The Self-Improving Company | Kavak's AI Playbook

**Alejandro Maza Ayala** (0:00)
I'm investing more today in tokens than in knowledge workers.
We could build superhuman agents. This means that by every dimension that matters, our agents would outperform the best human we had ever hired.

**Gabriel Vasquez** (0:15)
The most ambitious companies listening to this will decide to follow suit, which is you decided to build an agent per customer.

**Angela Strange** (0:22)
Yes.

**Alejandro Maza Ayala** (0:23)
Every day between 100 and 200,000 agents get instantiated, specifically for this customer with its own virtual machine.

**SPEAKER_4** (0:32)
There's a lot of people worried about how the organizations of the future are going to look like, and the role that humans are going to play.

**Alejandro Maza Ayala** (0:39)
If you haven't faced fear before, you haven't felt it, then you haven't tried AI. We launched a program inside Kavak that's called the Jedi Academy. From the CEO to AI engineers, to mechanics, we train everyone. And after six weeks, they launch state-of-the-art agents to production.

**Gabriel Vasquez** (1:00)
What advice do you have to future founders or first-time founders that might be listening?

**Alejandro Maza Ayala** (1:05)
What works right now is...

**Angela Strange** (1:06)
Most companies are asking how to add AI to the organization. Kavak asked a much more radical question. What would we build if we were starting the company from scratch with AI?
Angela Strange and Gabriel Vasquez sit down with Kavak's chief product and AI officer, Alejandro Ayala, to unpack what happened when the company bet on rebuilding itself around agents. Today, hundreds of thousands of agents can be instantiated each day, handling everything from selling and financing cars to maintaining long-term customer relationships. They discuss why Kavak tore down an agent architecture that was already working to start again, how evals became the foundation for moving faster, and what happens when agents don't just work for humans, but humans sometimes work for agents.

**SPEAKER_4** (1:53)
Welcome back to the ACC Podcast. Today, we have Ale Maza, the head of AI at Kavak. We're going to discuss today the transformation that Ale led within Kavak to turn into an AI-native company. Thank you, Ale, for being with us today.

**Alejandro Maza Ayala** (2:05)
Thanks for having me.

**SPEAKER_4** (2:06)
Before starting at Kavak, you were running a company called Oppy Analytics.

**Alejandro Maza Ayala** (2:11)
That's right.

**SPEAKER_4** (2:12)
And you were very much into AI before ChaiGBT. Do you want to tell us a little bit about that journey?

**Alejandro Maza Ayala** (2:18)
Yes, yes, of course. Well, we called it machine learning back then. It was a different family of algorithms. And we founded the company with this very ambitious vision there that new machine learning models would be so powerful that they could solve any complex problem. This was pre-transformers, right? This was like 2013
So we started building the company that way. And I think we were like 10 years ahead of time, but we built a great company. We served 14, 500 companies around like risk algorithms, logistics, forecasting, marketing, but really the power of what transformers and then the chagy pitty moment, when it arrived, make things like very clearly that we could now build a whole new company and way of building companies. And we joined Kavak and Carlos to build it.

**Gabriel Vasquez** (3:12)
Amazing. All right. So we're going to spend the bulk of this podcast talking about exactly how you've identified Kavak, but maybe just to start, what does Kavak do and what is your role there?

**Alejandro Maza Ayala** (3:22)
Kavak started out as a used car marketplace. So we buy cars, we refurbish them, and then we sell them and finance them. But to do that, we also had to build a fintech and a logistics company and the car facts and like basically all the infrastructure for this to work didn't exist in La Tams so we had to build everything vertically so we could serve our customers the right way.

**Gabriel Vasquez** (3:48)
I'm going to sort of start with the framing of what the architecture looks like. So a consumer comes in and says, I want to sell my car. Like how many agents do they touch? What's the harness look like? Ground us in how you design this.

**Alejandro Maza Ayala** (4:00)
So we bet the company in transforming to a company run by agents. The questions we ask ourselves is, how would we build Kavak in 2035 with fabled 10 or GPT-10 level intelligence? And actually that company looks very different than what we had built or what we had back then.
So when a customer comes in right now, agent will get spawned specifically for this customer with its own virtual machine. It will remember years of interaction of these customers with Kavak, what they visited in the web page or a call they had two years ago, remember everything in its memory, come up with a strategy and set a long term goal to maximize the lifetime value of this customer and do whatever it takes to make the customer happy and convert them into all their different products across time. And this is a completely new and ground breaking architecture at scale, I think, because people are still building multi-agent system with experts and we realize to bet that long running agents with hard goals, not just workflows, could maximize our customer's satisfaction and obviously their lifetime value.