Building software got easier. Getting customers didn't. Every week, a founder building B2B SaaS or an AI product tells you how they got their first customers, and how they kept them: what worked, what didn't, and what they'd do differently.
You'll hear how founders find a channel that works, price their product, sell without a sales team, and stop churn. That includes founders building AI startups and AI agents.
Host Omer Khan has interviewed almost 500 founders since 2014, including Calendly's Tope Awotona and Fyxer's Richard Hollingsworth. The SaaS Podcast is part of SaaS Club, which helps early-stage B2B SaaS and AI founders go from first customers to 7 figures, through the podcast, a weekly newsletter and coaching.
New episodes every week.
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Selling an AI SaaS Platform After 200 Buyers Called It Crazy
Épisode 499
jeudi 5 novembre 2026 • Durée 56:29
He asked buyers to rip out their whole Salesforce stack. The first 200 to 300 called him crazy. David Zhu, co-founder and CEO of Reevo, built an all-in-one AI sales platform and learned the hard way how to sell it. He kept the platform and changed the ask: buyers start with one piece, like the dialer, the sequencer or the AI-native CRM.
David explains why Reevo spent two years in stealth before selling, how a founder he met at an AWS program became the first paying customer before seeing the product, and how Reevo got to $1M ARR in 6 months while an introvert ran founder-led sales. He also shares the Mom Test mistake he made early on.
Plus: why the startups that happily adopted his whole platform turned out to be false validation.
David ran engineering teams at DoorDash and worked with Keith Rabois at OpenStore before founding Reevo, his fifth startup, with a founding team of 14. Reevo now serves hundreds of customers without paid marketing.
🔑 Key Lessons
Be strict on architecture, flexible on the sale: Reevo kept its all-in-one platform but stopped demanding rip-and-replace. Letting a buyer start with one tool asks for a much smaller first yes than throwing out Salesforce.
Give buyers three doors, not the whole menu: Reevo groups its land motions into pipeline generation, deal progression and manager reporting. Prospects pick their biggest pain instead of facing every module and integration at once.
Watch out for false validation: Startups with no RevOps leader or security review adopted Reevo's whole platform easily. That said little about the entrenched mid-market buyers Reevo actually wanted to win.
Play to your strength, not someone else's game: David had no sales brand, so he built on what he did have, a dense engineering team. Copying founders who have distribution you lack is how you lose.
Open the deal, then hand off the close: David, an introvert, sent the first email and ran the intro call. A founding sales leader from HubSpot closed, and Reevo copied his talk tracks across 12 to 15 AEs.
Ask for payment to test your discovery: Early prospects praised David's pitch, then said no when he asked them to pay. The Mom Test's non-leading questions get you to the honest answer sooner.
Don't outsource ICP discovery: Paying a vendor $50,000 to run your outbound skips the learning. David says founders without product-market fit have to do discovery themselves or risk a false start.
Chapters
What Reevo does and who it's for
Founding team of 14 and the size of the business
From DoorDash and OpenStore to the idea for Reevo
Why Reevo built in stealth for two years
The first paying customer from an AWS program
The Mom Test mistake and winning without a sales brand
Founder-led sales as an introvert
Platform vs point solution
200 buyers called the rip-and-replace pitch crazy
Selling the platform one piece at a time
Why startup customers were false validation
Three land motions instead of an a la carte menu
Vibe coding, outsourced SDRs and enterprise bake-offs
ChatGPT Should Have Killed His AI SaaS. Instead, He's Nearing $10M
Épisode 498
jeudi 29 octobre 2026 • Durée 53:08
ChatGPT turned his product into a commodity at close to $1M ARR. Alon Talmor rebuilt Ask-AI into Mosaic AI, an AI platform for technical support, by narrowing into a market rivals called too hard and selling measurable cost reduction instead of features. The company is now at multiple seven figures in ARR, approaching eight.
Alon breaks down how six months of listening to enterprises produced the product idea, how he closed a $6.5M seed after his co-founder quit in the middle of the round, and how 5-day pilots built on technical support automation turn skeptical enterprise buyers into six-figure customers.
Plus: why he believes the next phase of enterprise AI is agents orchestrating people, not people orchestrating agents.
Alon sold his first startup, BlueTail, to Salesforce, went back for a PhD in AI and question answering, and founded Ask-AI in 2021, a full year before ChatGPT. Today Mosaic AI serves about 100 enterprise customers, with go-to-market in Toronto and R&D in Tel Aviv.
🔑 Key Lessons
Find a blue pond, not a blue ocean: When funded giants crowd your market, narrow into a segment they cannot serve well. Mosaic left enterprise assistants to own technical support and grew past seven figures.
Talk to customers six months before investors: Alon pitched no VCs until enterprises had told him their problem. Yotpo's knowledge chaos became the product, and the seed closed on that proof.
Tell investors bad news before they find it: His co-founder quit mid-raise; he disclosed it immediately and still closed $6.5M. Prettifying reality reads as mistrust, and investors sense it every time.
Sell the cost reduction, not the feature list: Support is a cost center with a known price tag. Mosaic pitches 50-70% savings on a seven-figure budget instead of arguing about features.
Pick the metric your buyer cannot argue with: Tickets per engineer per day survived scrutiny where time-to-resolution failed. One customer measured an 80% lift in a week of the pilot.
Treat build vs buy as your real competitor: With AI coding cheap, enterprises default to building. Win by showing the end state their side project becomes obsolete against, not a feature comparison.
Founder-Led Sales: He Learned to Sell SaaS and Closed 50 Customers
Épisode 497
jeudi 1 octobre 2026 • Durée 52:19
He walked into his first pricing call asking for $100,000 and walked out with $10,000 a year. Shahar Azulay had never sold anything before co-founding groundcover, and the product he was selling had no user interface yet, just a sensor and some dashboards. He took it anyway, and still argues that was the right call.
Shahar explains why founders should close their first dozen customers at almost any price, how he published a per-host list price and then discounted it up to 70 percent, and how his first 50 to 100 customers came from his own network plus 10 to 15 LinkedIn messages a day.
Plus: the moment customers started ripping Datadog out on their own, before groundcover had built anything to help them do it.
Shahar Azulay is the co-founder and CEO of groundcover, an eBPF observability platform that lets engineering teams monitor production without changing their code. He has grown it to eight-figure ARR with more than 250 customers, competing head-on with Datadog and New Relic.
🔑 Key Lessons
Close your first dozen deals at any price: Early contract value teaches you nothing. The reps, the reference logos, and learning to survive security and legal reviews are what make the next twenty deals closeable.
Publish your price when you sell to mid-market: A public number per host anchors the conversation and skips a negotiation a first-time founder will lose. groundcover listed $30 per host, then discounted up to 70 percent.
Don't expect to grow your earliest accounts: Shahar never planned to 10x those first contracts. Chasing them traps you in old relationships when the rest of the market is still unsold and will pay more.
Your first sales calls are really feedback calls: Prospects treat a founder as someone to advise, not buy from. Shahar framed early meetings as "we'd love to show you what we're building" and converted from there.
Selling the deal is not replacing the incumbent: groundcover closed customers who still kept Datadog running. The post-sale migration motion only got built after those early displacement attempts visibly failed.
Bootstrapping a SaaS From a $500K Goal to a $50M Company
Épisode 496
jeudi 24 septembre 2026 • Durée 42:03
He closed most of the deals himself. It took him ten years to stop. Ross Andrew Paquette bootstrapped Maropost to around $50 million in ARR, and for most of that run he was the discovery call, the demo and the follow-up. It worked well enough to take the business from $300,000 to $27 million in 28 months with six or seven people. Then the thing that built the company became the thing capping it.
Ross breaks down how he won accounts paying $10,000 a month on a five-minute response time rather than features, how two people signed brands like Rolling Stone and Mercedes off a conference floor, and why seven or eight experienced sales leaders all failed at Maropost before he changed what he hired for.
Plus: why he took investor money he did not need, and what it felt like to write a $37 million check three years later to buy it back.
Ross Andrew Paquette is the founder and CEO of Maropost, a commerce and marketing platform with roughly 300 people and 5,000 customers. He started it in 2011 out of his apartment while still selling Oracle ERP software full time, planning on ten customers and a quieter life.
🔑 Key Lessons
🤝 Win on service before you can win on product: Ross offered 24-hour live chat and a five-minute reply when Maropost had ten or fifteen customers, and landed accounts paying $10,000 a month without the deepest feature set.
🎯 Sell to people who already trust you: Three or four customers from Ross's previous jobs signed almost immediately, which is why Maropost had real revenue before it had a finished product or any marketing spend.
⚡ Founder demos beat decks: Ross ran simple discovery then a personalized demo with no slides, and credits his edge to having designed the features himself rather than to any sales methodology.
🚀 Concentrate spend where your buyers already are: Buying top-tier sponsorships at a handful of conferences let two people sign brands like Rolling Stone and Mercedes off the floor, helping take Maropost from $300,000 to $27 million.
🧠 Hire for tenacity, not logos: Seven or eight sales leaders with strong resumes failed at Maropost because their experience came from different engines, price points and company sizes that did not transfer.
Inbound Marketing That Grew a Fintech SaaS to $100M
Épisode 495
jeudi 17 septembre 2026 • Durée 38:39
He never bought a keyword, never ran content marketing, and the big outbound sales force he tried did not work. Rodney Robinson still grew TabaPay to $100 million in revenue, almost entirely through inbound, on a single $2.5 million seed round that stayed the company's only outside money for nine years.
Rodney explains how he found a problem Mastercard could not solve, why he chased small fintechs instead of big logos, how his inbound marketing came from banks and the card networks rather than ads, and why he believes outbound sales no longer works in B2B.
Plus: the six-month lawsuit that cost TabaPay its sponsor bank, and what Rodney had personally put on the line to get that bank in the first place.
TabaPay is payment processing infrastructure that gives fintechs one API to move money instantly in both directions, and processes payments for companies like Dave. The company runs at $100 million in revenue with about 150 people, profitable, growing 35 to 40 percent a year. On the day this interview was recorded, Rodney announced a $155 million raise and the acquisition of a bank.
🔑 Key Lessons
Build what the incumbent is forbidden to build: Mastercard would not add pull payments because it would compete with its biggest processors. That structural refusal, not a missed feature, was the opening TabaPay walked through.
Make the trusted party your sales channel: Fintechs do not know a new processor, but they trust their bank and Visa. TabaPay processes for about 20 banks and lets those relationships generate its inbound pipeline.
Solve revenue before expense: Rodney paid vendors above market to reach the market in a year, charged what it would bear, then displaced the vendors later. Getting to revenue outranked protecting early margin.
Chase minnows, not whales: The first ten customers were small fintechs where the founders already knew each other. Those minnows grew into whales, and the relationship carried through the growth.
Reliability is the product for infrastructure: Three vendors at 99 percent availability leaves you down about 3 percent of the time. Customers bet their business on payments working, so TabaPay took the stack in house.
Founder-Led Sales: A SaaS to $1 Million ARR With Just 10 Customers
Épisode 494
jeudi 10 septembre 2026 • Durée 45:54
He needed a big retailer's data to build the product. No big retailer gives data to a company with no product. Felix Hoffmann solved it sideways: 7Learnings sold a paid consulting project, kept the right to use the data, and built its predictive pricing product on top of it. Ten customers later it was at $1M ARR, and he had closed every one himself.
Felix explains why a demand forecasting product cannot start with a small customer, how he structured the first pilot as an A/B test so a retailer could hand over half its prices without betting the business, and what happened when the first run came back far too expensive.
Plus: how a pricing optimization company prices itself, and why he refuses success-based fees even though he can prove the uplift.
7Learnings is a Berlin company whose software forecasts demand for each product at each price, then sets the price that hits a retailer's goal. It is now at multiple seven figures in ARR with around 40 customers. Felix spent six years as a pricing consultant at Kearney and two years running price optimization at Zalando before founding it.
🎯 Solve the data cold start by selling something else first: 7Learnings could not train a forecasting model without a large retailer's sales history, so it sold a paid consulting project and kept the right to use that dataset.
🤝 Shrink a scary ask into a reversible test: Retailers would not hand pricing to an algorithm outright, so 7Learnings ran an A/B test on half the assortment while the retailer's own team priced the rest.
📉 Pick an early customer who can survive a failure: The first live pricing run was badly wrong on high-priced products. It survived because the buyer had a big enough problem, no alternative, and understood they were working with a startup.
Rick Knudtson (Workshop): The email signal his SaaS ignored for 9 months
Épisode 493
jeudi 3 septembre 2026 • Durée 47:57
Nine months in. Close to zero customers. He was ready to hand the money back to investors. Rick Knudtson had already sold one company, so Workshop started with the idea he found interesting: an intranet. Customers kept telling him to fix email instead. The rebuild took 30 days and brought in 10 customers.
Rick explains why big enterprises cannot run internal comms on a cheap marketing tool, how a year of newsletters and ungated resources filled the pipeline before Workshop had anything to sell, and what changed when the founding team stopped defending its own idea and started listening to customers.
Plus: why Workshop dropped per-user fees for audience-based pricing, and how that changed the way customers expand into new departments.
Workshop is an internal communications software platform based in Omaha with around 140 employees and just under 1,000 customers, including Capgemini. It is five years old and past $10M ARR. Rick previously co-founded Flywheel, a WordPress hosting platform sold to WP Engine in 2019.
👂 The signal was in the sales calls all along: Prospects named email as their biggest internal comms pain for nine months while Workshop kept building an intranet. Listening to customers only started once the ego from a previous exit got out of the way.
🎯 Finding product-market fit was obvious when it finally arrived: Nine months of selling the intranet earned about three customers. Thirty days on the email product brought ten. That gap told the team exactly where to go all in.
🧱 Pick a first problem you can ship fast: An intranet cannot be built iteratively, so feedback loops stall for months. Email analytics was small enough to ship in 30 days and grow into a wider platform.
Selling a SaaS Before Building It: $1M ARR in Six Months
Épisode 492
jeudi 27 août 2026 • Durée 46:55
Ten thousand ads, all built by hand. Julius Körfgen left that grind to build Uplane, software that automates it, then sold to his first customers before writing a line of code. Uplane reached a million dollars in ARR in about six months.
Julius makes the case for selling before building: the cold outreach that got strangers on calls, the one-week sprint from discovery call to working demo, and why he refuses to run a free pilot. Without a dollar attached, he argues, you cannot tell a real business case from a polite conversation.
Plus: why Julius threw out per-seat pricing and now charges a share of ad spend, so Uplane only earns more when the customer's campaigns do better.
Uplane runs around twenty people across San Francisco and Berlin. Julius and his two co-founders raised their first funding round close to a year before the product existed, AG1 is a customer, and a project with Deutsche Bahn is underway.
🤝 Sell before you build: Julius closed customers before writing a line of code. His discovery calls ended with a promise to return in a week with a solution, which forced both a real deadline and a real answer about demand.
🎯 Frame outreach as learning, not selling: His cold LinkedIn messages said he had just left his job and was exploring an idea, and asked for a few questions. People opened up about problems they would never have shared with a pitch.
💰 Never run a free pilot: Without a dollar attached you cannot tell a business case from a polite conversation. Julius has watched founders stay attached to an idea for months because nobody ever asked them to pay for it.
⚡ A week is long enough to build the thing you promised: Three founders and one week produced demos that won real customers. Scrappy was fine; fake was not, and he argues AI removes the excuse for a mock-up that does nothing.
Enterprise SaaS Sales With No Product: Landing a Big Four Customer
Épisode 491
jeudi 20 août 2026 • Durée 43:58
Two founders. Two engineers. No product. Christian Lund closed one of the Big Four accounting firms as Templafy's first customer before the software existed, by selling a point of view instead of a demo. When that customer asked to start with ten people, he didn't say no. He said "yes, if."
Christian breaks down his approach to selling to enterprise without a product, why he answered every ten-person pilot request with "yes, if," and how fixing the proof criteria upfront turned trials into company-wide deals. He also explains why disqualifying prospects beats trying to convince them.
Templafy now runs at eight figures in revenue with a couple of hundred employees. Christian and his co-founder spun it out of an on-premise document business, raised their first funding round close to twelve months before the product existed, and are now rebuilding the company again for the AI shift.
🏢 Sell your point of view before you sell product: During a technology shift, large enterprises buy people who understand the transition. Templafy won a Big Four firm on domain expertise alone, then co-created the product with them.
🤝 Answer pilot requests with "yes, if" rather than no: Christian never refused a proof of concept. He attached conditions on proof criteria, budget, timeline, and the rollout that follows, and walked away when they were missing.
🎯 Define what you are proving before any trial starts: A POC to see whether someone likes the product proves nothing. Agreeing the exact pass conditions upfront turns a trial into a decision rather than an experiment.
⚡ Setting the criteria shapes the competition: Because Templafy defined the proof points first, prospects who later ran competitive evaluations often used Templafy's criteria to score every vendor in the process.
Featherless AI: When a Weekend Experiment Makes More Than Your AI Startup
Épisode 490
jeudi 13 août 2026 • Durée 44:33
He spent two years building his own AI model. Over one launch weekend, a side experiment out-earned it. Eugene Cheah killed the original product and rebuilt Featherless AI around what customers actually paid for. He explains why he concluded people wanted these models more than they wanted his, and how he made the call to walk away from two years of work.
Eugene breaks down how GPU hot-swapping changed the unit economics of AI inference, why he charged a flat monthly rate while the rest of the AI industry billed per token, how stripping the technical explanation off the homepage kept improving conversion, and why Reddit and Discord drove his earliest customers.
Featherless AI now provides instant access to more than forty thousand open source AI models, on the way to a target of all three million on Hugging Face. It reached multiple seven figures in ARR within about a year, and has since raised a Series A led by Airbus Ventures and AMD Ventures.
🔄 Let the experiment beat the plan: Eugene spent two years on his own AI model, then a side experiment made more money than it over one launch weekend. He renamed the company and rebuilt around what customers actually paid for.
🧠 Attachment to your own technology is the trap: The pivot was emotional, not technical. People wanted these models more than his model, and he had been holding his own mission back by insisting it run on his architecture.
💰 Flat pricing sells to the CFO, not the engineer: Per-token billing meant teams could not answer "what will this cost?" A fixed monthly rate removed bill shock and unblocked procurement.
🎯 Removing explanation improved conversion: Featherless kept stripping the technical story off the homepage, eventually removing their own research from the top. Conversion improved each time.
🚀 Go where nobody is competing: The top hundred models have ten providers each. Beyond that, Featherless is usually the only one. A quarter of an uncontested market beat a slice of the crowded top.
Chapters
What Mosaic AI does and who it's for
The real cost of technical support
BlueTail, Salesforce and the PhD detour
Founding Ask-AI a year before ChatGPT
Losing a co-founder in the middle of the seed round
A remote first hire with no local team will fail: The first SDR in North America was hired alone, remote, under marketing, with no one around him. Shahar calls it a failure of gravity, not of the person.
Bet on the technology your team can already build: eBPF worked for groundcover because the founders came from cybersecurity and knew how to ship a safe kernel agent. Incumbents selling SDKs still struggle to copy it.
Chapters
Introduction
What groundcover does and who buys it
Why observability pricing stopped making sense
Bring your own cloud and charging per host
From Apple and cybersecurity to founding groundcover
💰 Capital you do not need still costs you: The 2016 secondary brought expectations rather than money Maropost required, and growth fell from around 400 percent to 6 to 10 percent before Ross bought the investors out.
📉 Getting out of founder-led sales takes longer than you think: Ross spent about ten years moving from what he called "Ross and Co" to an actual organization, and says it was the hardest part of building the company.
Chapters
What Maropost does and who it serves
The ten-customer lifestyle plan
Getting the first customers from old relationships
Expect arrows in year one: Six months in, another company claimed TabaPay stole its software and the sponsor bank dropped them. They won, but only because there was nothing to find.
💰 Price high enough to lose some deals: His test is blunt. If nobody is walking away because you are too expensive, you are too cheap, especially for a complex product carrying real delivery cost.
🚀 Founder-led sales lasts longer than founders expect: Felix closed all ten customers behind the first $1M ARR himself, and stayed closely involved through the next forty, because handing off enterprise sales is genuinely hard.
⚡ Pick the technology after the problem, not before: Felix argues founders are all digging in the same technical space, and that decisions needing determinism, low cost and explainability should not be handed to an LLM.
🔒 Enterprise email is not a MailChimp problem: Security layers, IT governance, and getting a message into 100,000 inboxes in minutes are why large companies cannot run internal comms on an off-the-shelf marketing tool.
📣 Market for a year before you sell anything: Workshop launched a weekly newsletter on day one, now at 50,000 subscribers, alongside ungated resources and monthly webinars that grew from five attendees to five hundred.
💰 Audience-based pricing removes expansion friction: Workshop charges by employee audience size and by channel rather than per seat, so adding another department never triggers a procurement review or a new negotiation.
🧭 Write the mission first and the values later: A broad mission gave the team direction before the product existed. Values waited twelve months so they described what had actually kept the company alive.
Chapters
How selling Flywheel led to the internal comms idea
Writing the mission statement before the product
The intranet bet and why it never found a through line
Why enterprise email is harder than founders assume
Building a newsletter and resource library before selling
Nine months, near-zero customers, and the plan to return the money
The bar conversation that led to the 30-day email rebuild
Ten customers in 30 days and what product-market fit felt like
💰 Align pricing with the outcome you claim: Uplane charges a fixed fee covering costs plus a variable share of ad spend. Julius says it makes the pitch easier, because he only earns more when the customer's campaigns do better.
🏢 Be reachable faster than an agency can be: Uplane answers customers within 120 seconds. Julius treats speed of response as the main structural advantage an early-stage company has over an incumbent agency.
🧠 Volume is not the constraint anymore: AI made producing ads nearly free, so the bottleneck moved to picking the roughly ten percent that perform. Companies pushing more output without connecting it to analytics are solving the wrong half.
🧠 Disqualify rather than convince: Christian's team filters for buyers who already accept the market is changing. He argues sales has nothing to do with convincing people, and that defending buyers cost too much time to pursue.
🚀 Land wide, then go deep: Enterprise security and procurement cost the same for ten users or a hundred thousand, so Templafy pushed for company-wide rollouts first and expanded into specific team use cases afterwards.
📉 Being too far ahead is a real cost: Templafy's AI messaging ran ahead of what buyers wanted. Christian's rule is that you can be fifteen percent ahead of the market but not eighty, or you lose the conversation entirely.
Chapters
Introduction
What Templafy does and the size of the business
Seeing the cloud shift and spinning out of the on-premise business
Two founders, two engineers, and a year of unlearning
Selling thought leadership instead of product
Targeting 800 people with specific messaging
Raising funding twelve months before the product
Why every enterprise customer is its own market
The ten-person pilot problem
"We didn't say no, we said yes if"
Writing the criteria your competitors get scored on
Disqualification as a sales strategy
Resetting the company again for AI: fifteen percent ahead, not eighty
Uphill skiers, downhill skiers, and the lightning round
🤝 First customers came from where the complaints already were: Reddit's LocalLlama and Ollama communities and Discord were full of people asking how to run models they could not host.
⚡ A constraint you solve for yourself can become the product: They built GPU hot-swapping because they had thousands of fine-tuned models and could not afford thousands of GPUs. That workaround turned out to be the company.
Chapters
What Featherless AI does and the size of the business
Starting as an open source model project
One GPU per model, and not enough money
Building GPU hot-swapping
The weekend the experiment made more money than the platform
