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Jul 9, 2017 · 20m · top-founders

715: This CEO Doesn't Care That VC Has Him By Throat

Ryan Seavey · 10m spoken Nathan Latka · 7m spoken
0:00 / 0:00

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In this episode of The Top podcast, host Nathan Latka interviews Ryan Seavey, CEO and co-founder of Nexosis, an automated machine learning API platform that enables developers to build predictive time-series models without in-house data science teams. Seavey outlines Nexosis's developer-first acquisition strategy, consumption-based pricing model, $7 million in venture backing, and rapid month-over-month expansion across more than 100 enterprise organizations.

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 41.7% of the talking time here. How this is scored →

Nathan as informed peer 4.0 Guest teaching 3.1 Guest disagreement 1.6 Nathan pushing back 3.0
05100:0010:0020:001:24–4:35 · Nathan as informed peer 4/10 Introducing Ryan Seavey and Nexosis AI Platform Latka challenges the guest on whether Nexosis is genuine or merely riding the AI marketing hype. Seavey explains their transition from internal retail forecasting to an open developer API.4:36–7:16 · Nathan as informed peer 5/10 Data Layers and External Variables in Machine Learning Models Latka asks how models are trained beyond basic spreadsheets and provides a localized SXSW retail example. Seavey educates on multidimensional feature layers like sentiment analysis and event impact modeling.7:16–9:37 · Nathan as informed peer 6/10 Monetization Strategy and Enterprise Pricing Latka pushes for clarity on unit pricing and what constitutes a prediction versus an API call. Seavey explains the developer-led enterprise land-and-expand pricing model.9:37–13:24 · Nathan as informed peer 6/10 Founding Journey, Developer Adoption, and Team Growth Latka attempts to back into Nexosis's revenue numbers by multiplying enterprise accounts by average contract value. Seavey avoids disclosing exact numbers but concedes the formula is accurate.13:24–16:44 · Nathan as informed peer 6/10 Venture Funding and Prioritizing Consumption Growth Latka presses on churn and MRR metrics, but Seavey rejects that traditional SaaS framing in favor of consumption-based expansion. When Seavey hesitates to share monthly prediction counts, Latka presses until he confirms exceeding one million.16:45–19:15 · Nathan as informed peer 1/10 Sponsor Break: Acuity Scheduling Standard sponsor break followed by the rapid-fire Famous Five question sequence.19:15–20:19 · Nathan as informed peer 0/10 Episode Conclusion and Next Episode Teaser Host monologue summarizing Nexosis key business metrics, VC funding, and closing teaser.1:24–4:35 · Guest teaching 4/10 Introducing Ryan Seavey and Nexosis AI Platform Latka challenges the guest on whether Nexosis is genuine or merely riding the AI marketing hype. Seavey explains their transition from internal retail forecasting to an open developer API.4:36–7:16 · Guest teaching 5/10 Data Layers and External Variables in Machine Learning Models Latka asks how models are trained beyond basic spreadsheets and provides a localized SXSW retail example. Seavey educates on multidimensional feature layers like sentiment analysis and event impact modeling.7:16–9:37 · Guest teaching 4/10 Monetization Strategy and Enterprise Pricing Latka pushes for clarity on unit pricing and what constitutes a prediction versus an API call. Seavey explains the developer-led enterprise land-and-expand pricing model.9:37–13:24 · Guest teaching 3/10 Founding Journey, Developer Adoption, and Team Growth Latka attempts to back into Nexosis's revenue numbers by multiplying enterprise accounts by average contract value. Seavey avoids disclosing exact numbers but concedes the formula is accurate.13:24–16:44 · Guest teaching 5/10 Venture Funding and Prioritizing Consumption Growth Latka presses on churn and MRR metrics, but Seavey rejects that traditional SaaS framing in favor of consumption-based expansion. When Seavey hesitates to share monthly prediction counts, Latka presses until he confirms exceeding one million.16:45–19:15 · Guest teaching 1/10 Sponsor Break: Acuity Scheduling Standard sponsor break followed by the rapid-fire Famous Five question sequence.19:15–20:19 · Guest teaching 0/10 Episode Conclusion and Next Episode Teaser Host monologue summarizing Nexosis key business metrics, VC funding, and closing teaser.1:24–4:35 · Guest disagreement 2/10 Introducing Ryan Seavey and Nexosis AI Platform Latka challenges the guest on whether Nexosis is genuine or merely riding the AI marketing hype. Seavey explains their transition from internal retail forecasting to an open developer API.4:36–7:16 · Guest disagreement 1/10 Data Layers and External Variables in Machine Learning Models Latka asks how models are trained beyond basic spreadsheets and provides a localized SXSW retail example. Seavey educates on multidimensional feature layers like sentiment analysis and event impact modeling.7:16–9:37 · Guest disagreement 2/10 Monetization Strategy and Enterprise Pricing Latka pushes for clarity on unit pricing and what constitutes a prediction versus an API call. Seavey explains the developer-led enterprise land-and-expand pricing model.9:37–13:24 · Guest disagreement 2/10 Founding Journey, Developer Adoption, and Team Growth Latka attempts to back into Nexosis's revenue numbers by multiplying enterprise accounts by average contract value. Seavey avoids disclosing exact numbers but concedes the formula is accurate.13:24–16:44 · Guest disagreement 4/10 Venture Funding and Prioritizing Consumption Growth Latka presses on churn and MRR metrics, but Seavey rejects that traditional SaaS framing in favor of consumption-based expansion. When Seavey hesitates to share monthly prediction counts, Latka presses until he confirms exceeding one million.16:45–19:15 · Guest disagreement 0/10 Sponsor Break: Acuity Scheduling Standard sponsor break followed by the rapid-fire Famous Five question sequence.19:15–20:19 · Guest disagreement 0/10 Episode Conclusion and Next Episode Teaser Host monologue summarizing Nexosis key business metrics, VC funding, and closing teaser.1:24–4:35 · Nathan pushing back 4/10 Introducing Ryan Seavey and Nexosis AI Platform Latka challenges the guest on whether Nexosis is genuine or merely riding the AI marketing hype. Seavey explains their transition from internal retail forecasting to an open developer API.4:36–7:16 · Nathan pushing back 2/10 Data Layers and External Variables in Machine Learning Models Latka asks how models are trained beyond basic spreadsheets and provides a localized SXSW retail example. Seavey educates on multidimensional feature layers like sentiment analysis and event impact modeling.7:16–9:37 · Nathan pushing back 4/10 Monetization Strategy and Enterprise Pricing Latka pushes for clarity on unit pricing and what constitutes a prediction versus an API call. Seavey explains the developer-led enterprise land-and-expand pricing model.9:37–13:24 · Nathan pushing back 4/10 Founding Journey, Developer Adoption, and Team Growth Latka attempts to back into Nexosis's revenue numbers by multiplying enterprise accounts by average contract value. Seavey avoids disclosing exact numbers but concedes the formula is accurate.13:24–16:44 · Nathan pushing back 6/10 Venture Funding and Prioritizing Consumption Growth Latka presses on churn and MRR metrics, but Seavey rejects that traditional SaaS framing in favor of consumption-based expansion. When Seavey hesitates to share monthly prediction counts, Latka presses until he confirms exceeding one million.16:45–19:15 · Nathan pushing back 1/10 Sponsor Break: Acuity Scheduling Standard sponsor break followed by the rapid-fire Famous Five question sequence.19:15–20:19 · Nathan pushing back 0/10 Episode Conclusion and Next Episode Teaser Host monologue summarizing Nexosis key business metrics, VC funding, and closing teaser.

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

0:00 · Nathan 62.3% · guest 37.7%0:00 · Nathan 62.3% · guest 37.7%3:00 · Nathan 12.3% · guest 87.7%3:00 · Nathan 12.3% · guest 87.7%6:00 · Nathan 27% · guest 73%6:00 · Nathan 27% · guest 73%9:00 · Nathan 25.5% · guest 74.5%9:00 · Nathan 25.5% · guest 74.5%12:00 · Nathan 30.1% · guest 69.9%12:00 · Nathan 30.1% · guest 69.9%15:00 · Nathan 72.5% · guest 27.5%15:00 · Nathan 72.5% · guest 27.5%18:00 · Nathan 68.3% · guest 31.7%18:00 · Nathan 68.3% · guest 31.7%
Sharpest disagreement ▶ 14:25 Rejecting standard SaaS churn framing

Seavey firmly dismisses Latka's inquiry regarding churn and MRR, explaining why traditional SaaS subscription metrics are inapplicable to API usage models.

 Hardest push from Nathan ▶ 16:15 Drilling into undisclosed prediction volumes

After Seavey states he prefers not to share usage statistics, Latka refuses to drop the inquiry and prompts with a baseline until Seavey reveals they do over a million predictions monthly.

 Biggest teaching moment ▶ 14:25 Explaining Twilio-style API expansion economics

Seavey educates Latka on API consumption dynamics using Twilio and Uber to illustrate how usage fluctuation differs fundamentally from customer churn.

 Nathan holds their own ▶ 13:01 Constructing implied enterprise revenue equation

Latka demonstrates sharp business acumen by synthesizing disclosed data points—100 enterprise accounts at $10k per month—to calculate an implied $1M monthly run-rate.
the scores for every segment, with the reasoning behind each
ChapterTopicNathan as informed peerGuest teachingGuest disagreementNathan pushing backWhy
Introducing Ryan Seavey and Nexosis AI Platform 4424 Latka challenges the guest on whether Nexosis is genuine or merely riding the AI marketing hype. Seavey explains their transition from internal retail forecasting to an open developer API.
Data Layers and External Variables in Machine Learning Models 5512 Latka asks how models are trained beyond basic spreadsheets and provides a localized SXSW retail example. Seavey educates on multidimensional feature layers like sentiment analysis and event impact modeling.
Monetization Strategy and Enterprise Pricing 6424 Latka pushes for clarity on unit pricing and what constitutes a prediction versus an API call. Seavey explains the developer-led enterprise land-and-expand pricing model.
Founding Journey, Developer Adoption, and Team Growth 6324 Latka attempts to back into Nexosis's revenue numbers by multiplying enterprise accounts by average contract value. Seavey avoids disclosing exact numbers but concedes the formula is accurate.
Venture Funding and Prioritizing Consumption Growth 6546 Latka presses on churn and MRR metrics, but Seavey rejects that traditional SaaS framing in favor of consumption-based expansion. When Seavey hesitates to share monthly prediction counts, Latka presses until he confirms exceeding one million.
Sponsor Break: Acuity Scheduling 1101 Standard sponsor break followed by the rapid-fire Famous Five question sequence.
Episode Conclusion and Next Episode Teaser 0000 Host monologue summarizing Nexosis key business metrics, VC funding, and closing teaser.

Statements from this episode (9)

Assertion Not checkable as stated
Seavey: About 300 developers use Nexosis API for machine learning
“Today we have about 300 different developers that are all using our API to do time series type of problems or impact analysis type of problems, and they just make an API call, and now they're actually using artificial intelligence or machine learning without h…”
Ryan Seavey Jul 9, 2017 ▶ 2:22
Disclosure
Seavey: Sports bars use Nexosis to predict revenue from game matchups
“We see a lot of sporting bars and things of that nature use the system they're trying to use it to understand You know, what's the difference between two really good baseball teams playing versus maybe a good baseball team and a bad baseball team, right?”
Ryan Seavey Jul 9, 2017 ▶ 7:01
Disclosure
Nexosis charges developers 10 cents per 1,000 API predictions
“So we charge about 10 cents per 1000 predictions.”
Ryan Seavey Jul 9, 2017 ▶ 7:42
Disclosure
Nexosis enterprise pricing starts at $10,000 per month
“Typically it starts around 10 grand a month and kind of scales up from there, but again, I mean, it really just depends on how much data they have and what kind of prediction interval they're looking at.”
Ryan Seavey Jul 9, 2017 ▶ 9:24
Assertion Not checkable as stated
Nexosis processes millions of API calls every month
“Yeah, we have Millions of API calls every month, but what we're really focused on right now as a company is how many developers have signed up for the platform, and then of those developers that have signed up, how many of them are actually putting real applic…”
Ryan Seavey Jul 9, 2017 ▶ 10:39
Assertion Not checkable as stated
Nexosis counts approximately 100 unique enterprise users on its API
“Yeah, so we have approximately about a hundred different enterprise types of developers, unique enterprises on the API today.”
Ryan Seavey Jul 9, 2017 ▶ 11:54
Disclosure
Seavey: Nexosis has raised just under $7M in total funding
“We've raised a little less than seven million dollars to date.”
Ryan Seavey Jul 9, 2017 ▶ 13:30
Assertion Not checkable as stated
Nexosis claims 100% month-over-month API consumption growth
“We're aiming for at least a hundred percent month over month. And so far we fit that every single month.”
Ryan Seavey Jul 9, 2017 ▶ 15:38
Assertion Not checkable as stated
Seavey: Nexosis platform prediction consumption exceeds 1M
“So it's over a million.”
Ryan Seavey Jul 9, 2017 ▶ 16:38
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