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Jun 30, 2018 · 15m · top-founders

1071 His AI Tool Tells You How To Price Realtime

Adam Treiser · 7m spoken Nathan Latka · 6m spoken
0:00 / 0:00

gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

In this episode of The Top Entrepreneurs, host Nathan Latka interviews Adam Treiser, founder and CEO of Arjuna Solutions, exploring how his company productizes real-time AI pricing decisions. Treiser details their unique per-decision business model, proprietary data handling, and capital-efficient growth from 7 million to 17 million processed units.

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 42.2% of the talking time here. How this is scored →

Nathan as informed peer 5.3 Guest teaching 3.3 Guest disagreement 1.5 Nathan pushing back 2.3
05100:0010:002:41–7:45 · Nathan as informed peer 6/10 Data Sourcing, Customer Price Elasticity, and Dynamic Discounting Adam corrects Nathan's assumption that the algorithm scrapes third-party purchase data, explaining they rely strictly on first-party business data and dynamic discounting. Nathan quickly grasps the per-decision pricing structure and accurately calculates the company's revenue run rate from volume numbers.7:46–10:51 · Nathan as informed peer 5/10 Fundraising History and Unit Volume Growth Trajectory Nathan drills into the friends-and-family fundraising round, expressing surprise at a priced equity round and playfully teasing Adam about whether his non-professional investors were unsophisticated. The discussion remains lighthearted as they review unit volume growth.10:53–13:51 · Nathan as informed peer 6/10 API Integrations, Data Privacy, and AI Knowledge Accumulation Nathan uses Clearbit as an industry benchmark to ask how API integrations trigger automatic pricing adjustments and whether terms of service allow cross-client data training. Adam educates him on how generalized knowledge is abstracted without commingling client data, invoking a Warren Buffett analogy.13:52–15:43 · Nathan as informed peer 4/10 The Famous Five Questions and Interview Conclusion A collaborative Famous Five sequence covering Adam's background and favorite business influences, followed by Nathan providing a succinct summary of Arjuna Solutions' business metrics in his outro.2:41–7:45 · Guest teaching 5/10 Data Sourcing, Customer Price Elasticity, and Dynamic Discounting Adam corrects Nathan's assumption that the algorithm scrapes third-party purchase data, explaining they rely strictly on first-party business data and dynamic discounting. Nathan quickly grasps the per-decision pricing structure and accurately calculates the company's revenue run rate from volume numbers.7:46–10:51 · Guest teaching 2/10 Fundraising History and Unit Volume Growth Trajectory Nathan drills into the friends-and-family fundraising round, expressing surprise at a priced equity round and playfully teasing Adam about whether his non-professional investors were unsophisticated. The discussion remains lighthearted as they review unit volume growth.10:53–13:51 · Guest teaching 5/10 API Integrations, Data Privacy, and AI Knowledge Accumulation Nathan uses Clearbit as an industry benchmark to ask how API integrations trigger automatic pricing adjustments and whether terms of service allow cross-client data training. Adam educates him on how generalized knowledge is abstracted without commingling client data, invoking a Warren Buffett analogy.13:52–15:43 · Guest teaching 1/10 The Famous Five Questions and Interview Conclusion A collaborative Famous Five sequence covering Adam's background and favorite business influences, followed by Nathan providing a succinct summary of Arjuna Solutions' business metrics in his outro.2:41–7:45 · Guest disagreement 2/10 Data Sourcing, Customer Price Elasticity, and Dynamic Discounting Adam corrects Nathan's assumption that the algorithm scrapes third-party purchase data, explaining they rely strictly on first-party business data and dynamic discounting. Nathan quickly grasps the per-decision pricing structure and accurately calculates the company's revenue run rate from volume numbers.7:46–10:51 · Guest disagreement 2/10 Fundraising History and Unit Volume Growth Trajectory Nathan drills into the friends-and-family fundraising round, expressing surprise at a priced equity round and playfully teasing Adam about whether his non-professional investors were unsophisticated. The discussion remains lighthearted as they review unit volume growth.10:53–13:51 · Guest disagreement 2/10 API Integrations, Data Privacy, and AI Knowledge Accumulation Nathan uses Clearbit as an industry benchmark to ask how API integrations trigger automatic pricing adjustments and whether terms of service allow cross-client data training. Adam educates him on how generalized knowledge is abstracted without commingling client data, invoking a Warren Buffett analogy.13:52–15:43 · Guest disagreement 0/10 The Famous Five Questions and Interview Conclusion A collaborative Famous Five sequence covering Adam's background and favorite business influences, followed by Nathan providing a succinct summary of Arjuna Solutions' business metrics in his outro.2:41–7:45 · Nathan pushing back 3/10 Data Sourcing, Customer Price Elasticity, and Dynamic Discounting Adam corrects Nathan's assumption that the algorithm scrapes third-party purchase data, explaining they rely strictly on first-party business data and dynamic discounting. Nathan quickly grasps the per-decision pricing structure and accurately calculates the company's revenue run rate from volume numbers.7:46–10:51 · Nathan pushing back 3/10 Fundraising History and Unit Volume Growth Trajectory Nathan drills into the friends-and-family fundraising round, expressing surprise at a priced equity round and playfully teasing Adam about whether his non-professional investors were unsophisticated. The discussion remains lighthearted as they review unit volume growth.10:53–13:51 · Nathan pushing back 3/10 API Integrations, Data Privacy, and AI Knowledge Accumulation Nathan uses Clearbit as an industry benchmark to ask how API integrations trigger automatic pricing adjustments and whether terms of service allow cross-client data training. Adam educates him on how generalized knowledge is abstracted without commingling client data, invoking a Warren Buffett analogy.13:52–15:43 · Nathan pushing back 0/10 The Famous Five Questions and Interview Conclusion A collaborative Famous Five sequence covering Adam's background and favorite business influences, followed by Nathan providing a succinct summary of Arjuna Solutions' business metrics in his outro.

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

0:00 · Nathan 41.1% · guest 58.9%0:00 · Nathan 41.1% · guest 58.9%3:00 · Nathan 26.1% · guest 73.9%3:00 · Nathan 26.1% · guest 73.9%6:00 · Nathan 41.1% · guest 58.9%6:00 · Nathan 41.1% · guest 58.9%9:00 · Nathan 70.9% · guest 29.1%9:00 · Nathan 70.9% · guest 29.1%12:00 · Nathan 20.7% · guest 79.3%12:00 · Nathan 20.7% · guest 79.3%15:00 · Nathan 81.3% · guest 18.7%15:00 · Nathan 81.3% · guest 18.7%
Sharpest disagreement ▶ 2:49 Dismissing third-party scraping assumption

Adam immediately counters Nathan's premise that they jack up prices using external Amazon purchase tracking, clarifying they rely strictly on internal CRM data.

 Hardest push from Nathan ▶ 8:28 Drilling into priced family round terms

Nathan challenges the idea of a priced equity round among family and friends, pressing Adam on who negotiated the valuation and teasing him about unsophisticated relatives.

 Biggest teaching moment ▶ 4:20 Defining productized AI pricing model

Adam reframes Nathan's SaaS comparison by explaining how selling individual AI pricing decisions differs fundamentally from recurring software subscriptions.

 Nathan holds their own ▶ 6:27 Instant ARR calculation from unit volume

Nathan leverages Adam's unit volume and per-decision fee data to instantly extrapolate and pin down the company's estimated annual revenue run rate.
the scores for every segment, with the reasoning behind each
ChapterTopicNathan as informed peerGuest teachingGuest disagreementNathan pushing backWhy
Data Sourcing, Customer Price Elasticity, and Dynamic Discounting 6523 Adam corrects Nathan's assumption that the algorithm scrapes third-party purchase data, explaining they rely strictly on first-party business data and dynamic discounting. Nathan quickly grasps the per-decision pricing structure and accurately calculates the company's revenue run rate from volume numbers.
Fundraising History and Unit Volume Growth Trajectory 5223 Nathan drills into the friends-and-family fundraising round, expressing surprise at a priced equity round and playfully teasing Adam about whether his non-professional investors were unsophisticated. The discussion remains lighthearted as they review unit volume growth.
API Integrations, Data Privacy, and AI Knowledge Accumulation 6523 Nathan uses Clearbit as an industry benchmark to ask how API integrations trigger automatic pricing adjustments and whether terms of service allow cross-client data training. Adam educates him on how generalized knowledge is abstracted without commingling client data, invoking a Warren Buffett analogy.
The Famous Five Questions and Interview Conclusion 4100 A collaborative Famous Five sequence covering Adam's background and favorite business influences, followed by Nathan providing a succinct summary of Arjuna Solutions' business metrics in his outro.

Statements from this episode (8)

Disclosure
Treiser: Arjuna trains pricing algorithms on clients' first-party CRM and email data
“The data we use will come from the businesses themselves. So it oftentimes be the CRM data, the email data. Social media interaction data.”
Adam Treiser Jun 30, 2018 ▶ 2:50
Opinion
Treiser: Arjuna is first company to productize artificial intelligence
“What we've done here at our soon is we're really the first company to have productized artificial intelligence.”
Adam Treiser Jun 30, 2018 ▶ 4:21
Disclosure
Arjuna charges 10 to 12 cents per pricing decision
“The average price that we charge is between 10 and 12 cents for each price point decision.”
Adam Treiser Jun 30, 2018 ▶ 5:57
Disclosure
Arjuna processed nearly 17 million decision units in 2017
“Last year we did about I believe it was near a seventeen million units that we don't, that not all those units are actually paid.”
Adam Treiser Jun 30, 2018 ▶ 6:08
Prediction Not checkable as stated
Treiser: Arjuna will pass $1M ARR pretty soon
“I think we're going to pass it pretty soon.”
Adam Treiser Jun 30, 2018 ▶ 6:42
Assertion Not checkable as stated
Treiser: Arjuna Solutions' decision volume grew 116% year-over-year
“So it's growing year over year at about a 116, a 116%. And those are the numbers as of D 31.”
Adam Treiser Jun 30, 2018 ▶ 9:00
Disclosure
Treiser: Arjuna improves AI models without commingling customer data
“Every group we work with, our algorithms get better, they get smarter, but none of the actual data for any group gets ever basically commingled, aggregated, so effectively the model may itself may get smarter. The AI learns, and it's acquiring more knowledge, …”
Adam Treiser Jun 30, 2018 ▶ 12:06
Insight
Treiser: AI value is domain expertise rather than infrastructure
“When you think of AI, that's where we really see the opportunity going forward, which is, it's not so much about infrastructure, but, and about the ability for machine to learn, but what machine, what do you want your machine to actually learn? Because you wan…”
Adam Treiser Jun 30, 2018 ▶ 13:31
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