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Nov 3, 2017 · 16m · top-founders

832: SaaS: Machine Learning and AI for Re-Engaging Customers, $250k ACV and $1.5m Raised

Victor Szczerba · 8m spoken Nathan Latka · 6m spoken
0:00 / 0:00

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In this episode of The Top, host Nathan Latka interviews Yeti Data CEO Victor Szczerba to explore how the enterprise SaaS startup built an AI-driven virtual data warehouse on a lean budget, scaling to an $800k ARR run rate with $250k–$500k enterprise contract values ahead of an institutional Series A round.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Nathan holds 43.9% of the talking time here. How this is scored →

Nathan as informed peer 5.0 Guest teaching 4.2 Guest disagreement 1.8 Nathan pushing back 4.0
05100:0010:000:59–3:34 · Nathan as informed peer 4/10 Promotion of GetLatka SaaS Database Platform After opening promotional material, Nathan introduces Victor and asks for specifics regarding Yeti Data's contract values and pricing mechanics. Victor explains their unmetered enterprise pricing model rather than traditional per-seat gating.3:34–6:40 · Nathan as informed peer 5/10 Deconstructing Machine Learning and Enterprise Data Unification Nathan challenges Victor on whether Yeti Data genuinely uses machine learning or is merely adopting Palo Alto buzzwords. Victor demystifies ML by comparing it to longstanding actuarial modeling and details their predictive purchase logic.6:40–9:34 · Nathan as informed peer 6/10 Customer Traction and ARR Growth Projections Nathan presses Victor to pin down his customer count and estimate current ARR run rate around $800k. Victor explains how they structured their $1.5M convertible note with dynamic early-bird discount tiers.9:34–12:41 · Nathan as informed peer 5/10 Competitive Landscape and Data Virtualization Advantage Victor contrasts Yeti Data against legacy giants like IBM and Teradata, describing traditional consulting-heavy integration as inefficient. He details how data virtualization reduces onboarding from years to weeks.12:41–16:06 · Nathan as informed peer 5/10 Target Metrics for Upcoming Series A Round Nathan pushes Victor on his planned Series A fundraising metrics, teasing him about aggressive Silicon Valley valuations when Victor targets a $15M-$20M pre-money valuation on $1M ARR.0:59–3:34 · Guest teaching 3/10 Promotion of GetLatka SaaS Database Platform After opening promotional material, Nathan introduces Victor and asks for specifics regarding Yeti Data's contract values and pricing mechanics. Victor explains their unmetered enterprise pricing model rather than traditional per-seat gating.3:34–6:40 · Guest teaching 6/10 Deconstructing Machine Learning and Enterprise Data Unification Nathan challenges Victor on whether Yeti Data genuinely uses machine learning or is merely adopting Palo Alto buzzwords. Victor demystifies ML by comparing it to longstanding actuarial modeling and details their predictive purchase logic.6:40–9:34 · Guest teaching 4/10 Customer Traction and ARR Growth Projections Nathan presses Victor to pin down his customer count and estimate current ARR run rate around $800k. Victor explains how they structured their $1.5M convertible note with dynamic early-bird discount tiers.9:34–12:41 · Guest teaching 6/10 Competitive Landscape and Data Virtualization Advantage Victor contrasts Yeti Data against legacy giants like IBM and Teradata, describing traditional consulting-heavy integration as inefficient. He details how data virtualization reduces onboarding from years to weeks.12:41–16:06 · Guest teaching 2/10 Target Metrics for Upcoming Series A Round Nathan pushes Victor on his planned Series A fundraising metrics, teasing him about aggressive Silicon Valley valuations when Victor targets a $15M-$20M pre-money valuation on $1M ARR.0:59–3:34 · Guest disagreement 1/10 Promotion of GetLatka SaaS Database Platform After opening promotional material, Nathan introduces Victor and asks for specifics regarding Yeti Data's contract values and pricing mechanics. Victor explains their unmetered enterprise pricing model rather than traditional per-seat gating.3:34–6:40 · Guest disagreement 3/10 Deconstructing Machine Learning and Enterprise Data Unification Nathan challenges Victor on whether Yeti Data genuinely uses machine learning or is merely adopting Palo Alto buzzwords. Victor demystifies ML by comparing it to longstanding actuarial modeling and details their predictive purchase logic.6:40–9:34 · Guest disagreement 1/10 Customer Traction and ARR Growth Projections Nathan presses Victor to pin down his customer count and estimate current ARR run rate around $800k. Victor explains how they structured their $1.5M convertible note with dynamic early-bird discount tiers.9:34–12:41 · Guest disagreement 2/10 Competitive Landscape and Data Virtualization Advantage Victor contrasts Yeti Data against legacy giants like IBM and Teradata, describing traditional consulting-heavy integration as inefficient. He details how data virtualization reduces onboarding from years to weeks.12:41–16:06 · Guest disagreement 2/10 Target Metrics for Upcoming Series A Round Nathan pushes Victor on his planned Series A fundraising metrics, teasing him about aggressive Silicon Valley valuations when Victor targets a $15M-$20M pre-money valuation on $1M ARR.0:59–3:34 · Nathan pushing back 2/10 Promotion of GetLatka SaaS Database Platform After opening promotional material, Nathan introduces Victor and asks for specifics regarding Yeti Data's contract values and pricing mechanics. Victor explains their unmetered enterprise pricing model rather than traditional per-seat gating.3:34–6:40 · Nathan pushing back 5/10 Deconstructing Machine Learning and Enterprise Data Unification Nathan challenges Victor on whether Yeti Data genuinely uses machine learning or is merely adopting Palo Alto buzzwords. Victor demystifies ML by comparing it to longstanding actuarial modeling and details their predictive purchase logic.6:40–9:34 · Nathan pushing back 5/10 Customer Traction and ARR Growth Projections Nathan presses Victor to pin down his customer count and estimate current ARR run rate around $800k. Victor explains how they structured their $1.5M convertible note with dynamic early-bird discount tiers.9:34–12:41 · Nathan pushing back 3/10 Competitive Landscape and Data Virtualization Advantage Victor contrasts Yeti Data against legacy giants like IBM and Teradata, describing traditional consulting-heavy integration as inefficient. He details how data virtualization reduces onboarding from years to weeks.12:41–16:06 · Nathan pushing back 5/10 Target Metrics for Upcoming Series A Round Nathan pushes Victor on his planned Series A fundraising metrics, teasing him about aggressive Silicon Valley valuations when Victor targets a $15M-$20M pre-money valuation on $1M ARR.

speaking balance: gold is Nathan, purple is the guest (3 minute bins)

0:00 · Nathan 82.7% · guest 17.3%0:00 · Nathan 82.7% · guest 17.3%3:00 · Nathan 26.9% · guest 73.1%3:00 · Nathan 26.9% · guest 73.1%6:00 · Nathan 24% · guest 76%6:00 · Nathan 24% · guest 76%9:00 · Nathan 21.7% · guest 78.3%9:00 · Nathan 21.7% · guest 78.3%12:00 · Nathan 52.2% · guest 47.8%12:00 · Nathan 52.2% · guest 47.8%15:00 · Nathan 64.8% · guest 35.2%15:00 · Nathan 64.8% · guest 35.2%
Sharpest disagreement ▶ 4:15 Pushing back against buzzword dismissal

Victor rejects Nathan's skepticism that their technology is empty marketing hype, countering that machine learning is rigorous mathematics akin to century-old actuarial work.

 Hardest push from Nathan ▶ 12:55 Challenging aggressive Series A valuation targets

Nathan refuses to let Victor give vague answers regarding his funding milestones and questions whether a $15M-$20M valuation on $1M ARR is realistic outside Palo Alto.

 Biggest teaching moment ▶ 10:55 Explaining data virtualization mechanics

Victor leverages his enterprise background at SAP to explain how virtualizing data connections replaces years of complex ETL pipelines with metadata descriptions in weeks.

 Nathan holds their own ▶ 8:20 Drilling down into convertible note terms

Nathan demonstrates sharp venture finance knowledge by actively breaking down and clarifying Victor's convertible note discount structure and time-based teaser mechanics.
the scores for every segment, with the reasoning behind each
ChapterTopicNathan as informed peerGuest teachingGuest disagreementNathan pushing backWhy
Promotion of GetLatka SaaS Database Platform 4312 After opening promotional material, Nathan introduces Victor and asks for specifics regarding Yeti Data's contract values and pricing mechanics. Victor explains their unmetered enterprise pricing model rather than traditional per-seat gating.
Deconstructing Machine Learning and Enterprise Data Unification 5635 Nathan challenges Victor on whether Yeti Data genuinely uses machine learning or is merely adopting Palo Alto buzzwords. Victor demystifies ML by comparing it to longstanding actuarial modeling and details their predictive purchase logic.
Customer Traction and ARR Growth Projections 6415 Nathan presses Victor to pin down his customer count and estimate current ARR run rate around $800k. Victor explains how they structured their $1.5M convertible note with dynamic early-bird discount tiers.
Competitive Landscape and Data Virtualization Advantage 5623 Victor contrasts Yeti Data against legacy giants like IBM and Teradata, describing traditional consulting-heavy integration as inefficient. He details how data virtualization reduces onboarding from years to weeks.
Target Metrics for Upcoming Series A Round 5225 Nathan pushes Victor on his planned Series A fundraising metrics, teasing him about aggressive Silicon Valley valuations when Victor targets a $15M-$20M pre-money valuation on $1M ARR.

Statements from this episode (7)

Assertion Not checkable as stated
Szczerba: Yeti Data customers pay $250k to $500k annually
“Average customers is paying us around two 50 to 500 K a year.”
Victor Szczerba Nov 3, 2017 ▶ 2:45
Insight
Szczerba: Data products should avoid seat-based pricing to maximize customer value
“In fact, we love universal usage of our data with inside of a customer, right? The more they use, the more valuable we are to them. And so we don't want to put in any kind of artificial barriers and saying, oh my God, there's only this many people get these re…”
Victor Szczerba Nov 3, 2017 ▶ 2:58
Opinion
Szczerba: Machine learning is standard actuarial science used for a century
“Machine learning is something very, very specific, and not very hocus pocus, right? I mean, machine learning was something that I don't know, Actuaries inside the insurance industry have been using for, you know, a hundred years.”
Victor Szczerba Nov 3, 2017 ▶ 4:29
Assertion Not checkable as stated
Szczerba: Yeti Data serves fewer than half a dozen customers
“So we have, we're less than half a dozen.”
Victor Szczerba Nov 3, 2017 ▶ 6:45
Disclosure
Yeti Data offered a 30-20-10% decaying discount on its convertible note
“So, 30% discount if you discount, if you came in in the first 30 days of fundraising, then 20% in the second day, and then 10% after that.”
Victor Szczerba Nov 3, 2017 ▶ 8:39
Insight
Szczerba: Professional services revenue is a failure for a software company
“Quite frankly, we see professional services revenue as a fail.”
Victor Szczerba Nov 3, 2017 ▶ 12:23
Prediction Not checkable as stated
Szczerba: $1M ARR will secure a $15M to $20M Series A valuation
“I think a good AIR at a million bucks is gonna get us a really nice Series A. Where you're selling 10, 20% of the company in good terms? 10, 20% of the company in good terms, you know pre-money valuation between the 15 to twenty-ish.”
Victor Szczerba Nov 3, 2017 ▶ 13:01
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