jacqueline Chong]

Yep, Yep. And maybe it's a coffee chat. Maybe it's a zoom call. And then you. And then. And then, like, you just don't. It's not very. Like, the process is, like, different across funds. So it can be like two, three meetings. It can be six, seven, eight meetings until you get to an answer. And it can be, you know, like one to two weeks between meetings. Like, who knows? It just like, it just a lot more of a black box, and it goes on for a lot longer. Standard is just like, they. There's a formula that they follow, and they actually follow it. It actually is like, you know, after the deadline, within two weeks, everybody knows it's a yes or a no. And you don't have to, like, wonder. You don't have to wonder. Oh, that's the other thing. If a VC says no, it's probably just like, they'll just ghost you. So it's not like a hard no. So you have to kind of deduce that and figure it out.

[Turner Novak]

And then randomly, like, three weeks later, they'll be like, oh, hey, I was on vacation, but we should catch up. And it's just because you got a turnkey from someone else.

[Jacqueline Chong]

Yeah, yeah, yeah.

[Turner Novak]

And so. But you could say these are just kind of three guys. No one's heard of them before. There's some funds. They have a bunch of people on the team that will help you and, like, go join your board. How did you feel? Like, standard capital kind of matches up against maybe what you might get from a, like a. A board member who will show up to your meetings and, like, add all this value to you. And, like, they have a platform team with, like, all these people that will help you, like.

[Jacqueline Chong]

Yeah.

[Turner Novak]

How did you think through that?

[Jacqueline Chong]

Very different. So there's no board. They don't take a board seat. What they do is every quarter you do, like, group office hours.

[Turner Novak]

This is with other. Because you do batches, right? It's like, we funded six companies in a batch kind of.

[Jacqueline Chong]

Yes. So, like, every quarter they'll fund around, like, five or six companies.

[Turner Novak]

Okay.

[Jacqueline Chong]

And then each quarter they'll. I think presumably they'll like, move people around, but you'll have a group of founders where you're doing your. Your board meeting too.

[Turner Novak]

So it's just a bunch of other founders come to your board meeting.

[Jacqueline Chong]

And the idea is you learn a lot faster when you hear about other companies that are roughly in the same spot. What are the challenges that they're facing? How are they thinking about solving it? Because you're probably like, it's similar. Ish problems.

[Turner Novak]

So have you gone to some other board meetings now or not yet?

[Jacqueline Chong]

Yeah, yeah, we've done. We've done one so far. It was. It was with the entire group because it was just the first, like, cohort.

[Turner Novak]

Yeah. Did you all do all your board meetings all at the same time?

[Jacqueline Chong]

Yeah. Like, you literally go up, like, one by one, and you present your. You have, like, 10 minutes to present. Like, where are your. Like, what are your challenges? What do you need help on? And then we all talk about it together and. Yeah, I mean, I don't know what a normal board meeting looks like, but I thought that was, like, really helpful because we all are roughly the same, like, having the same problems.

[Turner Novak]

So it's all, like, different products and maybe like, industries and markets, but we all working on the same types of things.

[Jacqueline Chong]

Yeah, it's like, how do we build a team? How do we do. Like, we're all hiring salespeople now, how are you thinking about that? Like, what does marketing look like for all our different companies? The things that you don't think of, like pre product market fit that is necessary now we're all working together and brainstorming and then we all have a Slack channel and you can talk to all three of them whenever you need advice. You can choose to do recurring monthly chats with Dalton or something like that, because their whole model is, we don't need to be on your board to be helpful. We can just be helpful.

[Turner Novak]

Yeah. I've always had this weird personal feelings about for my strategy. Do I need to be a board member or whatever? There's also, man, that kind of seems like a pretty big burden. Got to be on the board of a company. I'm on the board of one company and. And one aspect. I don't do anything. I just text the founder a lot, but I don't actually do anything. You're on the board and you have to sign and approve things. You have read a document. And we do board meetings, but they're not on zoom at this point. It's one of the things. It sounds really daunting and you do a ton. But also, you don't really do anything at the same time, and you don't necessarily have to be on the board to do the same thing.

[Jacqueline Chong]

That was what I was going to bring up. If you weren't on the board, can you still still do all those things that you do as a board member?

[Turner Novak]

Like, I have your phone number and we just text about random things.

[Jacqueline Chong]

Yes.

[Turner Novak]

Maybe if I was like a board member, there'd be more pressure of, like, do you have to actually do the thing?

[Jacqueline Chong]

Oh, like proactively text.

[Turner Novak]

Yeah. But, like, I don't know. I feel like I do try to. When you do ask for something, I do try to help as much as I can. But also, what context does an investor have of the business to, like, truly actually help? Like, there's maybe a couple cases where you can actually move the needle. Like, hey, trying to do a podcast to announce a series A. Can I come on your podcast? Maybe that's a place I can actually tangibly move the needle. But also you're like, you know, there's always, like, recruiting. Like, everyone's like, oh, we help with recruiting. And there's. I think there's like, maybe you sit down with them and think through what a process might look like. Or maybe it's like, we have a recruiter on the team that works with you. Or I know someone who maybe could be a good fit, and I'll just, like, convince them they should join. Like, I'll help you sell it to them.

[Jacqueline Chong]

Yeah.

[Turner Novak]

Or maybe sometimes, you know, I see that the investor will, like, try to close a candidate that the. The founder and the team worked on. So there's like, all these different elements of ways you can kind of help with things, but again, you don't have to actually be on the board.

[Jacqueline Chong]

Yeah. I mean, our best investors, like, they all help with, like, customer intros. Like you said, we have a candidate, like, way down the funnel. We gave them an offer, were competing against, like, this other offer that they have from a startup. Yeah. Can you talk to some of our investors? And like, you know, those are all, like, very helpful things and referrals. I think an investor's network is probably the most, like, the most. One of the most helpful things there. And then, like, pattern matching.

[Turner Novak]

Right.

[Jacqueline Chong]

Like, if you guys have seen. If you guys have invested in like, a hundred thousand, maybe not 100,000 companies, but I have 10,000 companies. You know, like, you can kind of, like, tell us, like, what path we're like, we're thinking about doing this, and you're like, oh, well, I've seen this fail, like, 99 times out of a hundred. And this is exactly why this doesn't work out. It's, like, still good context to have. Like, we may still decide to do it because we're a little different from, like, that context, but it's good context to have. So you go in, like, eyes wide open.

[Turner Novak]

Yeah. That seems to be like, the value prop of YC is like, hey, we've actually had, like, three YC companies that did exactly the same thing. And you can talk to the founders of why they failed or someone actually did figure this out. Talk to them and see what they can do to help.

[Jacqueline Chong]

Yeah.

[Turner Novak]

So maybe last question. Do you have a favorite founder, CEO or business that you've learned a lot from or gotten inspiration from? Just when it comes to building rd?

[Jacqueline Chong]

Yes, I think we. One of the best. He's no longer alive. He's an author. One of the best books I've read in the last couple of years is the Score Takes Care of Itself.

[Turner Novak]

I've heard of that before, but who wrote it?

[Jacqueline Chong]

So I believe someone had to take over and write it for him because he passed away. But he was the coach for the 49ers, and it was during a period of time when the 49ers were doing, like, a really terrible job.

[Turner Novak]

This is Bill Walsh.

[Jacqueline Chong]

Yes, they Were like the worst, worst team in the NFL for, like, multiple seasons. And he took over. And within, I think it was like within two seasons, they won the super bowl, and then they continued to win the super bowl for, like, many years after. And it's his philosophy of how he ran his team and his standard of performance and how he implemented it. And a lot of it is directly translatable to running a business.

[Turner Novak]

What were the biggest takeaways from the book?

[Jacqueline Chong]

I mean, the biggest, biggest takeaway is the title. It's like the whole thing is about if you just focus on everything that you can control and you make sure that every single person on your team is performing to the highest standard of performance, and you have your standard of performance strictly written out for everybody. It was not just the players, not just the coaches, but up to the people, the janitors that worked in that building had a standard of performance. And if you control all of the inputs that you can control and you do a really good job, you don't have to care about the outcome. Like, the outcome will just happen.

[Turner Novak]

And there's a lot of takeaways, obviously, for building a company with that.

[Jacqueline Chong]

Yeah, yeah. It's like focusing on all the most important things. Focus on what you can control. Like, don't think too much about, like, whether or not this deal closes or this product is. If you focus so much on doing all the right things and you've done work that you're proud of, at the end of the day, you don't have to think about whether or not it was a good or a bad outcome. And then in the long run, it's almost for sure that you're going to have a good outcome rather than a bad one.

[Turner Novak]

Makes sense. Well, I'll throw a link in the description for the book if anyone wants to read it. Yeah, well, this is a lot of fun. Thanks for. Thanks for coming on the show.

[Jacqueline Chong]

Yeah, thank you for having me.

[Turner Novak]

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