Frontier AI labs have no shortage of companies willing to collect data for them.
Their harder problem is determining which data will actually make their models better.
José Nunes built Vetto to solve that problem. Instead of waiting for a lab to specify the data it needs, Vetto studies where a model is underperforming and designs the work required to improve it.
That changes Vetto’s role.
Most data suppliers receive guidelines from the labs and find people to execute them. Vetto works one step earlier: its researchers design the guidelines themselves.
To determine whether those guidelines work, they build benchmarks and train small models on each dataset. The results show the labs how the model performed before and after training.
If the model does not improve, the data is not good.
This ability to demonstrate improvement is also what drives the business. The research gives a lab a reason to buy before a traditional sales process begins.
Today, José is 25 and Vetto is doing eight figures a year. It tripled revenue several months in a row, with no sales team or paid marketing.
As José told me:
“What you do with the data is as important as having the data.”
In the episode, José explains how Vetto became a research partner to the AI labs and why deciding what data to collect is more valuable than collecting it.
Presented by Ashurst Perkins Coie. Paul Navarro works with some of the best founders in Latin America, many of them guests on the podcast. I always ask my guests who I should interview next, and the only time anyone recommended a lawyer, it was Paul. Glad to have them as a founding sponsor.
[YouTube] → [Spotify] → [Apple Podcast]
Also in this episode:
[03:15] His journey from a small town in Brazil to Silicon Valley
[16:20] How Vetto competes with the labs in research
[18:05] Why the most valuable part of AI data happens before collection
[19:15] How Vetto recruits researchers it cannot outbid the frontier labs for
[57:20] Why researchers hired in Brazil can perform alongside people from DeepMind
