# Vercel replaces analytics ticket queues with an internal agent

Markdown copy of https://realalpha.app/s/vercel-replaces-analytics-ticket-queues-with-an-internal/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Guillermo Rauch](https://realalpha.app/builders/guillermo-rauch/) · Guillermo Rauch is founder and CEO of Vercel. · Founded Vercel, which created Next.js and the v0 AI tool for building websites.
- Host: [Nakul Mandan](https://realalpha.app/people/nakul-mandan/) · Host, Knuckle Up with Nakul · General partner at Audacious Ventures; his show goes deep with founder CEOs on how they build.
- Show: Knuckle Up with Nakul
- Episode: [Inside Vercel's AI-era playbook: "Recursive Founder Mode" | Guillermo Rauch (CEO)](https://realalpha.app/v/DdbCtAScTsk/), Aug 25, 2026
- Moment: 51:32 to 54:47 of the episode (3 min), playing on https://realalpha.app/s/vercel-replaces-analytics-ticket-queues-with-an-internal/; the episode from there: https://realalpha.app/v/DdbCtAScTsk/?t=3092

## Quote

> “Nowadays, the SLA is something that we measure in our observability platform. It's how fast is **time to first token**.”
> — [Guillermo Rauch](https://realalpha.app/builders/guillermo-rauch/) at [53:44](https://realalpha.app/v/DdbCtAScTsk/?t=3224) · Knuckle Up with Nakul

## Takeaways

1. Employees ask V instead of filing data-analysis tickets.
2. The data team builds the agent and its access controls.
3. The analytics SLA is measured by time to first token.

## About this segment

Vercel routes company questions to its internal agent instead of analyst-ticket queues. The data team builds the agent and access controls, while measuring response time by first token.

From “Inside Vercel's AI-era playbook: "Recursive Founder Mode" | Guillermo Rauch (CEO)”, Aug 25, 2026. [Watch the full episode](https://realalpha.app/v/DdbCtAScTsk/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[51:39](https://realalpha.app/v/DdbCtAScTsk/?t=3099) But the way you're describing, why do you even need execs anymore, right? Because the way you're describing is you go for research of any kind, even internally, first to V. It gives you full visibility into what's happening. You only go to a human when there's a very specific thing that there's a problem that needs to be solved. do you need execs or super ICs then outside? Or maybe they're approaching the same thing, right? And again, it goes back to like why we're so horizontal and transparent. You know, our COO came into with this incredible perspective when she joined that she was going to create the most technically competent and most scalable go-to-market organization on the planet.

[52:17](https://realalpha.app/v/DdbCtAScTsk/?t=3137) And what she did is two things. One, create the agents that have sales skills. So lead routing, and going back to what informed eventually the creation of the EVE framework and V was that she built like a dozen go-to-market agents. I don't even know, I personally don't even know them because they're in the weeds of the operational rituals of my go-to-market team. But if I had a question about go-to-market, where would I go?

[52:45](https://realalpha.app/v/DdbCtAScTsk/?t=3165) It's like, I have to learn about like these 12 agents. And so I go to V. So she started sort of rethinking go-to-market from an agent-first perspective. Our data organization did the same thing. Data is actually maybe one of my proudest agentic accomplishments because we're able to bend the headcount curve. We created the organization that just works on the agent. They work on the agent that can answer data analytics.

[53:14](https://realalpha.app/v/DdbCtAScTsk/?t=3194) They work crucially in the security model because we need to have policy for who can access what information. But that has to be transferred into the brain of the agent. And so what they're working on, going back to the factory, they're working on the machinery for data science at scale. And it's fascinating because my expectation a few years ago was, I told Abby, our VP of data, I want every person in the company to be able to file a ticket and within an SLA get an answer from an analyst or data scientist.

[53:51](https://realalpha.app/v/DdbCtAScTsk/?t=3231) Nowadays, the SLA is something that we measure in our observability platform. It's how fast is time to first token. And instead of going into a ticket queue, it just goes to V. So you ask, for example, our CTO asked the other day, can you give me ideas for banger tweets? And V said, we recently reached 2 billion deployments. We should tweet it. I mean, if that's not an AGI moment, because it had to piggyback on the data agent and it had to come up with hypothesis.

[54:24](https://realalpha.app/v/DdbCtAScTsk/?t=3264) What could be interesting to tweet about? Could be progress in the business. It could be something that is not obvious. And it came up with this observation about a milestone. And so this is kind of how we run the company now. Those executives are agent builders. If you were to rebuild Vercel today, let's say you were starting Vercel today,