Vercel Took a 10-Person SDR Team Down to 1. The Whole Thing Costs $5,000 a Year. With Vercel’s COO Jeanne DeWitt Grosser.

Vercel’s COO Jeanne DeWitt Grosser ran go-to-market at Google and Stripe for roughly a decade each before joining Vercel. Six weeks into the job, in June 2025, she stood up a go-to-market engineering team with one mandate: bring agents to everything in GTM. That was before “GTM engineering” was a phrase anyone used.

Ten months later, the team has automated a real chunk of core company functions. Not a demo. Production, at scale, with the costs and the rough edges included.

The customer support agent now handles 93% of total case load. And Vercel’s support cases are not “reset my password.” They are deep, technical, infrastructure-level problems. The content agent did 96% of major content updates last quarter. And the lead qualification agent took a 10-person function down to roughly one and a quarter people.

The Lead Agent: 10 People to 1, for Under $5K a Year

Vercel launched a lead qualification agent in August 2025. It started as 20% of a single engineer’s time. With a human in the loop over six weeks, they moved the entire function from 10 people down to one person running it in the US, plus about 20% of a person covering all of Europe and all of APAC.

The agent runs about $5,000 a year between infrastructure and tokens. It takes 20% of one engineer to maintain. Jeanne’s math on that: a 32x ROI. You saved 10 salaries, replaced them with $5,000 of compute, and the thing runs 24/7 with faster speed-to-lead and human-equivalent quality.

When this came out publicly, plenty of people declared the end of the knowledge worker. Vercel’s read was different. They moved those 10 people into higher-value roles. The agent took the deterministic part of the job. The humans went up the stack.

That distinction matters for how you sell this internally. This is not “fire your team.” This is “stop having your best people do the part a workflow can do better 100% of the time.”

The Build Method Is a Tripod

Every internal agent at Vercel gets built the same way. Three people, shoulder to shoulder:

• A GTM engineer
• A data scientist
• The single best subject-matter expert for that exact function

They document the best practice for the function first, then encode it into workflows that become the agent. A human stays in the loop to QA every output. The agent does not autonomously execute. Over time, as the subject-matter expert runs out of feedback to give, you finally pull the human.

For the lead agent, this was literal. A GTM engineer shadowed Vercel’s best SDR for days, watching every single tab she opened. LinkedIn, BuiltWith, the company website, the CRM, Slack history. The engineer turned each of those into a step in a tool-calling workflow. They documented the whole thing and made it work as a deterministic workflow before any AI touched it.

Then the agent ran in shadow mode in production for six weeks. The best SDR reviewed every output and fed corrections back in. The flywheel ran until she could not improve it anymore. The architecture mirrors exactly what she did manually, except now it performs like a 90th-percentile rep 100% of the time.

A single engineer prototyped the first version over a weekend. It was in production six weeks later. They then ran the same framework across 30 different SDR workflows, from event follow-up to product-qualified-account flows to time-based campaigns. SDR quotas went up 30% that quarter.

The build was not hard. The discipline was. Document the human, then encode the human, then QA the agent until it beats the human, then remove the human.

The Three Things That Actually Matter

Jeanne pulled out three takeaways for anyone building agents, whether for your product or for your own internal teams.

1. Agents Need Headless, Composable Architecture

Agents do not live in UIs. They call APIs. They hit MCP servers. They consume webhooks. If your product is not composable and developer-accessible, you are invisible to agentic workflows. You are simply not in the stack.

This cuts both ways. Internally, Vercel’s “Deal One” meeting intelligence agent only works because Gong has an accessible API and Salesforce has webhooks you can actually compose into automated workflows. Jeanne’s line on the tools that did not have those surfaces: they would have ripped them out.

Deal One ingests every call, generates notes with action items, posts coaching suggestions into Slack, proposes CRM field updates, tracks competitive mentions and objections over time, and runs postmortems on every closed-lost deal. When a rep @-mentions it in a Slack channel, it searches Slack in real time, queries a second agent for data, pulls Gong transcripts, hits the internal knowledge base, and streams the answer back. The rep never leaves Slack. The agent never has a UI.

Same with the Playbook Platform. A signal fires, a usage spike or a high-intent visit to the pricing page, and the platform matches it to a play, generates personalized outreach, and surfaces it to the rep for a one-click review. The best reps’ instincts, made available to everyone, triggered automatically. No separate tool to log into. Signals flow in, drafted outreach flows out, reps review in the workflow they already use.

The to-do here is simple even if the work is not: go build the developer surface area now. MCP servers, webhooks, APIs. Box, Notion, and Salesforce are all making major bets on their developer platforms for exactly this reason. The companies whose products can be called by an agent will be in the stack. The ones that can’t, won’t.

2. Invest in Your Data Foundation

Good data equals good agents. None of the clean-warehouse, semantic-layer, knowledge-base work is fun to build, but all of it is load-bearing.

Vercel’s most popular internal agent is D0, a data analyst agent the entire company reaches through Slack. Questions that used to take a week to ticket through the data science team now get answered in under a minute. Things like: what was our token volume through AI Gateway last week? Which companies do we have logo rights for in the UK? Who are our highest-revenue startup customers using sandboxes?

D0 translates plain-English questions into SQL against the analytics infrastructure, so anyone can get an answer without writing code or waiting on the data team. To power it, Vercel built a structured, queryable knowledge base, a semantic layer sitting on top of a model of their revenue, broken into the smallest causal units and enriched with first-party and third-party signals.

That layer is what makes Deal One’s analysis actionable instead of just interesting, and it is what lets the Playbook Platform contact the right company with the right message. Every agent runs on top of it. Without it, agents hallucinate or give generic answers. With it, they are grounded in your actual business.

There is a second-order effect here too. Put that data in front of smart people and the data itself gets better, not just the agent using it.

3. The Build-vs-Buy Calculus Has Flipped

With the rise of B2B software, enterprise software was a procurement exercise. You bought because building was too slow and too expensive. That assumption is gone. Robust applications and agents can be built and run at scale in a matter of weeks.

The cost numbers make the point. The lead agent: under $5,000 a year, 32x ROI. Vertex, the in-house customer service agent that powers the help site, costs $300 a month in infrastructure plus about $12,000 in tokens. Call it $150,000 a year, with three engineers, handling thousands of technical cases a week. Vercel started with an off-the-shelf tool, did not see enough results, and built in-house in two months.

Compare that to some of the agentic-support companies running 150 engineers on equivalent workflows at dramatically higher cost. The difference is not magic. It is that Vercel used infrastructure built for agents from the start, instead of discovering the cost problem after committing to an architecture.

But building