What happens when an analytics company spends nine months teaching an LLM to understand ad revenue data? I talk with Nico Schueller, CEO and co-founder of Adomik, about how the company went from floor optimization on AppNexus to JP, its AI infrastructure for publishers.
We cover:
What’s inside Adomik’s market data set, and why accuracy matters more than completeness
Why JP is infrastructure, not an agent or a chatbot
How a semantic model makes LLM analysis of transactional data reliable
Onboarding speed, and JP’s December release with versions for AdOps, sales and yield
Whether publishers can trust their data again
When to keep a human in the loop
Nico’s contrarian view that enterprise AI belongs in enterprise software companies
This conversation is part of my series on the companies driving AI change in digital media and ad tech.
Chapters
0:00 Why I’m talking to AI-driven ad tech companies
1:29 Adomik’s origin story: AppNexus and floor optimization
3:52 Header bidding and the shift to revenue management
4:45 Building the market data set
5:35 AI and the launch of JP
6:37 What’s inside Adomik’s market data set
13:34 Directional or reliable? Completeness vs. accuracy
16:01 JP is infrastructure, not an agent
17:41 Why the semantic model matters
21:25 Revenue Analyst, Market Analyst and JP as an MCP server
22:48 How long does onboarding a new client take?
26:17 JP’s December release: JP for AdOps, Sales and Yield
28:05 Can publishers trust their data again?
31:47 Guardrails and the human in the loop
35:29 Nico’s contrarian view: enterprise AI is enterprise software
39:43 Where to learn more about Adomik
Learn more about Adomik: https://www.adomik.com/
Connect with Nico on LinkedIn: https://www.linkedin.com/in/nschueller
Article on this conversation:
https://korukeamedia.com/enterprise-ai-is-enterprise-software-what-adomik-learned-building-jp/
I’m James Deaker, The Yield Doctor. Through Korukea Media, I help publishers and ad tech companies with pricing strategy and yield management. https://korukeamedia.com/
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