a16z just led a $28M Series A in Hilbert, that puts AI agents to work on a company’s growth. One of its first customers is a Fortune 10 company.
Most people building with AI agents think more context is the fix.
Nazli Tan (cofounder & CEO) who previously led growth at Getir ($12B) thinks the opposite.
Context usually means someone writes down what a “good customer” or a “risky customer” is and the agent runs with it.
But those definitions are mostly human guesses. They’re not mathematically rigorous.
Agents should never get to define the problem. Ask one the same question twice, and you can get two different answers for which customers are about to leave.
Nazli’s answer is that the definition is already sitting in your data. Years of customers acquired, retained and lost.
Hilbert trains deep learning models on at least 3 to 4 years of that history, and those models decide who’s a good customer and who’s about to leave. Same customer, same answer, every time.
Their agents only work inside those guardrails. They aren’t even allowed to do math beyond rounding.
Why so strict? Her customers use those answers to decide where hundreds of millions in marketing go. If the answer changes every time you ask, nobody will trust it with that budget.
Today, I had Nazli on the pod and she went deeper into where agents break, why anything that touches the money runs on DL models not LLMs, and why she rejects the traditional Silicon Valley growth playbooks.
[YouTube] → [Spotify] → [Apple Podcast]
Also in this episode:
[42:19] Why “more context” makes your AI agents worse
[45:03] How to price AI when a 1% improvement is worth millions
[52:58] Why Silicon Valley growth playbooks don’t work for 99% of enterprises
