The SaaS Podcast: Founder Interviews - Page 6

Nate Sanders, Artifact

How This AI SaaS Solved the Cold Start Data Problem

While working at Pluralsight, Nate Sanders spent 70-85% of his time doing something most product leaders dread - manually synthesizing customer research data across departments. Teams were running thousands of customer interviews a year, but the synthesis was happening in glass conference rooms covered in sticky notes, with 10 people earning $150K+ spending two days on affinity mapping exercises. When the first large language model, BERT, emerged in 2018, Nate ran internal experiments and saw promising results. He and co-founder Trey spent seven months of nights and weekends building prototypes in React, testing half a dozen different product ideas before raising a small angel round from friends and family in the Salt Lake City area. But building an AI SaaS product created a painful chicken-and-egg problem. Artifact needed training data to fine-tune its models, but getting training data required having customers, and customers required a working product. Nate solved this by recruiting nearly a dozen design partners through his network, offering free product access in exchange for their data, feedback, and 30 minutes of weekly time. Each partner signed letters of intent with paid deposits of $1,000 to $1,500 - small enough to vanish on a corporate card, but enough to prove real commitment. With three to four active design partners collaborating weekly, Artifact closed its first $100K in ARR and raised a $5M seed round led by Josh Buckley of Buckley Ventures. But scaling this AI SaaS beyond those initial partners proved harder than expected. The team tried bottoms-up self-service, community building, dinner events in San Francisco, and conferences - spending $30K-$40K on events alone before realizing none of these channels attracted the right buyers. The breakthrough came when Nate discovered that outbound SDR-driven outreach to enterprise accounts produced dramatically better retention, higher ACVs, and stronger engagement than any other channel. Going down-market was tempting because smaller companies moved faster, but enterprise customers had 90% higher ACVs and significantly lower churn. Nate's AI SaaS playbook became clear: double down on a structured SDR-to-AE pipeline with commitment-based sales stages, replacing subjective pipeline assessments with concrete customer actions like sending data source lists and authenticating integrations.

John Li, Vimcal

70% Close Rate: The 4-Step SaaS Onboarding Call Framework

In 2017, John Li and his co-founder Michael got accepted into Y Combinator with an augmented reality idea. The market was not ready. They pivoted to a fitness app. That did not get traction either. After two years and two failed products, they had eight months of runway left. In 2019, they bet everything on their third idea: Vimcal, a calendar app designed to be the fastest scheduling tool for busy founders, VCs, and remote teams. But building a calendar app proved harder than expected, and by launch day they had just 1.5 months of cash remaining. They had already prepared legal documents to shut down the company. John's survival tactic was SaaS onboarding calls. He got on a 30-minute Zoom with every single user, perfected a demo script, and asked for a $15 subscription at the end of each call. His close rate hit 70%. When users matched the qualifying profile of using Calendly, Google Calendar, and Superhuman, the close rate jumped to over 90%. One of those SaaS onboarding calls changed everything. A user offered to write a $5,000 investment check instead of paying $15. More checks followed. John used the calls as both a conversion tool and a fundraising vehicle during the early months of COVID, when no one else was writing checks. John also built a waitlist with a Typeform that acted as a qualifying filter. Of tens of thousands of signups, Vimcal only let in about 20%. The first question asked whether the user felt overwhelmed by meetings, and 92% said yes. Qualifying questions about which apps they used and how many external meetings they had determined who got access. The company later stumbled when it dropped both the waitlist and the onboarding calls on the same day. Growth slowed for six to eight months until they rebuilt momentum by shipping every two weeks, landing a TechCrunch feature, and reintroducing onboarding call prompts in the self-serve flow. Today, Vimcal is a nine-person team that has raised $2.4M and is targeting $1.5M ARR with profitability in sight. John also launched Vimcal Maestro, the first calendar built specifically for executive assistants.

The SaaS Podcast

7 Years to Find Product-Market Fit and Hit $1M ARR

From 25-Minute Cold Emails to 7-Figure Founder-Led Sales

How SaaS Content Marketing Built a 7-Figure Developer Tool

Why SaaS Partnerships Work With Just Hundreds of Customers

116 LinkedIn Conversations That Built an 8-Figure SaaS

Lost 90% of Revenue Then Made Retention the Growth Engine

Why Selling to Startups Stopped Working for This SaaS

5 Years to Find Product-Market Fit Then Revenue Took Off

How This AI SaaS Solved the Cold Start Data Problem

This CEO Still Does 5 Demos a Day - Scaling SaaS

How SaaS Content Marketing Built an 8-Figure Business

From Nice to Have to Must Have: Repositioning a SaaS Product

Horizontal Product, Zero Focus, Then 100 Customers in 100 Days

He Waited 10 Years to Launch - Then Grew 400% Year Over Year

How a Weekend Data Analysis Closed an Enterprise Deal

Why Your SaaS Product Design Fails Without Wireframes

How Sitting in Back Offices Built a Vertical SaaS

2 Years of Discovery Before Writing a Line of Code

From 60% Churn to Zero - How Howwe Fixed SaaS Retention

5 Lessons on Finding Product-Market Fit and Scaling

From Services to SaaS by Selling One Customer for 2 Years

70% Close Rate: The 4-Step SaaS Onboarding Call Framework

Scaling SaaS With No Sales Team Using Automation

100 Interviews Before Code: Finding Product-Market Fit