The SaaS Podcast - Real Lessons on Growing Profitable SaaS

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.
This episode is brought to you by:
🤖 Hobbes → Don't book a demo. Take one.
🔑 Key Lessons
👂 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.
🔒 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
Audience-based pricing and dropping per-seat fees
Lightning round
Resources
Full show notes: https://saasclub.io/493
Join 5,000+ SaaS founders: https://saasclub.io/email

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.
This episode is brought to you by:
🤖 Hobbes → Don't book a demo. Take one.
🔑 Key Lessons
🤝 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.
💰 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.
Chapters
Introduction
What Uplane does and the problem it solves
Ten thousand ads by hand
Deciding to leave and build it
The cold LinkedIn outreach that worked
Standing out when everyone uses AI to personalise
The first customer
Why free pilots are a trap
The one-week sprint from call to demo
The 120-second response rule
Throwing out per-seat pricing
Attribution and charging on ad spend
Guardrails and atomic content
Lightning round
Resources
Full show notes: https://saasclub.io/492
Join 5,000+ SaaS founders: https://saasclub.io/email

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.
This episode is brought to you by:
🤖 Hobbes → Don't book a demo. Take one.
🔑 Key Lessons
🏢 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.
🧠 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
Resources
Full show notes: https://saasclub.io/491
Join 5,000+ SaaS founders: https://saasclub.io/email

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.

🤖 Hobbes → Don't book a demo. Take one.

🔑 Key Lessons

🔄 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.

🤝 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

What they hoped to learn from the experiment

Finding demand on Reddit and Discord

The mission: AI beyond English and Chinese

Realizing he was holding his own mission back

Why flat-rate pricing instead of per-token

Removing the explanation and improving conversion

Hosting the long tail of open source models

Competing where no one else is

The Series A and what comes next

Resources

Full show notes: https://saasclub.io/490

Join 5,000+ SaaS founders: https://saasclub.io/email

Five years at $50K ARR. Ten failed projects. Lending the business money out of his own bank account. George Georgiadis came close to shutting Happier Leads down. Instead he broke through the revenue plateau and reached $1.5M ARR with zero employees.
George explains what moved the number: an end-to-end platform instead of a narrow point tool, cold email as his cheapest channel because he owns the mailboxes and the data, and running a SaaS with AI agents he built himself to handle support and bug fixing around the clock.
Plus: why he turned down a $1M offer to sell, and why he is hiring again after reaching seven figures alone.
Happier Leads identifies anonymous website visitors, qualifies them with AI, and engages them by email. George Georgiadis bootstrapped it from a $50,000 AppSumo campaign to $1.5M ARR with no outside capital.
This episode is brought to you by:
🍎 Product Fruits → Book a demo tailored to your product
🔑 Key Lessons
📉 A plateau is a depth problem, not an effort problem: George wore every hat for five years at $50K ARR and never went deep enough on one channel to make the unit economics work.
💰 Own the infrastructure your channel depends on: Building his own mailboxes and using the 175-million-contact database he already owned pushed cold email costs low enough to send millions profitably.
🎯 The point tool that felt like a mistake became the moat: Building identification, qualification, enrichment, and email sending into one platform took seven years, but no competitor covers the full path.
🤝 Cold email works on precision, not personalization theater: He picks the exact company and job title, keeps the message short, and withholds links until the prospect replies to protect deliverability.
🛠️ AI replaces a team only when the data lives in one place: Ripping out HubSpot, Intercom, and Pipedrive for self-built tools gave his AI brain the visibility it needs to fix bugs unattended.
🚀 Lifetime deals buy time, not revenue: The $50,000 AppSumo campaign got consumed by server and data costs within two years, but the reviews, word of mouth, and runway were worth more.
🧠 A solo operator owns a job, not a company: Even at $1.5M ARR with AI doing the heavy lifting, George is hiring because a business that stops when he stops cannot be sold.
Chapters
Cold open: five years stuck, then $1.5M
What Happier Leads does
$1.5M ARR with zero employees, bootstrapped
From Greece to London and 10 failed projects
Where the Happier Leads idea came from
Clearbit quoted $20,000 so he built his own
Funding the product with AppSumo lifetime deals
Buying the data, building the business on top
The real cost and hidden upside of lifetime deals
Five years stuck at $50K ARR
80% development, 20% marketing and sales
Building end to end instead of a point tool
Nearly quitting and lending the business his own money
What finally changed: going deep on unit economics
Cold email becomes the main acquisition channel
What makes cold email work at scale
Running the business with self-built AI agents
Self-healing software and KPI monitoring
Why he's hiring again after zero employees
Lightning round
Resources
Full show notes: https://saasclub.io/489
Join 5,000+ SaaS founders: https://saasclub.io/email