Should you join: Monk

Inside the startup making it easier for companies to get paid fast.

✨ Hey there - this is a free edition of next play’s newsletter. This is part of our series Should You Join, where we go behind the scenes on interesting companies. Our hope is that documenting these sorts of details, which never really make it to big publication press releases, can help you a) discover more interesting opportunities and b) inspire you to think creatively (for any of your own endeavors). You can join our private Slack community here and access $1000s of dollars of product discounts here.

---

In mid-2022, Patrick McKenzie wrote the following in his Bits About Money newsletter: “[B2B payments] are one place human effort is currently required and glaringly should not be.”

At the time, Accounts Receivable (i.e. AR, the business of getting paid) was done one of two ways: you paid an in-house team of people to do a lot of tedious manual work and/or you used legacy software that kinda, sorta, sometimes automated some parts of AR. It was pretty rough.

But then, later that year, magic. OpenAI released ChatGPT to the public. LLMs went from something that people in the know were excited about to something that everyone was using and making grand proclamations about. These models were full of promise, and the faster companies like OpenAI (and later Anthropic, and Google, and others) made improvements to them, the more possibilities people saw. Among them: what if we solved AR?

Fast forward to 2026. You might expect that we are now living in a dream utopia where the AR problem has been completely solved by wonderful AI software products. A world where companies no longer have to worry about how they will get paid, when they will get paid, or who paid them. A world where companies have their money sooner, are not buried in manual work chasing past payments, and can move faster and spend time forecasting.

So you may be surprised (or perhaps not) to learn that this is not the world we are living in.

Believe it or not, most companies are still doing AR the old way. Manual invoice-to-cash matching, dunning emails sent one by one, and finance teams perpetually buried trying to answer the most basic question: who actually paid us?

And there still isn’t much great software out there to solve it. The whole space remains underloved and underserved, despite it being one of the most universal headaches in business. “$10T is currently stuck in unpaid invoices,” George Kurdin, co-founder and CEO of Monk. “And an average invoice takes 59 days to clear. If you’re doing >$10M in revenue, getting paid in 30 days instead of 59 will literally make you millions over two years.”

Monk’s co-founders’ George Kurdin and Joe Zhou’s answer to the AR problem. Their bet is that they can build a generational B2B revenue platform, and they’re starting by solving the mess that is how businesses get paid.

There’s a lot to like about Monk, and plenty of reasons you might consider joining, which is what I’ll focus on for the remainder of this essay. What are they building? How is it different? Why is Monk going to be the platform that a bunch of companies finally adopt? Why will it be the company most people want to work for? And why should you join now, before the window might close?

The product

The sad state of AR today is not necessarily due to a lack of products. If you run a Google search for “accounts receivable software” you still see plenty of companies that purportedly offer the tools to help you get paid. The problem is moreso that a lot of these products are not very good, or at least, not very comprehensive.

Part of this is because, if you want to run an excellent AR operation, you generally have to end up doing a lot of things that are not (or do not seem to be) scalable:

• Personalized, 1:1 email/Slack/text follow-ups

• Uploading invoices into customer-specific AP portals

• Matching payments to invoices when the memo is blank

• Chasing the right approver when you get ghosted

• Resolving disputes over line items and PO mismatches

• Random invoice changes at the last minute

This kind of hard-to-scale work accounts for at least 39% of invoice delays, George Kurdin said.

So, is it very hard to send an automatic reminder template? No. Plenty of software can automate that. But those things, George says, “are the easy parts.” Most existing products stop there and then hand the hard stuff to your team. This may explain why there is no one AR platform that every company in the world uses and is happy with. (Far from it!)

The world accepts AR as a broken hard problem. And the workflows that solve it are deterministic and rigid. The bet that the team at Monk is making is that this is a systems problem that is solved via conversational agents.

Even though AR workflow automation is act 1 of the company, there are a number of technical challenges embedded in this problem:

1. Agent harness for workflows that touch money (evals, observability, reliability, deterministic vs. probabilistic fallback design)

2. Building reliable long-horizon agents (ETL, data hydration, guardrails)

3. Multi-modal agents (voice, text, email)

4. Improvement mechanisms beyond RLHF

5. Distributed systems and data wrangling

6. System design (data modeling, unified abstraction layer, parent/child hierarchies, business domain-specific logic)

7. Customer-facing design (information density, escalation paths, agent design, enterprise-level controls)

A version of an AR platform designed from first principles automates the cash cycle. Including the bits that historically required a human to do well, like sending a custom-written email to a specific person asking them when they are going to make a payment.

This is what Monk built: an AR platform that doesn’t just automate follow-ups, but makes them smarter and context-aware enough to actually strengthen the customer relationships your business runs on.

Monk plugs into your ERP and your CRM, follows up on unpaid invoices, matches incoming bank deposits to the right invoice, produces real-time reports, and lots more. It’s effective at each step of the way because it is using AI agents which can understand context and execute the way a human would; this unlocks capabilities that legacy platforms never had a way to offer. So, if you are a company that uses Monk, you can spend as close to zero hours worrying about payments as is humanly possible with the right escalations and guardrails in place to protect your customer relationships.

Monk already manages more than $1B in receivables for customers. They’ve reduced the amount of time it takes for their customers to get paid by 40%. Their collections messages receive 24% higher response rates vs. standard dunning emails. AR teams using the platform save more than 25 hours a month. Instead of chasing, they get a full picture of their cash position, collections, and which customers need attention, all in one place.

The strategy

The master plan here is much bigger than AR. Writing on his Substack, one of Monk’s investors (Nikhil Basu Trivedi from Footwork) observed that “Stripe started with payment processing. Ramp started with corporate cards. Like those two companies, Monk has the opportunity to move beyond its initial wedge to building a broader B2B finance and revenue platform.”

But perhaps I’m getting ahead of myself. In order to build a ‘broader B2B finance and revenue platform,’ as Nikhil hopes they will one day do, Monk most likely needs to first win in AR.

So how does Monk achieve the first step?

One way is what I covered when explaining their product, which is that they build the most effective and comprehensive platform; the platform that covers all of the edge cases and takes you from contract to cash. That’s already a leg up on competition.

But it’s very likely that Monk will also need to establish itself as the most trustworthy platform you could use to run your AR. “Revenue automation software has a credibility problem,” George wrote. LLMs and ‘AI-native software’ sounds good, but it also raises doubts for a lot of people. We’ve all seen how AI can hallucinate. We’ve seen how it can be inaccurate, particularly when it comes to getting specific numbers right. These mistakes are intolerable when your product handles customers and revenue.

Monk is solving this by taking a transparent and deterministic approach. By transparent, I mean that customers can drill into anything and everything the product does on their behalf. “Every message, every ambiguity, and every general ledger entry,” Joe Zhou, co-founder, wrote. “Our principle: white-box by design.” This means you’re never left wondering how something happened or where a specific number came from. The info is all there.

And by deterministic, I mean that they wrap LLMs (which are probabilistic) with an additional, non-LLM layer that determines whether the model’s output is actually correct and safe to act on. This is an increasingly common approach when dealing with AI in important contexts, like finance. “There’s no room for error,” George said, and Monk has tested its approach “against thousands of edge cases.”

You can start to envision the arc. Build a reliable, effective, trustworthy AI automation product, put it at the center of cashflow for huge numbers of businesses, and then expand in a bunch of useful ways. The growth so far suggests it’s working.

The growth

Co-founders George and Joe make for a confidence-inspiring pair. George worked at D.E. Shaw, spent years as a professional poker player, ran product at Minecraft, and was GM of Streamlabs through its sale to Logitech in 2019. Joe was at Intuit, then Google, then Snap.

The two spent months getting to know each other (mostly by going on runs) before starting Monk. They mocked up fintech products in Figma and pitched them to more than 200 CFOs across stages and sectors. AR landed almost every time, so they started work on Monk.

Today, just six months after Monk came out of stealth in October 2025, Monk manages over $1B in receivables. Customers include AI-forward companies like ElevenLabs, Profound, Siro, Subject, Unify, and Pump. When I asked George about revenue, he told me ARR was up 10x in the past 6 months. Much of this early growth (especially during the first few months) happened with no dedicated marketing spend at all. It’s mainly been “email and inbound,” George said. (This is generally an encouraging thing to hear; people really, really want what Monk is building!)

Investors have moved fast, too. Better Tomorrow Ventures led Monk’s $4M seed in spring 2025, and a year later, Footwork and Acrew co-led a $25M Series A.

Much of this success is, George said, “talent density + past experience + our DNA/culture. Deeply believe that our judgement and sense of urgency/obsession with winning will beat out everyone else, regardless of their funding or our late start.” Which begs the question: just what would it be like to go and work at Monk?

The culture

When I asked George what success for Monk would look like, he said building it is building a truly useful applied AI platform loved by customers. He said something else, though, which struck me as a bit more rare: Monk will be a success if “100s of early teammates go on to build successful businesses.”

When I asked how he pitches the work to people who want to join, George said he will “share everything I’ve learned over the last 20 years and help you become a founder.”

This was, I thought, an uncommon way to talk about the startup culture. And as I chatted with the team and looked deeper into how work gets done at Monk, it’s clear that this very founder-esque focus is a big part of how they get their work done and think about winning.

“I had ownership and trust the moment I walked in the door,” Henry McQueen (ops) said, “with the opportunity to work on anything that drives us forward. On my first day, George said ‘let nothing fester, be relentless with questions, max transparency’. That’s how the team operates.”

“I am genuinely, enthusiastically proud of what I’m doing,” Nurah Kutty, also in ops, said. “Coming from banking, it is very different to have nobody telling me what to do and no structure or process to follow. I choose where to focus, and when something goes well I know it’s mine.”

Even on their careers page, Monk is quite clear that they care about winning and outcomes more than anything else. Part of the description for their Applied AI role reads: “we do not care about your education, age, location, or past titles. All that matters is (1) your heart, (2) whether you have taste/exercise good judgement, and (3) if can ship quality code faster than us.”

There’s a culture of “extreme transparency,” Henry told me, and almost everything is discussed publicly rather than in private DMs. Meetings are rarely (“if ever”) scheduled. People tend to work long hours, not for its own sake but as an input to producing a lot of great work.

Of course, the team (and the founders) care a lot about everyone at Monk and want to make sure everyone is feeling good about the work. Nurah said that George is “constantly asking what he could do better and how he can help. On my first week, he asked me: are you happy? Do you like this? What would you like more? What do you like the least?”

All of which raises a natural question…

Should you join Monk?

Success and failure at Monk are rather straightforward. If you succeed, you are “always learning,” George said. And you are an “owner with an obsessive work ethic. You know when to rip it and go deep on a problem.” (More of that founder mindset.) If you fail at Monk, it is probably because you are “not learning/not improving.”

This criteria alone should give you a good feel for whether or not it might be a good fit to reach out about a role. If you’re at a stage of life where you feel like you just want to come into a company and execute what you know how to do (as opposed to becoming 2x or 5x or 10x better), then Monk might not be the right fit for you. And vice versa.

The Monk team believes that this intense culture of pace and ownership is exactly what they need to become the company they’d like to become. To beat all the competition and build a generational B2B revenue platform. If you think you’d be a good fit to help them build a category winning platform, check out their open roles.

Thanks to Monk for supporting Next Play and making this spotlight possible.