Kavak

“The way it looks like now is very flat teams, very senior teams, super empowered. If you look at a team, you'll have engineering, AI, like operations, like everything. And they're either building the agents, working for the agents, or being in the physical wor…”

“And then if you look at Kavak now, any process, it's really a collaboration of, Agents and humans, and sometimes like agents are the bosses or of humans, and sometimes humans are designing the agents.”

“We tripled NPS and customer satisfaction score by putting the agent in front of the customer.”

“Level tier three tokens, the most valuable. Are this agents where you can get the ROI of each specific token? And I can do that now. That's great news for me because I'm growing. And because I know the ROI of each token, because it goes to agents that are perf…”

“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 webpage or like Call they had two years ago.”

“So we train everyone, and after six weeks, they launch state-of-the-art agents, AA agents to production. And it's mechanics and finance guys and engineers, like, everyone can do it.”

“Warranties came down around like 20, 26% since we launched and customer satisfaction again went up.”

“The intelligence now doesn't need like the graph and the multi-agent lattice work and harness because it will constrain this level of intelligence.”

“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 customer experience, our agents would outperform the best human we had ever hired.”

“And it, at first it converted, like, 50% more than our human team, and now it's converting over that, like, 2.1 x more.”

“And we moved to a relational company where now I have ten million customers in my database and I have agents assigned to most of them with the task of maximizing their lifetime value.”

“The first is it has to be top down because of this. Like, if you just get adoption, it won't go anywhere because it's hard to generate this taste or strategy for people to bottom up, decide what to build and whatnot, and come up with something that works for t…”

“And usually in, in Mexico and in some emerging markets, it'll get like two months or more to get a car loan approved. We usually approve it in under three minutes which is like pretty cool because we have all this data around the customer and the car.”

“Every day between a 102 100,000 agents get instantiated in a day. They wake up, they work sometimes for three minutes, sometimes for eight hours, sometimes for three days, and they like set an alarm clock for their next task and to go back to sleep.”

“So we decided to like destroy everything we had been building for two years that was working, that brought us to profitability, that brought us amazing growth. And start over with a harness that we thought would be robust and scalable and leverage recursive se…”

“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 LATAM, so we had to build everything vertically so we could serve our customers the right way.”

“The goal of the first month was to double the profits of Cuernavaca. It didn't reach it, but it was 1.5 X, like 50% more profits just by managing the city, which is, it's crazy, right?”

“90, like 96% of all interactions are handled by agents. So, so no humans there. Like 95% of all transactions are completely handled by agents.”

“We spend about the same amount of time, engineer time, tokens, and money on building the evals than building the agents.”