Building Ramp Sheets: Ramp Labs and Applied AI With Alex Stauffer and Alex Shevchenko
Unpacking spreadsheet agents, product philosophy, and shipping velocity
Software Synthesis analyses the evolution of software companies in the age of AI - from how they're built and scaled, to how they go to market and create enduring value. You can reach me at akash@earlybird.com.
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Today we’re speaking with Alex Shevchenko and Alex Stauffer, the Leads behind Ramp Labs. They released Ramp Sheets in November to a strong reception, with an exciting roadmap ahead.
Alex Shevchenko is the Engineering Lead at Ramp Labs. He’s been at Ramp for two years, working on the Applied AI team. He started on AI platform-level infrastructure (NVIDIA CUDA drivers, memory partitioning for model services) before moving into experimental projects like Ramp Tour Guide (an early computer use prototype). He set up Ramp Labs with Alex Stauffer.
Alex Stauffer is the Product Lead at Ramp Labs. He’s also been at Ramp for two years and previously was on the founding team at Actively AI. Based in New York, he leads the team responsible for pushing the boundaries of AI on the product and application layer.
The Mission of Ramp Labs
How does Ramp Labs operate relative to other product teams at Ramp? Is there a clear roadmap of products, or is it more exploratory?
Alex Shevchenko: One of the big things when we were starting out is that there wasn’t a clear mandate given to us. We were free to roam and figure out things that we think are interesting in the applied AI space and to make products out of them. We already had some experiments in flight just from trying to make our finance team more efficient, which is actually the backstory for Sheets.
We had no proper guideline of “you need to do growth, you need to get this many followers, or you need to save this many hours for our team.” It was really just exploring things, and getting to something that’s useful in the long run. We don’t need to hit a specific metric this quarter. We can just build out some incredible design, some incredible product. It might not necessarily be useful right away, but it can then get disassembled into parts that will be used in Ramp to deliver more saved time, more saved money for our customers later on without us being constrained to really think every minute about our KPIs.
Alex Stauffer: To build off of that, one of our first projects was an AI form filler. This is a huge problem in finance teams. A lot of their time is just spent filling in forms whenever they need to wire money. It’s incredible how much wasted time there is. They’re just inputting the same company information, the same beneficial owner information all the time.
We shipped that product, we got a lot of impressions on Twitter and a lot of users. But then the broader story is that technology is being phased into all of the Ramp product today. There are 55,000 businesses on Ramp, millions of users. And now whenever it pains a finance admin to go fill in forms in any part of the product, this is being turned into an automated process so they don’t have to do that.
The Launch and Early Momentum
You launched with no paid marketing and hit 10K users organically. How’s the momentum been, and where are these users coming from?
Alex Stauffer: We launched a little bit before Thanksgiving last year, during the holiday season, which was interesting timing. We got over 2 million impressions when we launched. We have over 10,000 users today, and a lot of the top VC funds, PE funds, top founders, small business owners; those are really the core users that we’ve seen on the product so far.
It’s really exciting. Users keep coming back. There’s a lot of organic growth just through word of mouth. We might throw some paid marketing at this soon, but right now it’s all been organic. And a lot of users are asking for more product features, and that’s what I’m really excited for. They’re giving us a lot of great feedback about how to improve the product.
For example, having multiple options for formatting, or you could have custom formatting based on how your company does things. So these are really interesting insights. The product is constantly being improved right now.
What’s the most surprising or creative way you’ve seen someone use Ramp Sheets?
Alex Stauffer: Most of it is the standard use cases: small business owners, CFOs, small finance teams is definitely one cohort. And then there’s another that’s VC, investment banking, consultants.
In the first cohort, there’s 13-week cash flow and, for example, modeling out, “Okay, what if I hire three more people to the sales team this quarter? How does that affect my burn?” Another interesting point is we’ve seen founders use it to create valuations of their companies. That’s pretty novel to us. We did not expect that. These are things that have been self-reported from customers and through our user interviews; we can’t see your data at all.
On the investment banking side, a lot of friends in the IB space are signing up for this, even though they’re using their personal Gmails to not do it through their work. But it has been helping them quite a lot. There’s a lot of leveraged buyout models. These things take hours and days to create. And with Ramp Sheets, you can get a really good version of this in 10 to 20 minutes.
Why Spreadsheets?
Why did you pick spreadsheets, and how has your thinking evolved about why this was the right problem to tackle?
Alex Shevchenko: This is a project that we started a long time ago, where we were tasked with improving the speed and efficiency of our own internal finance team. That’s a non-trivial thing to do because Ramp’s financial structure is very complex and very mature. So there’s not a lot of super easy wins to be had there.
When you think about it, you can find an accountant or a bookkeeper, sit down with them, spend a day explaining a task that normally takes them 30 minutes, and then take two days to automate it away. So you spend three days of engineering time to save 30 minutes of a finance professional’s time that happens every month. The math doesn’t work out there.
So one of the first things I tried to do was streamline the process of getting the context from the finance person’s brain into the engineer’s brain. We started out with a tool where they could record all of the actions that they take for a certain process. And with an LLM, we would process it and create basically a piece of documentation that would map out the entire process with an explanation and the tools they’re using. So you could take that and start writing a Python script or an automation for it much quicker.
Then the logical next step was, well, you already have all of this information. Maybe you can just use an LLM to vibe-code something directly on your behalf. So we started generating Python or N8N workflow automations from the videos.
We made this prototype and we came to Finance and presented it, and they said, “Well, this is really neat and all, but I don’t really trust it. This is very black box. The output is this Python thing that is very hard to verify.” And it also uses Python blocks or JavaScript blocks for automation. So you’re generating this artifact that is not verifiable by the professional. They don’t want to really use it because finance is very high-stakes. You can’t make mistakes, and the artifact that you automatically generate is not verifiable by them.
So we took a step back, and I was going through the Loom recordings of the processes that they sent to us. And I realized that you drop your cursor anywhere in that video and 95% chance that you’re in Excel. Finance professionals live in Excel. That’s all they do because it’s such a powerful tool and it’s the thing that they understand the best. As an engineer, I can read through Python code and my brain is set up for that, but their brain is set up to quickly parse an Excel file and get the information out of it.
So we decided to take this video automation and instead of generating Python, we’re going to generate step-by-step instructions for the Excel modifications so that they have a clear view of what is happening. They can audit it very quickly because they’re used to Excel, and then they can have it as a repeatable process.
Then for the public launch, we decided that the video modality doesn’t necessarily make sense for a lot of people. The finance team at Ramp is very strong and has their processes in place. But that’s not necessarily the case when you’re a startup founder and you don’t know what an example of a good model is. So we opened it up to be a text box. You just come into it and you explain what you’re looking for, and then it takes care of the specifics for you.
Building the Infrastructure
There’s probably a lot of engineering and infrastructure thinking that’s gone into context management, tool calling, reasoning, debugging errors. Can you talk us through the harness you’ve built around the models to make this product production-grade?
Alex Shevchenko: For something that’s running on Excel, the harness is OpenAI Agent SDK for the most part. We have modified it quite a bit, but that’s the core of it. The hardest piece on something that interfaces with Excel is just how complex Excel is. There are quadrillions of different configurations and settings—incremental cell calculation for self-referential formula chains. In general, just a random formula is also very complex.
Making those Excel calculations is really the hardest piece. I know that, for example, Anthropic Claude for Excel uses LibreOffice Excel to do those calculations. They’ll spin up a headless version of it and make the modifications in that. That’s not the approach that we took. We have a more complicated approach that I don’t want to divulge fully, but for the formula calculation and formula understanding—getting the calculated value versus the cached value (which is the number that you see compared to the formula)—those are the hardest pieces to get really right. Otherwise, you’d get a huge sheet with #VALUE!, #REF!, and just other errors all over the place. That was really the hardest piece to get correct from the technical point of view.
Alex Stauffer: There’s also a lot of design delights that went into this. We tried to make it as simple as possible. That’s very clearly shown in the designs. There are a couple of other similar products that are a lot more cumbersome and complex, and we tried to avoid that. A lot of those tools are made for IB and all these crazy workflows. We really wanted to make this just general for the consumer and then also those specific users.
We care a lot about design and product at Ramp. It’s what we’re known for. So we wanted to make it very sleek, very simple. We actually have a lot of new features coming out soon. We’re going to have templates, and this is actually going to supercharge Ramp Sheets. It’ll be quite similar to Granola recipes where we’re going to have a lot of pre-baked templates that are for those core use cases, and it’ll be super easy to fire off prompts.
One thing that we’ve realized with a lot of these AI products is that prompting isn’t super easy at times. The whole concept of the blank text box is quite jarring to a lot of people. People don’t know what to do, people don’t know what to say. We’ve tried to make this easy in the UX. We have some basic templates already that are more general-coded, but this is going to be a lot more specific and a lot better.
Views on Agent Infrastructure
Given what you’re working on at Ramp Sheets and across Ramp Labs, do you have any strong opinions on the direction of agent infrastructure? There are all these primitives emerging—frameworks, sandboxes, memory, tool-calling protocols. From a supply-side perspective, there’s so much infrastructure for agents being built. Where do you see things headed?
Alex Shevchenko: That’s the hardest question to answer because it’s very case-by-case. I think whatever you’re building, a lot of the general chat interfaces are obviously going to have a ton of frameworks built around them. If you want to build out a customer support chatbot, there’s a billion different ways of doing it because there’s a bunch of providers that all have really good solutions.
But then there’s stuff that needs to be customized, like an Excel editor. If you want to create something for text-to-3D modeling or something along those lines. Obviously, in those cases, you will probably end up having to write a lot of custom code that interfaces between them.
In general, it’s really a gradient of how much hand-holding you get. The more hand-holding you get, the less customizable or the more finished-up solution you get. All of these are engineering trade-offs. Obviously you can move very fast on a finished solution from a vendor, but then you’re going to lose out on some of the customisability, and it’s case-by-case whether that helps you out or not.
I think one of the biggest things is sandbox providers. We use Modal for Inspect, for example. I think that’s a great product that is going to become very popular as we start letting agents roam much more freely. Letting them live in a bunch of sandboxes, you spin up one for each time you run something on the user’s behalf. Now it’s a sandbox within a Linux VM, but maybe they start giving them computer interfaces or something more complex. I feel like that’s one of the things I’m pretty bullish on.
The Roadmap for Ramp Sheets
Templates are coming. Can you speak generally about the roadmap for Ramp Sheets in the future?
Alex Stauffer: First of all, it’s going to be integrated heavily with the Ramp product. There are a lot of workflows today that finance teams are doing within Ramp, and they ultimately end up in a spreadsheet afterwards. We’re going to do a lot more to automate some of this and have them end up in Ramp Sheets with that AI agent that they can just go talk to and manipulate the data, which is a lot easier than having to do it themselves.
There are also a lot of really unique and creative product experiences that Ramp Sheets will support which Ramp is creating and will be shipping soon. You’ll see it coming soon, but it’s heavily impacting the product in a really positive and fast way.
And then for Ramp Sheets itself, it’s proving to be very successful and growing on its own. So we’re going to keep growing it and investing in the product and platform. We’re having more resourcing on our side to do that. The goal here is if we can provide a lot of value to a lot of new people and then have them learn about Ramp in the process, then it’s really a huge success.
Ramp’s Product Culture
Ramp’s product culture is hugely respected. How do you think it’s distinct, and what trade-offs is Ramp making to be that way?
Alex Stauffer: I think the biggest thing is that everyone here is very AGI-pilled. That’s probably the biggest difference between a lot of the top startups today and the ones that aren’t. AI is in everything. We’re using all the AI products available. Everyone is using AI to speed up their workflows. Claude Code and Cursor are upgrading everyone’s velocity for how fast we can ship.
We also have an internal agent called Inspect. Everyone is shipping. It’s really 10x-ing the velocity within the company.
I think another main cultural difference is that Ramp is still founder-led, and Eric and Karim are some of the best to do it. They want to grow Ramp into a massive business.
As a result, I think the culture is very non-bureaucratic here. It’s pretty flat. Everyone is an IC. Pretty much everyone’s shipping. And a lot of the top talent is here as well.
Shipping Velocity vs. Polish
Every company takes a different view on shipping fast and iterating from feedback quickly versus waiting until you have a jaw-dropping customer experience. Where does Ramp land on that spectrum?
Alex Stauffer: It’s the former. Our CPO, Geoff Charles, talks about this. We have a whole system of Alpha, Beta, and GA releases. The goal is to just get something in the hands of these alpha customers who have opted in, which are probably five customers/users of the product. Get their feedback really early. Tell us what they like and what they don’t like, and from there just go to the next version and then send that to the beta, which has more users, and then eventually GA the whole thing.
We think of velocity as actually enabling high quality product versus the other way around. The opposite is a bad habit to get into—I would actually describe it as an AI that just keeps thinking in loops. That’s the worst thing to happen where the customer isn’t involved. You just have a lot of iteration without actually understanding if you’re solving the problem. So that’s what we try to avoid.
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Have any feedback? Email me at akash@earlybird.com.
Brilliant insight on choosing spreadsheets as the interface. The shift from video-to-Python to video-to-Excel instructions solves the trust gap perfectly becuase finance teams can audit their native tools. I've seen simialr patterns where engineers build for elegance but users need transparency. The 95% Excel statistic really drives home why domain-appropriate artifacts matter more than technical sophistication.