She raised $20M from Accel to replace QuickBooks with AI. | Helen Hastings, Founder of Quanta

Helen Hastings (00:00:00) :
I think that a lot of people think that founders have this one aha moment where it suddenly becomes clear, but I actually do not think that is the case after talking with a lot of founders. I think it is more that you become so immersed in a space that you do not realize how much context you are gaining every day, and then suddenly you look back, and say, why does the world operate like this? When we first launched throughout 2025, we started growing consistently at twenty percent to sixty percent month over month. Which was really exciting and then we actually hit a point where we had way too many onboardings that we had to take a pause. Finding product market fit is like rolling a boulder up a hill. It is really hard but once you found product market fit, it is like the boulder is rolling down the hill and you are chasing to keep up with it.

Previous Guests (00:00:47) :
That is product market fit. Product market fit. Product market fit. I called it the product market fit question. Product market fit. Product market fit. Product market fit. Product market fit. I mean, the name of the show is Product Market Fit.

Pablo Srugo (00:00:59) :
Do you think the product market fit show, has product market fit? Because if you do, then there is something you just have to do. You have to take out your phone. You have to leave the show five stars. It lets us reach more founders, and it lets us get better guests. Thank you. Helen, welcome to the show.

Helen Hastings (00:01:15) :
Thank you. I'm glad to be here.

Pablo Srugo (00:01:16) :
I'm excited for this one. I mean, on the one hand, obviously from a fundraising perspective, things seem to be going really well. I just raised $15 million in Series A from Excel. But the thing I'm actually most excited about is that I've started to see more and more of these AI enabled services type of companies, which I think you fall into. So we had Tenex on here, which is effectively an AI enabled MSP, Managed Service Provider. We have Eudia coming on, which is an AI enabled law firm and it sounds like what you're building is an AI enabled accounting firm. And you'll probably dive into more what exactly it is but that's very, very high level. So we'll kind of get into all that and I think obviously there's big differences between building something like that. Which probably still involves some human in the loop, versus just a normal product. Whether it's an AI product or SaaS or whatever. So maybe as a starting point, you started this company in '22, '23. Maybe take us back to that time. Where were you right before you started, and what's kind of the origin story?

Helen Hastings (00:02:13) :
Sure, so yes, we got started in 2023, but I got started on user research in 2022, and did a ton of full time user research before really getting started on Quanta. And I got into this because of my background. So I'm actually a software engineer turned founder CEO. So computer science was my education at Stanford, and my whole career was software engineering. Specifically in fintech and specifically building financial systems. The term we used was financial systems of record, which means that it tracks all of the balances, all of the money across the system. But really it was building ledgers, and ledgers are also the core data structure of accounting. And I built one ledger in specific that really prepared me for Quanta, which was the in house accounting system at Affirm. Which was the place I worked at before Quanta. I joined when it was around one hundred people, got to grow it through over two thousand people through IPO and working on that in house accounting system with the accounting and finance team. And seeing how lacking the existing incumbent systems are really showed me that something better needs to exist here.

Pablo Srugo (00:03:28) :
And that's like you were building something. You guys, I assumed used traditional FP&A tools. Did you use QuickBooks? Where exactly does accounting system fall in relative to everything else?

Helen Hastings (00:03:37) :
Yes, so NetSuite was the primary tool. So, QuickBooks is the monopoly for early stage companies, and then eventually they switch to NetSuite. And really these systems are monopolies. That is what everyone is using in the U.S. They go through that path, but NetSuite cannot handle the scale of something like Affirm or any consumer company where you have, you are talking millions to even billions of money movement events throughout the day. And I saw that NetSuite alone just did not provide the visibility that is needed to know where is money moving? How much do we owe our counterparties? What is going to happen in the coming days? Really things that seem basic day to day questions and in fact, when I was doing user research in 2022. I saw that it was not just bigger, complex fintech's like Affirm that needed more financial visibility. It was actually smaller and seemingly simple SaaS companies that did not know how much money they were making. They did not know what their true revenue was. They did not know how much money their customers owed them and for how long they were overdue. And the root is the slow manual accounting systems. So that is what we wanted to change.

Pablo Srugo (00:04:50) :
When you started, you're talking about this user research period. Were you still at a firm or did you leave a firm to go and start interviewing clients?

Helen Hastings (00:04:57) :
I had left. I left a firm in early 2022. I am the sort of person that goes all in on things, and it would have been very hard for me to, in a full time job. Carve out the space to do the level of user research. It was so difficult for me to not give my all to the thing that I am working on.

Pablo Srugo (00:05:13) :
That part of user research, I think everybody knows you kind of have to do something but many founders I find. Especially first time founders, just kind of gloss over it, and they just always want to put in an MVP as quickly as possible. What did you do? Tell me more about that phase.

Helen Hastings (00:05:31) :
Yes, and it is so important. I certainly understand the inclination as an engineer myself. I just wanted to get going on building things, but that is not important in the early stage at all. The most important thing is, do people want this? And you do not find that out by building, by building beautiful software. You find that out by talking to people. So I spent many months of full time just slogging through LinkedIn, searching for titles I wanted to talk to. So that is controller, which is the head of accounting, or CFO, head of finance, accounting manager.

Pablo Srugo (00:06:04) :
At specific type of companies?

Helen Hastings (00:06:07) :
Yes, and in a specific type of company as well, which is software companies. For me, Quanta will eventually grow into non software but the power of network is really important and also, that is the space I was familiar with, and the problems that I saw. So by having the network, looking for mutual connections, I was able to talk to hundreds of these people and I really do not think I could have done that in another industry.

Pablo Srugo (00:06:29) :
Plus, it is probably where a firm is most recognized and known, and people would appreciate that you were there for five years. So you kind of know, you have that credibility in what you are talking about.

Helen Hastings (00:06:37) :
Exactly, yes and those people connected me with people I needed to talk to as well. And then eventually you build up the network, and you ask people you are chatting with, hey, can you connect me with someone else? And they do.

Pablo Srugo (00:06:47) :
So that is how you get in front of them. How are you structuring these conversations? And typically, what I found is there is kind of this funnel where your first conversations are very broad, and then you kind of narrow in. But how did you do it? 

Helen Hastings (00:06:57) :
So this was in 2022. If the AI era had truly taken off, I think I would have built more things with live coding tools. But it was a funnel starting with just conversations that were just questions. Hey, what are your day to day pain points? What is top of mind for you? If you could wave a magic wand and change anything about your role, what would it be? So starting there.

Pablo Srugo (00:07:18) :
So it was classic high level. It wasn't like you were going in with this pointed idea that you already had. You really were starting at the top.

Helen Hastings (00:07:23) :
Started at the top, yes, and then switched to the pointed idea, and then honed in after that. And there was a transition phase where the first half of the conversation is, hey, top of mind, before you bias them with your idea. People love to be helpful and once you have planted an idea in their mind, they just want to talk about that thing, even if it is not really a pain for them. So even when, if you are a founder doing user research, even if you think you have the perfect idea, always start with really what is the most painful thing for them and if they list a few things. And it has nothing to do with your idea or your problem, then that is really a sign.

Pablo Srugo (00:07:58) :
Were you finding that it was very aligned? What were some of the things that people were saying to you before you even planted anything in their heads?

Helen Hastings (00:08:04) :
Month End Close, which is the name for once a month, the accounting books are closed. Accounting operates really on a once a month, only twelve times a year cadence. Which is part of the root of the problem that we are working on at Quanta. So, how long and annoying that process is, and how little visibility finance folks at early stage companies have because of that. So they say, hey, I get ten questions, and I cannot answer any of them because I do not have the data. The clean data about my finances to understand that question. So, for example, the CEO comes to me and says, hey, what is causing this decrease in revenue last month? People do not have the answer to that question because the accounting work is getting done and even after it is done. It is often done in a lossy way. So the real contextual data is missing. Basically, that entire accounting process is causing these problems. So people do not say it like that. They just say, yeah, I have this problem. The CEO asked me a question, and I did not know the answer, and I had to spend all day solving it. And that is a big top of mind pain point for me.

Pablo Srugo (00:09:11) :
Is it usually like in this specific space, is it really just the timeliness of the data? The data gets there at some point, but it is just three weeks after month end you can figure out what happened last month. You are already trying to figure out what is going on this month. Is that the main problem? Or is it literally that even after the fact, there are questions that they just cannot answer? There is just no visibility.

Helen Hastings (00:09:30) :
It is both, it is two things. One, the timeliness, and then two, how lossy the processes are that produce the accounting data. So when I say lossy, an example is if you look in an accounting system and want to see what was my revenue in January. You might see just one single entry that says a million dollars and that is all it says, a million dollars. It does not say who are the customers, who churned last month, how much of that revenue came from upgrades, how much churn expansion, which cohorts were doing better, and who is switching between products, what new products are working well. All of that data is lost because that work is done usually in manual spreadsheets that live on someone's laptop and is done a little bit differently month over month. These are called month end closed workbooks, which are always an Excel sheet living on someone's laptop with a lot of tabs and because all of that data is lost. Then when you want to go back and say, hey, well, what was going on in that one million dollars of revenue last month? The answers are not there. They are lost in the manual spreadsheets and someone on finance has to go back into Stripe or back into the invoicing system, or back into the bank account to try to stitch all that data together and build up that context. And so the complaint comes in the form of the finance team just doing that work every day. Even though accounting is often some other world operating very behind and not producing data that can actually help the finance side of the house.

Pablo Srugo (00:10:58) :
How many of these conversations did you have?

Helen Hastings (00:10:59) :
I would say it was on the order of hundreds, and I'm still doing them also. So the number is always increasing, always talking to more people and learning. But it's certainly in the many hundreds.

Pablo Srugo (00:11:10) :
But before you build probably like a hundred or so?

Helen Hastings (00:11:12) :
I think maybe more like two hundred.

Pablo Srugo (00:11:13) :
OK, fifteen, thirty minutes each?

Helen Hastings (00:11:16) :
Thirty minutes. An hour if I could take it, if I could grab them for coffee.

Pablo Srugo (00:11:20) :
Right, as long as they gave you and over how many weeks or months did you do this?

Helen Hastings (00:11:24) :
Started in summer of 2022, and did it well into mid 2023. So it was a long time, but in 2023 I started building at the same time.

Pablo Srugo (00:11:36) :
Did you find anything out from here, besides just validating what you already knew. Were there any big aha moments that you remember? Whether it's a conversation or a pattern that emerged out of all these conversations that led to what you ultimately decided to build?

Helen Hastings (00:11:49) :
I think that a lot of people think that founders have this one aha moment. One clever eureka thought where it suddenly becomes clear, but I actually do not think that is the case after talking with a lot of founders. I think it is more that you become so immersed in a space that you do not realize how much context you are gaining every day, and then suddenly you look back and say, why does the world operate like this? This is crazy, I think it should operate a different way because of what I have seen every day and what just makes sense to me. So I think that was more of the case after being in it for a long time. I look back and realize I think I need to change some things. It is going to, it feels obvious to me that something needs to exist that does not exist today and that will make every single company's life easier. Give them visibility that they do not have today. Make today feel this crazy past where, I think a metaphor I like to use is for the internet, you had to go to the library to answer any history question, but now you can just Google it. I think that is truly how financial analysis is going to feel in a shift change, where today it feels like going to the library. Teams have to do two weeks of analysis work to answer a single question from the CEO and after a system like Quanta has taken over. It will feel as easy as just Googling it, having it at your fingertips. So that feeling came not all at once in a eureka moment, but it came over time from having all of these conversations and knowing if I build things in the way that I have built them before. I can reveal these answers. The data is there, I can just build something to organize it and put those answers at the fingertips of my customers.

Pablo Srugo (00:13:28) :
For what it is worth, it just drives me absolutely insane just working with companies, and we are trying to figure out things that are sometimes actually unanswerable. You have got to figure out, should you go here, should you go there, and you do not really know what to expect. On top of that, you do not even know what happened last month. Even though it is fully answerable, and you are like, three weeks later. And it is just absurd that you have that problem on top of it. There is one problem that is actually unsolvable, and you just have to kind of guesstimate. And then there is another problem. You are like, this has to be solved. So I totally get where you are coming at it from. At the beginning, you were doing this alone. When did you bring in a second person into Quanta?

Helen Hastings (00:14:02) :
That was the end of 2023. Hired an engineer and a designer.

Pablo Srugo (00:14:07) :
End of '23?

Helen Hastings (00:14:08) :
Yes.

Pablo Srugo (00:14:09) :
So like eighteen months it was just you?

Helen Hastings (00:14:11) :
Well, I actually did co-founder dating, and I was doing user research with potential co founders. And honestly, that really helped to have someone doing it with me. But those were just early stage potential research, and ultimately, it just made the most sense for me to do this. And so I decided to go it alone.

Pablo Srugo (00:14:30) :
When did you decide to raise the first round?

Helen Hastings (00:14:33) :
This was in 2023. Honestly, there was never one moment of, oh, I have perfect conviction, now I get to raise. I feel very grateful that I had a wonderful network of incredible venture capitalists, partners that I knew from previous parts of my career who followed along with the journey and honestly saw that I had conviction before even I knew that I did. Because they have seen this vote before, and I am very grateful for that. Again, I think I had this misconception of founders having everything figured out before they even raise their first dollar. But actually you are figuring out a lot of it as you go, and that is really the reality even if people like to act like they have it all figured out.

Pablo Srugo (00:15:14) :
Was that a pre-seed or a seed the first round and how much was it?

Helen Hastings (00:15:17) :
A seed that was $4.7 million.

Pablo Srugo (00:15:19) :
Did you have other people or that was just like you were still alone at that point?

Helen Hastings (00:15:22) :
There were no employees, yeah. Pre-product, pre-revenue.

Pablo Srugo (00:15:26) :
That is very uncommon, for what it is worth. I mean, because first of all, being a solo founder already, it is uncommon, right? Most are probably two or three. Not that one performs better than the other, but just in terms of statistics and then the fact that you would be alone raising like $4.7 million is pretty wild. Actually, I am curious, was it purely inbound? Did you run a process? How did it happen?

Helen Hastings (00:15:48) :
Again, it was from the partner that I worked with at Excel, who actually led our Series A as well and I had known from before working on Quanta. He actually used to be the manager many years ago for one of my managers when I was at a firm. So my manager, she and I had an amazing relationship, worked together for a few years, and she had introduced me to this partner at Excel. I want to say in 2019, when I owned all of developer experience at a firm. We had these engineering goals, and we were a very large team at the time and I owned one goal, which was making us go faster. I had an engineering team and needed to go and look at all the tools that were out there in DevTools. And my manager introduced me to this partner at Excel, who is now on my board, to learn about developer tools since he was investing in the space and we just built this relationship from there. And it is also his job to keep in touch with high potential people. And he just followed along with the whole journey, all the user research through the co-founder dating and potential co founders as well. So that relationship really helped and also, of course, I was talking to other people as well. The venture capitalists love to jump on engineers doing research and trying to start something new. And so just seeing that journey following along was what led it to happen.

Pablo Srugo (00:17:12) :
And then going back to the beginning of 2023, you start to build a product. What is the first version of what you build?

Helen Hastings (00:17:17) :
Honestly, the real building did not start until mid to end of 2023. In early 2023, it was still a lot of prototypes, user research. Actually, the first design partner was something that no longer exists because that was not the right thing. But we really got started building in late 2023, and we had design partners then too. So our first design partners, we actually did not have a full product built. We were partnering with another accounting firm, who was doing a lot of the work behind the scenes. So I believe that the best way to get started with design partners is not by having everything built first, but it is getting that MVP. That working experience, and so we started building while also partnering with another firm behind the scenes. But come early 2024, we switched completely to the product that we had built in house.

Pablo Srugo (00:18:09) :
Well, tell me about that. That phase of working with design partners and learning through that is obviously, hugely informative as well. And you mentioned you started with a different product, is that what I understood? With the first design partners?

Helen Hastings (00:18:19) :
Yes, there was one design partner on something that really did not even do the accounting services. It was just a product that provided full visibility into the finances and this is just a way to show how messy the initial process is, right? I knew what I wanted to build long term, but the initial wedge into the space that will start making revenue and build enough momentum to raise the next round. That was really the hardest thing for me in this space. So there we had one design partner on something that just provided some extra visibility into the finances, but then I quickly realized that that is not something that is going to sell sustainably. So then we brought on three design partners that had the full accounting services and it was actually on QuickBooks. And partnering with other outsourced accountants behind the scenes while we built something that would do the work ourselves. And then once we really understood the full picture by doing that partnership, and by building at the same time. Then we were able to switch onto fully the Quanta product and Quanta in house accounting in early 2024.

Pablo Srugo (00:19:24) :
Let's zoom in on those three design partners. First, maybe just how did you structure it? Did they pay? Did you ask for a certain amount of time? Was it just get their data and then that is good enough? How did you set that up?

Helen Hastings (00:19:36) :
Paying for sure. Paying from the beginning. I think that is really important to make sure that the customer says this thing is valuable enough for me to pay. But in the contract, there was also a clause of the design partner is committed to giving feedback. Maybe a bi weekly call or a monthly call, and there is a discount for being a design partner as well. But the discount had a time limit, I believe six months or one year, depending on the contract.

Pablo Srugo (00:20:05) :
What were you delivering?You mentioned, you're working with an outsourced accounting firm. What were you doing differently than any other accounting firm at that point?

Helen Hastings (00:20:12) :
So the thing that was being delivered was the accounting services, was the books, but also with the understanding that a better product was coming to solve those problems of the visibility into the day to day numbers and actually having useful data instead of just these accounting closed books compliance checkboxes. So the status quo here, I have to clarify, is just so bad for outsourced bookkeeping. People hate their providers. They see all of these errors. There is just so much human error. The main firms that do this are offshoring, and by no fault of the bookkeepers, they just do not know the latest SaaS trends, the latest tools, they do not know the difference between AWS and buying some office furniture off of Amazon. And hitting on the pain of people being really unhappy with their outsourced bookkeepers was a great opportunity to say, hey, you know, we can do this better. So it did not need to create a new category, no user education on, or no saying, hey, you need a new tool, you need something new. I was able to say, you have this service, you know it is bad, you know it costs you problems, let's rip and replace that, we can do it better.

Pablo Srugo (00:21:26) :
And at the beginning it was just, you're just doing better than any other service provider could in theory do better. You're just executing better than others.

Helen Hastings (00:21:32) :
That is part of it, yes, but then the product side as well of you also get this product. Where you can actually really understand what's going on in your business and not have to do a bunch of work to do your investor reporting, board reporting, what's changing in my revenue, all of those finance KTIs.

Pablo Srugo (00:21:48) :
Maybe let's take a little tangent and go on this AI enabled service kind of space. And obviously that is where Quanta fits. What drives you to go down this road with services? You actually start with almost just services and then you add product over time. And obviously you are going to productize more and more, but there is probably always going to be some services element to it. I find a lot of founders are still very hesitant to do that. In a perfect world, they just want to sell software because software has the best gross margins. It is the easiest thing to scale. You do not have to worry about hiring people to do service type work. But you decided almost from the outset that this was the way that you were going to do it. How come, or how do you think about all that?

Helen Hastings (00:22:27) :
I understand the hesitancy, especially of founders with an engineering background. Because I love building software. I love it, it is a lot more satisfying than doing manual service work. However, one thing I have realized as a founder is you just have to deliver a product that people want and the reality is that early stage companies, they do not want accounting software. They just want the work to be done. They want the peace of mind. They want it off their plate and that is what a service is. And as a founder, you do not get to build the beautiful, pure initial thing that you really wanted. You have to build something that solves a problem for customers and for me. My vision, that of course, we are still working on for the long term, is to be the financial source of truth for businesses. Where it is not just the accounting data, it is all the finance data that we have been talking about too. This does not exist, by the way. There is no single tool that both finance and accounting work out of that is the live data source for day to day decisions. So I knew I wanted to build that, and I knew that that needed to exist. And then the question was, how do I get there? So it was very important to me to be that full picture of the finances from day one. I do not believe that you can start as a point solution and eventually take it all over. You need to be the full source of truth from day one and we also have to build this from scratch. I am also a first time founder that cannot raise tens of millions from the beginning. So how do you get there? So figuring out which was the most important thing and that meant selling to tiny companies because we could be the full source of truth from the beginning. And what do tiny companies want? Again, they do not want accounting software. They just want the work done and by doing the work, by doing the services, I knew that we could build a better solution than ever existed. Because we can actually automate all of the work, not just replace QuickBooks, not just build a shinier QuickBooks or NetSuite. But build something fundamentally different that automated all the work that bookkeepers are doing today and when LLMs came onto the scene in 2022. A lot of that was that last missing piece because I was seeing the work and saw that a lot of the bottleneck was human eyes reading text, reading memos, contracts, receipts, human generated language, or navigating browsers. These are things that I saw that LLMs could do. So that missing piece came together, and services was the way to go.

Pablo Srugo (00:24:53) :
I am going to ask you for a small favor, a tiny little favor. In fact, it is not even now that I think about it. It is not even really a favor for me. I am actually trying to help you do a favor for you. Just hit the follow button. You will not miss out on the next episode. You will see everything that we release. If you do not want to listen to an episode, you just skip it. But at least you do not miss out. This is what I really like about AI enabled services. Which is that in most scenarios where you are creating a new company, frankly, your biggest worry, unless it is deep tech, is generating demand. Is, yeah, you will build a product, but how do you actually get leads, how do you close leads, how do you generate interest? Are people really going to want this thing, right? To your point before? When you are building AI enabled services, there is no doubt about demand. The existing demand is not just there. It is massive. You talk about every single company needing accounting services. Literally every single one, especially early stage companies. They are not going to hire a full time accountant. They need accounting services for sure and so that is not a question. The only question becomes, how much of that delivery of services, the manual services, will you actually be able to automate and productize? And I think that is where pre-gen AI, because there were companies that tried to do things like this before. In many cases, it turned out that you actually could not automate or productize nearly enough of it and so you just became kind of an at scale manual service provider. Which is very hard to actually scale and very hard to fund through VC dollars. Post-gen AI, there is a much renewed opportunity to be able to truly automate and productize a lot of that delivery so that you might not end up with eighty five or ninety percent gross margins, but you could end up with sixty or seventy percent gross margins and get all the scale as well. So maybe as a follow up to that, as you are doing this with these design partners, what are the first pieces that you start to productize and start to automate?

Helen Hastings (00:26:44) :
I said you make a bunch of great points there and the points you make actually do dictate a different way of building and thinking about growth. So we can get more into that too. But to answer your question, the first thing that we really built was the customer facing parts of it. So the visualizations, what the customer was interacting with, so that we could test that early on. Again, I think putting things in front of customers, building with users, seeing what they like and what they do not like is the most important thing. So we built this visualization layer first.

Pablo Srugo (00:27:17) :
And what was that, like a dashboard sort of thing? Or what's the visualization?

Helen Hastings (00:27:20) :
Yeah, it was a dashboard, a way to drill into the different parts of the dashboard, better visualizations of your financial statements, searchability to search across everything. So that was the first thing that we built and then after getting that out the door, we then started going deep and working on the foundation.

Pablo Srugo (00:27:40) :
And the ledger, is that where you're rebuilding something like QuickBooks?

Helen Hastings (00:27:43) :
Yes, yes, so we fully replaced QuickBooks. So the majority of our customers have no QuickBooks at all. For some of them, we do offer what we call a QuickBooks Sync or a backup. Which is for new customers. They can keep their QuickBooks in sync with Quanta. It is a very, very risk averse user base that we are selling to. So we let them just keep their QuickBooks in sync. But typically after a few months, once they realize everything is OK, I do not need QuickBooks anymore. Then they can shut it down.

Pablo Srugo (00:28:12) :
This is also the crazy thing about Gen AI, which is a lot of these playbooks that really were just not. It was a bad idea before. For example, I am going to rebuild QuickBooks. We had Serval on here before, and they were like, yeah, we are rebuilding ServiceNow, right? And three years ago, it would have been, dude, you do not want. Find a different way to just build AI on top of it. You do not want to rebuild a twenty year old, legacy, deeply entrenched OS, effectively, right? And the same thing with QuickBooks. So I will ask you that question, was there an option to build on top? Have things changed so much that it is just, quote unquote, easy now to rebuild these massive, massive category defining platforms?

Helen Hastings (00:28:50) :
It is not easy. I will tell you that. QuickBooks is not something you can vibe code. The numbers need to be accurate. The foundation needs to be robust. The properties need to be built to scale and who knows, maybe Gen AI will get better in many years. But it cannot build something with that level of architecture, interconnectedness, and foresight. Our engineers certainly use the AI coding tools, but they are the ones architecting what needs to be built. So this took us a very long time. We built it throughout all of 2024. We did not even do a public launch. We did not launch until the beginning of 2025. We brought on more and more design partners. We had, I think, a couple dozen paying customers, but we were intentional about who we brought on while we built things out and we were not really even ready to open the floodgates until early 2025. So there was a lot of building first before we were able to launch.

Pablo Srugo (00:29:51) :
But still, I mean, a year to rebuild QuickBooks is like relatively fast. You think that would have been doable three, four years ago before all the AI tools?

Helen Hastings (00:29:59) :
I think with the right team, it would have. We were definitely accelerated, but I think the big difference was knowing that. For me, it was knowing that I have a path, right? Seeing what the full service accounting looked like and seeing, hey, there are these things with human eyes being done, navigating browsers that do not have APIs, reading all this human generated text, and I saw it. We actually have a path to automate this now, so this suddenly is not as crazy. This does not mean on day one, I am going to have that all built, but it means I feel the confidence to start on this path and even if it means we are manually doing some things to start. It is no longer crazy to embark on this journey and as a VC. You are no longer crazy to fund that because the path is very clear now.

Pablo Srugo (00:30:44) :
Bench accounting is the obvious one that comes at least to my mind of somebody that tried to do this kind of at least tech enabled, let's say accounting platform ultimately shut down. What do you think has changed like post-gen AI that now gives you the why now is like better to build something like Quanta today?

Helen Hastings (00:31:01) :
They were certainly before their time. They started many years ago, went through a founder getting pushed out and they just had a very large team of people. Very large team of people and I actually got a peek into what their plans to monetize were because they were very, very margin negative on their accounting. And it was actually things like we are going to introduce banks and expense cards. They were not actually trying to automate the work of all of the staff that they had. I think maybe if they had started ten years later, it would have been a different story. But just at that point, when you have built up all of this corporate culture, that entire team, that momentum, things are just in a very different state from when you get to start from scratch in the AI era, like we do.

Pablo Srugo (00:31:47) :
So fast forward, it's like beginning of 2025. How many people on the team now?

Helen Hastings (00:31:50) :
Actually, going into 2025, we only had six people, and now we are at fifteen.

Pablo Srugo (00:31:57) :
Was it you and five engineers?

Helen Hastings (00:31:59) :
No, it was three engineers, a designer, and someone who has an accounting background and was kind of doing everything.

Pablo Srugo (00:32:07) :
OK, but a hundred product, there's no basically sales and marketing, go to market people on the team at that point.

Helen Hastings (00:32:12) :
Exactly, yes. We now have two go to market people post raising the Series A. But going into 2025, no one dedicated to go to market.

Pablo Srugo (00:32:20) :
And where were things I'm just trying to get a sense of context before this kind of public launch. How many design partners did you have for example?

Helen Hastings (00:32:28) :
I think we had a couple dozen by then and again all paying.

Pablo Srugo (00:32:32) :
And were they paying a few thousand a month I would assume for full accounting services?

Helen Hastings (00:32:37) :
That was probably that at the upper bound. We had some a lot less than that as well but probably on the upper end several thousand a month.

Pablo Srugo (00:32:43) :
So let’s say ten to twenty thousand kind of ACV. So tell me a little bit about, maybe actually before we get into the public launch. Where did you get your product to before you did the public launch? What made you feel you were ready for it?

Helen Hastings (00:32:56) :
It was our ability to take on a lot of customers at once and actually, looking back on it. I think we could have done more to be prepared, but maybe that is always how it feels at this early stage. You should go a little bit before you are ready. We were still doing things manually. Again, service provider. It was funny at the beginning when it was just engineering. All the engineers were going through and looking at all the credit card purchases, all the reimbursements, and kind of figuring out what they were manually as we were building. We had this term we called engineers as bookkeepers, which was the engineers are doing the bookkeeping. But I really think that is the best way to build an automated product. Because the engineers have to understand the domain very, very deeply. So in 2024, we still had all these manual edge cases that if we had brought on a ton of customers, we would have fallen apart. We would have been moving slowly and it took us a while to build that automation. And at the beginning of 2025, we felt ready enough that we could take on a lot of customers and not fall over.

Pablo Srugo (00:33:57) :
What parts were at that point at launch? What parts were still manual and what parts were like fully automated?

Helen Hastings (00:34:03) :
The edge cases were very manual. We had a way of building at the beginning in the code. if something ever hit an edge case, we would actually just Slack someone on the team. Sorry, a Slack thing of, hey, you have to handle this. Which, coming from my previous experience at a firm. Where you have to think as an engineer, how do you handle all the edge cases? How do you automate them? It was such a mindset shift for me, building for a couple of customers and knowing, hey, I do not need to think through every possible edge case. There is a very, very low likelihood we will have to handle this. I am just going to write it into the code that it will hang somewhat on Slack if they have to handle it and that is OK. Maybe one in a hundred of these things happened. But then they started to happen more.

Pablo Srugo (00:34:47) :
What's like an example of a typical edge case?

Helen Hastings (00:34:50) :
Yeah, it might be a payroll run where the company gets more money in than it sends out. Which seems like it would not happen but actually it might happen if the employee quits mid period and needs to refund or you get a tax refund, or something like that. Or maybe at the beginning, it would be something like if the employee’s reimbursement was rejected partially, but a partial amount was approved. There were just a billion things like this in accounting, because we took on a very hard problem, which is, we are going to do all your accounting. Which means we are on the hook. If you do something that we did not know about, or you do something new. We have to handle it, which is a really bold thing to be able to do. That is not how traditional software should be. If a customer has a new feature request, you can say, I got it on the roadmap for six months from now. But basically for us, if we hit an edge case that should be a new feature, we were on the hook to handle it. Because we committed to doing all of the accounting and that is what being an AI enabled service is like. That is the difference. So we were on the hook to get everything done. Rippling is another example where they are just always changing what countries they support. Oh man, we have never seen Singapore before, and now we have to know what that looks like. Oh man, all these benefits in France that no one gets in the U.S. We have to do the accounting for those and the way we learned about those was just someone started doing it and we see it come through the data. Oh man, this person hired someone in France. We got to figure out how to do the accounting for that and it was pretty chaotic to be sure. But that is what AI enabled services are and so every time that would come up, someone would just get a ping, and we had to deal with it. And there was so much of that at the beginning that we knew we were not ready to open the floodgates yet. We had to get a better understanding of what all of these edge cases are before we were ready to launch publicly.

Pablo Srugo (00:36:43) :
And what about the relationship piece? When you have a service provider, like an accountant. One of the upsides is you have somebody that you can ask whatever, whenever, in whatever way you want to do it. Is that still enabled with Quanta? How does that work?

Helen Hastings (00:36:57) :
Yes, that is still enabled. That is a big part of what our customers want. We have a Slack channel for every single customer. For, up until very recently, the entire team was in it so that they could all learn. That is another great thing about AI enabled services, customers feel very comfortable talking with you because they expect there to be a human relationship. So we get great feature requests all the time just by hearing the questions that our customers ask and for a while, it was mainly me being the accountant for every single one of our customers. And now, thankfully, I have a great team that bears the brunt of that. But I Slack my customers all the time.

Pablo Srugo (00:37:35) :
But those are people, like there are people on the other side of that? 

Helen Hastings (00:37:37) :
Yes. 

Pablo Srugo (00:37:38) :
Do you think that part is always going to be human or is that also a piece you want to automate over time?

Helen Hastings (00:37:43) :
I think it depends on the customer size. I think a good analogy is TurboTax. Before TurboTax, everyone thought a human needed to do the work. But now we are very comfortable with TurboTax, just filling out the tax returns, just the software and if we have any edge cases, we buy the hour, let’s talk to a CPA. I think that is where bookkeeping at the lower end of the market is moving, where people will eventually be more comfortable with the software just handling everything as they see that it works, and then they pay hourly for any edge case specific situations they are in. Hey, can I hire this person in this country? Hey, we are in this weird tax situation since I moved from a different country, that sort of thing and then Quanta is also building software that works with in house companies. Once you get big enough, you need someone who really understands the business. What we say is we automate all the non creative work, but there is creative work that will always exist in accounting and finance as companies get bigger.

Pablo Srugo (00:38:44) :
Tell me about the launch, how'd you set it up and how'd it go?

Helen Hastings (00:38:47) :
Let’s see, so this is the beginning of 2025. We set it up just to put the word out there. You know, there really was not anything crazy from the technical side. Of course, we had set up all the monitoring, and we had a plan to make sure that we were ready for the load that was going to come in. But really, the biggest difference is just starting to talk about yourselves, doing the big announcement. What I did that I am very happy I did was I timed it with announcing the seed funding. Even though the seed funding was in 2023, I waited until the beginning of 2025 to announce it. It was hard in the meantime, right? It meant that I could not talk publicly about things in a way I could have otherwise and maybe we could have gotten more momentum earlier, but I really think it was the most important thing to weigh. And I am glad that I did, but it was a hard decision.

Pablo Srugo (00:39:34) :
It's easier to get PR for a funding announcement than a launch. Often enough, and so that kind of puts two in one.

Helen Hastings (00:39:41) :
Exactly, that is why I waited. Because you get all these natural, organic eyeballs from, hey, we are a new company that just raised our first round. People look at that, and then they see, OK, hey, there is this new product that is only ready to go to market now.

Pablo Srugo (00:39:56) :
And how did it go? So you do that, you get some PR, you probably post on Twitter or whatever else. Did you do product hunt or something like that?

Helen Hastings (00:40:02) :
Yes, we did product hunt, although looking back on it. Product hunt is not the best for services.

Pablo Srugo (00:40:06) :
Yeah, I would say. Yeah, it's true.

Helen Hastings (00:40:08) :
Product hunt is great for an indie, small product that any consumer can just use. I'm glad we did just for extra invisibility, but I'm not going to pretend that we got a ton of traction there.

Pablo Srugo (00:40:18) :
And what happened? Did you get a bunch of demand as a result of the launch or was it slower?

Helen Hastings (00:40:23) :
It was slower as we expected it to be. Again, we are services, and we do all of the accounting. This is not a new vibe coding tool or a new consumer product that anyone can just start using immediately. You have to either say, hey, I am a new company that needs accounting, or, hey, I am a company that already has accounting and my contract is up to be able to use us. So we did see a huge influx in demo requests, and we started getting a lot of new conversations that we would not have had before. But as a services business, you do not get a ton of new contracts all on one day. So it really did come in over time and it actually got to a point where it was too many. And it was too many onboardings for us. And we even had to slow it down a little bit. But it did not all come in on day one.

Pablo Srugo (00:41:12) :
I actually want to dive into go to market for a bit, but before I go into that. How are you positioning the value proposition? Is it accounting for cheaper? Is it accounting that is better? Is it faster? Where are you focusing when you talk to somebody about switching over to Quanta?

Helen Hastings (00:41:29) :
Faster, the status quo is incredibly slow. We are talking four to sometimes eight weeks late, at which point your data is completely useless. So it is faster. It is higher quality because of not only the automation we have built, but because the team that you work with is actually our in house, in person San Francisco team over Slack. The status quo is that you are matched with some offshore bookkeeper and what you get when you replace the offshore labor with AI automation is that we can afford to have this expert in house team handle a huge number of customers. So it is the quality, the team, and the ability to see into your finances in a way that you could not before if the data was coming in four to eight weeks late. For example, a couple of our early customers said that they never looked at their accounting data before Quanta. Which is actually pretty standard because it is so late that it is already useless. Companies move way too fast to work on that time scale. But once they started using Quanta, they said, I am in here multiple times a week. This data is suddenly useful for me in a way that it was never useful before.

Pablo Srugo (00:42:38) :
It makes sense. It is also, when I think about the portfolio companies I work with. It's almost like you tend to have a spreadsheet that is not true accounting. But it gives you, I mean, let’s say, the deals you just closed or your MRR, these kinds of non GAAP things and that is the thing you look at. The accounting stuff, like the true balance sheet, cash flow statement, P and L, whatever, and also the verified MRR. Where it is like, yes, this is actually the thing now that we have figured everything out, all the edge cases, tends to just numb. By the time that comes out, you are really onto the next thing.

Helen Hastings (00:43:06) :
Exactly, and that is actually a huge pain point as companies get bigger. That sometimes those metrics do not match the accounting data and later on the CFO might realize, I have reported something to the board that is completely different from what is in my P&L, and that is a huge problem. And so what we are doing differently at Quanta is we are building all of that out of the same foundation so that there are none of these scares and scrambles later on, where a CEO realizes, I reported the wrong thing to the board. Because the accounting was so behind, the finance team had to scramble to keep up and then they realized, oh, man, we are using completely different methodologies. Because at the end of the day, the accounting data is the data. That is how much revenue you are making. That is what is recorded in 10Ks, recorded publicly and there is a reason for that, right? Accounting exists to be the language of business, to be the standard metrics that every business can compare themselves to. So it is a huge problem if your operational metrics diverge from those, and finance departments spend so much time trying to reconcile between the two, and we are solving that problem.

Pablo Srugo (00:44:12) :
And how do you price relative to existing service providers? Do you match? Do you come in a little cheaper? A little bit higher, typically?

Helen Hastings (00:44:18) :
We are pretty similar, actually, and we have gone back and forth a lot about this internally. And I have gone back and forth about it with myself. And you have this dual problem of, we are higher quality and we are better. So you think that we should be able to price more. However, people say, well, with your competitors, I am paying for a team of humans and for you, I am not. So why should you be charging me more? So I will be candid and say we are still experimenting with pricing, and we will probably change things in the future, but right now it is very standard. But we are the no brainer, if you can compare a side by side to pricing, but we are better.

Pablo Srugo (00:44:59) :
Well, that's what I like, for me at least, on paper. Coming in the same or even ten percent cheaper just to make it a complete no brainer where it's like, listen, instead of taking three weeks, we'll take three days. You have no errors and by the way, it's the same price. And the thing is, this is what I also like about AI enabled service. Which is a lot of the other AI products are replacing employees, and that's always much harder. If you go into a company and say, hey, you've got ten people doing XYZ work. Now with my product you'll need two people, the company's like, OK, but now I have to go fire eight full time employees. Which, you know, is just not that simple or easy to do. With an external service provider, there's just not that affinity. They're also on a contract, so it's like, yeah, I don't really care, frankly, who does my books. Maybe in an edge case, you have some relationship, but most of the time, you're like, as long as the work is done. Like you mentioned earlier, and it's done well, I don't really care who does it. So if you're going to do it faster and you're going to do it better. Why wouldn't I switch? It becomes that kind of no brainer.

Helen Hastings (00:45:59) :
Exactly, yes.

Pablo Srugo (00:46:00) :
So tell me a little bit about go to market. How over the last year that you've been publicly selling, what has worked the best and how do you structure that in outbound, inbound. What's the setup?

Helen Hastings (00:46:12) :
So it's been a lot of founder led sales for sure and only late last year did I hire or first do a go to market hire. So a full stack marketer as well as an SDR that is also a go to market engineer. So we're doing both at once and with the one exception, that someone from the accounting operations team helps out with sales. Because these are domain experts also, right? And if you're selling accounting services, prospects like to talk to CPAs that have done this before. So there's that trust element as well. As we'll deploy that team within sales, but really organic and one order of network removed has been the biggest driver for us. So our referral program, having our customers refer others, has been huge and our investor networks, and social media have all been very big for us. Trust is very important in the accounting and finance space. We're still building our brand. We just announced the Series A at the end of 2025. So throughout a lot of 2025, building that trust for something as important as accounting is really a big bridge to gap. So cold email only does so much, but proving, hey, you have someone one order removed that has seen how good we are and can back channel that they're actually higher quality, they're actually faster. Because a lot of companies lie about this. They say they're high quality, they say they're fast, and they're not. It's a very hard thing to prove. So that organic base and that one order removed has been really great for us so far.

Pablo Srugo (00:47:51) :
But are you doing something like in your emails to cite other customers, or are you just relying on. Especially let's say the first half of 2025, mainly when it was just you selling, was it just organic? When you say organic, it was just word of mouth that was happening, or were you doing something to go out to a customer and say, hey, I know you know XYZ. They're actually using our product and they love it, you should too?

Helen Hastings (00:48:11) :
It was a combination, but actually what worked the best was the truly organic, where my customers were telling their friends. So we saw a lot of growth from little communities. So the Harvard Business School founder WhatsApp group or something like that. Where once one person is a Quanta user, when all of their friends ask, hey, who are you using? Someone says Quanta. This is a very social proof based community, accounting, and finance. And so seeing that others are using Quanta is a big lift. And we're doing more case studies now that we have that marketer. So that social proof is the biggest thing that we'll be leaning into.

Pablo Srugo (00:48:49) :
Walk me through, because when you have it and it's coming, and it's inbound, it's great. Then when you want to dial it up and get more of it. So case studies is one thing that you're doing. What else are you doing to accelerate the referrals, accelerate the word of mouth?

Helen Hastings (00:49:02) :
So social media and outbound email. Outbound email, and outbound LinkedIn messages are our outbound motion. But again, I'll be honest and say it works the best if there's already been some sort of touch point where they've heard about Quanta.

Pablo Srugo (00:49:17) :
Do you have a formal referral program? Somebody refers, they get X month free or something like that?

Helen Hastings (00:49:21) :
Yes, so we built that pretty quickly. A custom form that's linked into a Notion database that we send out to everyone and same with our investors as well. Having that dedicated place for referral discounts. A lot of investors have a discounts page. Same with our ecosystem partners. So we work very closely with the financial tools Brex, Mercury, and we have discounts on those pages as well.

Pablo Srugo (00:49:44) :
And now these days, are you mainly still in the 10 to 20K ACVs or have you moved up market?

Helen Hastings (00:49:50) :
We have moved up market, but we've also gained a lot of customers on the lower end as well. So we're still in, I'd say it's more of the 15 to 25 at this point, but it is going up and up as we build more.

Pablo Srugo (00:50:04) :
How fast did you hit like a millionaire ARR?

Helen Hastings (00:50:06) :
The thing I'll say about growth, because I know that people love to talk about the speed. Is as an AI enabled services business, sometimes growth is not the only thing to strive for. Because we have this team that is doing some manual work around the edges, and because we are always a little bit ahead of what we can fully automate. That's the way we build new things. We take on someone that has the thing we know we need to automate, but we haven't automated it yet. So let's take them on as a customer, and then we'll automate it while seeing what real data looks like. So we're always doing a bit of manual work around the edges. Which means it's very easy to say yes to a customer in a sales call that we really can't handle yet and I'll be honest, I got us into a few situations where I said yes to too many customers. Because I was trying to drive revenue up as fast as possible and then that actually slowed us down. It slowed down our revenue growth because all of a sudden the engineering team is scrambling to handle all these customers that our relationship said yes to and that has slowed down our ability to build new products. So when we first launched throughout 2025, we started growing consistently at twenty to sixty percent month over month. Which was really exciting and then we actually hit a point where we had way too many onboardings that we had to take a pause. So that is just one downside of being a services business is sometimes saying yes to every single customer is not a good thing. Sure, I could have hired a bunch of outsourced people to handle them manually, but then we're never going to deliver on what our goal is, which is actually automating all of this.

Pablo Srugo (00:51:47) :
It makes sense. I mean, again, you know there's existing demand. So either you find only perfect ICPs with one hundred percent coverage and you add as many of those as possible. Which is not easy to do at the outset until they have a conversation, or you focus more on building an automated product in order to increase that coverage and then once you have a certain amount of edge case coverage. Then you can go all out. But if you do that a little too early, like you said. I mean, you're going to just basically implode your business, and you're not really de-risking anything because you know that there's a huge market for accounting services.

Helen Hastings (00:52:20) :
Exactly, and I think maybe a different founder could have done it a different way. Decided, hey, we're going to hire a bunch of people really quickly to just scramble to do it manually. But that's not the type of company that I'm trying to build. But it is tough when a customer is knocking on your door saying, I want to use you. Please let me give you money, take my money and you have to say no. It is really hard to do, but it is the best thing in the long run.

Pablo Srugo (00:52:46) :
How did the Series A happen? Was that also kind of relationship or did you run a process for that?

Helen Hastings (00:52:51) :
I want to really say that was a preemptive offer, and I loved working with the partner that led my seed, really. We had a fantastic relationship. He also really believed in my vision and the way I wanted to build things. Which is what I just talked about and I just wanted to have a very lean, nimble board with him. Just me and him and I'm so happy I did that. So it was a no brainer to me to work with Excel. But I always like to bring on a lot of angels and strategic funds. And so I did go and leave space, and fill out the rest of the round. Did some of a process there, but the lead was locked in from the very beginning.

Pablo Srugo (00:53:32) :
When did that close?

Helen Hastings (00:53:33) :
That was mid 2025, and we announced it in December 2025.

Pablo Srugo (00:53:39) :
And when would you say was the moment, if it's happened actually. That you felt you'd found true product market fit?

Helen Hastings (00:53:46) :
Yeah, let me, I'll start with a metaphor but I promise I'll give you a specific answer. But the metaphor helps. The best metaphor I've heard is that finding product market fit is like rolling a boulder up a hill. It's really hard, but once you found product market fit. It is like the boulder is rolling down the hill, and you're chasing to keep up with it. And for me, I had some moments last year of that boulder rolling down the hill, and I cannot keep up with it. And mainly, it was when we had way too many companies trying to use us than we could handle, and I said yes to too many. And so all of a sudden, we had all these onboardings that we weren't prepared for, and we weren't giving a few customers the best experience. Because our processes could not keep up and, but then they still wanted to use us. So a lot of these were monthly contracts to start, and then they converted to annual. And I thought, oh my gosh, they're all going to churn. We're not delivering on our promise here. They're going to hate us. But actually, they wanted to keep working with us and I think that's what product market fit feels like. You have more than you can handle. The boulder is rolling down the hill, but they still want to work with you, even though things are not going perfectly.

Pablo Srugo (00:55:03) :
And then last question, what would be like your number one or maybe a top piece of advice for early stage founders that are still looking for product market fit?

Helen Hastings (00:55:11) :
My advice for founders looking for product market fit, I think you just got to do things sometimes versus wait for that perfect moment. Hopefully the stories I shared earlier show that, which is there wasn't one single eureka moment. But by just talking with customers, by getting design partners, by building the scrappiest thing. Even if it's manual behind the scenes, and just seeing what people want and do they love using this. And doing that by just getting the word out there, by getting the product out there, that is how you learn. So don't feel like, oh, there's some moment that I haven't experienced yet. Where it all should just become clear and people should suddenly be loving it, and ripping it out of my hands. No, just get early product out there in the scrappiest way possible, and suddenly you'll see that people keep knocking on the door and asking you for it. And instead of you shoving it at them, suddenly they're going to be pulling it from you, even if you think it's not perfect.

Pablo Srugo (00:56:10) :
Perfect. Well, Helen, thanks so much for taking the time. It's been awesome.

Helen Hastings (00:56:13) :
Thanks for having me on.

Pablo Srugo (00:56:15) :
Wow, what an episode. You're probably in awe. You're in absolute shock. You're like, that helped me so much. So guess what? Now it's your turn to help someone else. Share the episode in the WhatsApp group you have with founders. Share it on that Slack channel. Send it to your founder friends and help them out. Trust me, they will love you for it.