How Does Laundry Software Make $60M a Year?

At 23, Alex Jekowsky started building Cents, an all-in-one software, hardware, and payments platform for laundromats, dry cleaners, and shared laundry rooms. In 2025, Cents generated $60 million in revenue, up 67%, with just 80 employees and EBITDA margins above 20%. The company now has 99% customer retention, powers laundry rooms in nearly 7,000 apartment buildings, and recently raised a $140 million Series C at an approximately $800 million valuation. In this episode, Alex reveals: • How Cents grew from $700,000 to $60 million in four years • Why its first customer was a 73-location laundry operator • How Cents combines software, payments, and custom-built hardware • Why the company generates roughly $750,000 in revenue per employee • How Cents maintains 99% customer retention • Why Alex acquired a hardware company instead of continuing to build everything internally • How the $140 million financing included a $30 million employee tender • Why founders should avoid raising at valuations that eliminate future options • How Alex plans to turn an overlooked industry into a multibillion-dollar company

Froda generated $57 million in revenue in 2025 by lending to the small businesses traditional banks often overlook. In this episode, Nathan Latka sits down with Froda co-founder Oliver Mohseni to unpack the economics behind the startup bank. Froda has supported 150,000 businesses across seven markets and funded approximately $3.45 billion since launch. Oliver explains how Froda uses retail deposits, automated underwriting, and embedded partnerships with banks, fintech companies, payment providers, and software platforms to scale its lending operation. Listen to learn: • How Froda makes money on an average loan of just $23,000 • Why its banking license and retail deposits create an advantage • How it prices loans at approximately 16% APR while managing credit losses • Why embedded lending helped Froda expand across Europe • How the company built a roughly $500 million active loan book

Carl Turner, founder of Swipe.by, built an AI marketing platform for restaurants to $3.5M ARR growing 400% year-over-year — with $0 spent on ads and every customer closed by a rep knocking on doors. He breaks down the exact rep comp plan (6 locations/month, $6K ACV, 30–40% commission), why he took $450K from Founderpath instead of Stripe, and why he gives away websites and online ordering for free. Then he turns down a $15M cash offer and explains the 2x liquidation preference math that could leave a founder with nothing. SaaS, bootstrapped, AI, restaurant tech, outside sales, SMB, Nathan Latka, Top Founders

Raphaël Boukris is the co-founder and Chief Revenue Officer of Didomi (didomi.io), the Paris-based consent management platform that helps enterprises comply with GDPR, CCPA, and privacy laws worldwide. Roughly 2% of all global web traffic passes through Didomi's SDK, and the company processes billions of consent decisions every month for customers including Bloomberg, Sky, and Adyen. In this interview, Raphaël breaks down the full financial history on the record: three funding rounds, two acquisitions, and a majority sale to private equity. He raised under €300k from angels, then €5m from Breega in late 2019, then a €34m ($40m) Series B led by Elephant and Breega in summer 2021 — priced, he says, at above a 10x ARR multiple. Eight months later the market collapsed. He never burned through the money. In April 2025, Marlin Equity Partners took majority control, and Didomi immediately acquired Addingwell and Sourcepoint. Today the group does $40–60m ARR across 3,500 customers with fewer than 200 employees, and has at least one customer paying over $1m per year. What we cover: (01:02) What Didomi actually sells, and why every cookie banner in Europe is a business (01:51) Why roughly 2% of global web traffic runs through their SDK (02:12) Processing billions of consents per month — and who is not the customer (03:00) Pricing on monthly unique visitors, and why AI agents and bots are breaking that model (04:32) The Addingwell acquisition: a few million in ARR, a few thousand euros ACV, profitable enough to pay dividends (05:10) Why he bought a second product instead of building one (06:56) Every round, in order: sub-€300k angel, €5m Breega, €34m Series B (07:50) Above a 10x multiple in 2021 — and how he ran Elephant through a roadshow to get it (09:44) Secondaries in the Series B, and managing a team whose options went underwater (10:06) The year he crossed $1m ARR, and the year he crossed $10m (12:18) The question he answers "absolutely not" — was he ever close to shutting it down (13:09) What growth equity actually pays in 2025, and the range he'd give (14:22) Buying Sourcepoint, a company that had raised $48m, for its ACV and its US enterprise base (15:07) 3,500 customers, one paying over $1m/year, under 200 people (16:44) The combined group revenue number: $40–60m ARR Full metrics, funding history, and revenue timeline: https://getlatka.com/companies/didomi.io  

Stéphane Donzé has bootstrapped AODocs and Talarian to a combined $60m in 2026 revenue without raising capital. His top three customers each pay more than $2m per year, including Google, which pays him millions annually to control the documents behind its data center builds. He funded fourteen years of R&D off a cloud consulting business, never burned more than $200k in a month, and has fielded private equity calls four times a week declining $600m offers. 

He bootstrapped to $16.5m ARR selling software to 2,000 dental offices. Zero dollars raised, 24% EBITDA, and $1.5m in free cash flow last year. COVID cut him back to $2m in 2021; he's added $14.5m since, charging $13k per dental office per year. When I asked what happens if Henry Schein shows up with $180m cash, he gave a surprising answer.   

How do you go from a Chrome extension with no sales team to $15M in revenue in five years, while raising less money than most startups spend in their first year? David Chevalier is the co-founder and CEO of Surfe, the leading CRM enrichment and waterfall data platform for B2B sales teams. He bootstrapped for two years, grew to $1.5M purely through HubSpot and Pipedrive marketplace rankings, and has never taken a dollar of personal secondary despite being six years into the company. Today Surfe processes 50 million data points per year and is the third partner inside HubSpot's AI prospecting agent next to ZoomInfo and Apollo. You'll learn: How Surfe got to $1.5M in revenue with zero salespeople by becoming the number one app in HubSpot and Pipedrive marketplaces The waterfall enrichment model that aggregates 15 data providers and why building it at scale is much harder than it looks Why a third of their $15M revenue now comes from API usage and credits rather than seat licenses How David revealed his company revenue publicly for the first time ever on this show The Google partnership that started three and a half years ago and helped Surfe build the waterfall before the market even knew what it was Why their largest customer now pays over $1 million per year and how many more are approaching that threshold What happened when David tried to hire salespeople before the product was ready, and how many failed hires it took to find their CCO Eric How Surfe doubled from $9M to $15M in one year with 20 AEs each carrying $700 to $800K in quota Why 40% of their revenue comes from the US despite being founded in Paris and what it took to make that happen What David would do differently if he could go back and negotiate secondary at his pre-Series A round Connect: YouTube: youtube.com/@NathanLatkawatch Surfe: surfe.com Founderpath: founderpath.com

How do you build a $16.5M vertical SaaS company with 24% EBITDA margins with zero paid acquisition, zero outside funding, and your wife as co-founder? Rohit Garg is the co-founder and CEO of Practice by Numbers, an all-in-one dental practice management platform serving 1,300 customers across 2,000 locations. He started building it in 2015 because his wife is a dentist and he was the one running SQL queries to answer her business questions. Today they are bootstrapped, profitable, and sitting on $2 billion in untouched payment GMV from their existing customer base. You'll learn: Why Rohit built the entire platform by sweat equity while working a full-time job at Philips Healthcare How Practice by Numbers went from $2M in 2021 to $16.5M in 2026 without spending a dollar on paid ads Why their customers generate almost 2x the national average in dental practice revenue — and why that drives retention without any loyalty program The $2 billion in payment GMV sitting in their system right now that they have not started converting yet Why Henry Schein and Patterson, who own the biggest competitors, simply cannot move fast enough to build what Practice by Numbers has built How 100% inbound word of mouth growth leads to 22 to 24% EBITDA margins at $12.5M in revenue The AI receptionist and agentic workflow roadmap that will let dental office managers run the entire practice without touching a screen What Rohit actually said when Nathan asked if he would sell for $180M all cash to the company that owns Dentrix Why COVID cut the company at its knees in 2020 and what it took to relaunch and 8x revenue in five years The vision for $100M in revenue within 10 years and why Rohit thinks it will happen faster than expected Connect: YouTube: youtube.com/@NathanLatkawatch Practice by Numbers: practicenumbers.com Founderpath: founderpath.com

How do you build a $10M business by teaching robots to write handwritten notes with real pens, and do it with zero outside investment for 12 straight years? David Wachs sold his text message marketing company for eight figures in 2012, started Handwrytten the next day, and has bootstrapped it to 200 custom-built robots in a Phoenix facility sending 350,000 notes per month. He owns 100% of the company, took $350K in profit last year, and is targeting $10 to $11M in revenue this year. You'll learn: Why David thinks physical CapEx is now a competitive moat in the AI era, and why he is glad he can't be vibe coded away in a weekend How Handwrytten went from $1M in 2018 to $9.4M in 2024 without raising a single dollar The veterinarian euthanasia franchise that has been sending 80,000 robot-written notes per quarter for 8 straight years Why a $5 Google ad click from a realtor generates only 20 cents in contribution margin and what that forced them to do instead How they built a paper feed system that can write on a stuffed envelope without mismatching a single card across hundreds of thousands of sends The robot leasing model: 30 units in the field at $1,000 to $1,250 per robot per month going to 3PL facilities and swag companies How integrations with HubSpot, Shopify, Claude MCP, Zapier, and Salesforce turned Handwrytten into a fully automated marketing channel Why David would sell for $100M today and what roll-up strategy Nathan pitched him live on camera The SEO playbook that drives 40,000 organic clicks per month with zero agency spend and one in-house writer What it actually costs to build a robot when you manufacture them yourself in Tempe, Arizona Connect: YouTube: youtube.com/@NathanLatkawatch Handwrytten: handwrytten.com Founderpath: founderpath.com

How do you go from a pricing experiment inside a failing product to $3.6 million in annual revenue with 10,000 customers — in under two years and with zero paid marketing? Eugene Cheah is the co-founder and CEO of Featherless.ai, the largest open source LLM inference provider on Hugging Face. He co-created RWKV, the first attention-free AI architecture under the Linux Foundation, and today runs a 27-person team serving customers that pay anywhere from $25 a month to $2 million a year. You'll learn: Why Featherless started as an accident — a pricing experiment that outperformed the original company within days How they grew to 10,000 customers with zero paid marketing using only Reddit posts to AI communities The enterprise sales motion that closes $1 to $2 million per year contracts by showing startups how to cut their OpenAI or Anthropic bills in half Why supporting 6,700 models versus competitors' 100 is the moat — and why no one else is doing it How the RWKV architecture has the potential to reduce AI inference costs by over 1,000x compared to today's transformer models Why the bottom 50% of AI model usage — the fine-tuned, niche, non-English models — is where the real money is hiding What happened when servers caught fire from user growth and the team had to add capacity faster than they could hire How a $20 million Series A in December 2025 finally gave them enough server capacity to breathe Why Eugene believes AI models today are massively over-parameterized — and what a human brain tells us about where efficiency is actually going The refund guarantee Eugene made live on camera and what it says about their confidence in the product Connect: YouTube: youtube.com/@NathanLatkawatch Featherless: featherless.ai Founderpath: founderpath.com