Kavak AI转型：从交易型到关系型，96%交互由智能体处理

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 if we had ever hired.

The most ambitious companies listening to this will decide to follow suit, which is you decided to build an agent per customer.

Yes. Every day between 100 and 200,000 agents get instantiated specifically for this customer with its own virtual machine. 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. If you haven't faced fear before, if 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 like AI engineers to mechanics, we train everyone and after 6 weeks, they launch state-of-the-art agents to production.

What advice do you have to future founders or first-time founders that might be listening? What works right now is

welcome back to the ACC podcast. Uh today we have Ali Masa the head of AI at Kabak. We're going to discuss today the transformation that Ali led within Kabak to turn it into an AI native company. Thank you Ali for being with us today.

Thanks for having me.

Before starting at Kabak uh you were running a company called Oppy Analytics.

That's right.

And you were very much into AI before Chad GBT. You want to tell us a little bit about that journey?

Yes. Yes, of course. Well, we called it machine learning back then. It was a different family of of algorithms and and we founded a company with this very like like ambitious vision there that 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. uh but we built a great company.

We served like Fortune 500 companies uh around like risk algorithms, logistics, forecasting, marketing and but like really the the the power of what Transformers and then like the Chady moment uh when it arrived make things like very clearly that that we could now build a whole new uh company and and and way of of of building companies and we joined Kavak to and Carlos to build that.

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?

Kavakh started out as a use case 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 Carfax and like basically all the infrastructure for this to work didn't exist in in Latan. So we had to build everything vertically so we could serve our customers the right way.

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? Like what's the harness look like? Like ground us in how you design this.

Right. So, so, so we bet the company in transforming to a company run by agents. The questions we ask ourselves is how would we build Kabak in 2035 with fable 10 or or or GPT 10 level intelligence and actually that company looks very different than than what we had built or what we had back then. So when a customer comes in right now um agent will get spawned specifically for this customer with its own virtual machine.

It'll remember years of interaction of this customers with with Kavak what they visited in the web page or a call they had two years ago. remember everything like 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 like all our different products and like across time and this is a completely new and groundbreaking architecture at scale.

Uh I think because like people are still building multi- aent system with with experts and and we realized to bet that longunning agents with hard goals not just workflows uh could could maximize our our customers uh satisfaction and obviously their their lifetime value.

Awesome. Okay. So we're going to jump to the nuances that but maybe versus many companies that say hey we want to be a gentic and they try some workflows you guys took the just rip like we had to make this work you had to downsize dramatically it didn't work for a year

right

so do you want to talk through obviously you had to

tune a lot of things to make that work like describe the harness at that time and like what models you were using and sort of specifically yeah

so so there there were like three main decisions that that we had to make. The first and this is where I think many companies are stuck right now is the first instinct is okay let's adopt AI and you you basically leave your structure as it is and just give chat GP to your cloud to to your team and then there's no efficiencies your customers have the same problems and and nothing happens right and so so you need to redesign your whole company around the agents and around the future capabilities and this means really like rebuilding most of your APIs, rebuilding your system so the agents can use them to to perform.

Then you need to start generating the data and the feedback loops to fine-tune these agents. The only way to really make them work is if you teach them. And how do you teach them? You you put them out in the open. You you put them in front of customers. You get that data. You get those evals and then you train your your your agents. And this is the second bet that we made that that we could build superhuman agents.

This means that by every dimension that matters like conversion, lifetime value, uh customer experience, our agents would outperform the best human we had we had ever hired and we put them in front of the hardest problems.

Um and finally you you start to change how you measure the success of the company. Kavak was a transactional company. We used to measure how many cars we bought, how many cars we sold, how many brakes we we needed to to brake pads we needed to buy. And we moved to a relational company where now I have 10 million customers in my database and I have agents assigned to most of them with the task of maximizing their lifetime value.

Now, we're selling cars and and and personal loans and very high ticket items. So just activating 1% of this customer base, it's like hundreds of millions of of dollars uh if we do it the right way. So so it's a bet that made sense for us because of our industry, because of the ticket, and because at the end of the day, customers need to build trust with with a company because they're buying a used car. And the way to build trust is to to know them and to and to plan and and nurture a long-term relationship.

Ali, I just wanted to double click on something. You know, evos over agent demos. Yeah.

Um you probably get pitched a lot of new agents and you know, it's never been easier to build things like before. But um one of the questions is like how do you how do you guys go about evaluating this? Because not everybody test them across 90% of the customer interactions to see if they're really working. And you know you guys I believe is it about 98% of the interactions or something yes like that are now handled by agents.

Yes. Totally. So so to give you a sense of the scale 90 like 96% of all interactions uh are handled by agents. So so no humans there. Um like 95% of all transactions are completely handled by by agents. Obviously, you meet a human when you pick up your car, like there's someone physically there to give you the keys. But the rest of the of the of the experience of the journey is handled by by an agent. Every

原文超出正文长度上限，此处截断——上游还有内容，完整版见上方「原文 ↗」。