Six weeks. One engineer. Ten SDRs reduced to one. A million dollars in salaries replaced by a $1,000-per-year agent.
That's the headline from Vercel's AI-powered transformation of their inbound sales function. But the numbers only tell part of the story. The more compelling insight is how they built an AI agent that matches human performance—and the development process that other organizations can replicate.
Drew Bredvick, Vercel's Director of GTM Engineering, recently shared the detailed playbook in a webinar on building AI agents for go-to-market teams. What emerged is a masterclass in agent development: the shadow testing methodology, the counterintuitive prompt engineering discovery, and the technical architecture that makes enterprise-grade agent deployment achievable.
"For us, we had 10 SDRs doing this inbound workflow, and now we just have one that is effectively QAing the agent. The other nine, we deployed on outbound."
The results speak for themselves: 100% efficiency improvement (8 touchpoints per lead reduced to 4), conversion rates that match their best human performers, and a deployment timeline that challenged every assumption about enterprise AI implementation timelines.
Why Go-to-Market Matters More Than Ever
Before diving into Vercel's solution, it's worth understanding why sales transformation has become strategically urgent.
"With AI, it's just intensified because you have 10 players pursuing the same market opportunity," Grosser observed on the podcast. "Your ability to actually bring the product to market, to differentiate yourself from the competition has become more strategically important than it was previously."
When products converge at the margin, the buying experience becomes the differentiator. And here's the uncomfortable truth about enterprise buying: 80% of customers buy to avoid pain or reduce risk—not to capture upside. Enterprises aren't buying your vision of the future. They're avoiding the risk of missing their revenue target next quarter.
This has profound implications for how you structure go-to-market. Speed, precision, and consistency matter more than ever. Which is exactly where AI agents excel.
The Inbound Sales Challenge
Inbound lead qualification is an ideal starting point for AI automation—and Vercel's results prove why.
Response time determines conversion. Leads go cold. A sales representative—human or AI—that can respond and analyze a lead at any point during the day captures customers at the point of maximum interest. As Jeanne Grosser, Vercel's COO, noted on the Lenny's Podcast: "It's actually condensed the number of touches it takes to convert because it's so much quicker at responding to leads inevitably sitting in the queue or coming in at nighttime and no one can get to it."
SDR work is pattern-based. Lead qualification follows recognizable patterns: company size, industry, use case signals, engagement history. The best SDRs develop sophisticated mental models for scoring leads—but those models can be encoded and scaled.
Coverage gaps are expensive. Night and weekend leads represent lost opportunity. Every hour of delayed response decreases the probability of conversion.
These characteristics—pattern-based decisions, time-sensitivity, and 24/7 requirements—make inbound qualification an ideal agent candidate. But identifying the opportunity is the easy part. Execution is where most organizations fail.
The Development Approach: Shadow Mode First
Vercel's development process began not with building, but with validation.
Back-testing against historical data. Before writing a single line of production code, Bredvick's team tested their agent against three months of historical lead data. The benchmark: could the AI reach the same qualification decisions as human SDRs?
"We tweaked prompts until AI agreed with human decisions," Bredvick explained. This iterative refinement process revealed edge cases, exposed gaps in the agent's reasoning, and built confidence before any real leads were touched.
Shadow mode deployment. Even after back-testing showed promise, the agent ran in shadow mode before production deployment. It processed every incoming lead alongside human SDRs, but without taking action. The team compared outputs, identified disagreements, and refined the system.
This shadow testing methodology serves multiple purposes:
- Validates accuracy before real-world consequences
- Builds stakeholder confidence through demonstrated performance
- Identifies edge cases that back-testing might miss
- Creates training data for continued improvement
The timeline reality. Despite this methodical approach, Vercel reached production deployment in six weeks. "It wasn't like this was a multi-quarter process," Grosser noted. "It actually moved super quickly."
The Reasoning Structure Discovery
Perhaps the most valuable insight from Bredvick's process is counterintuitive: where you place the decision in your prompt affects accuracy.
"AI reasoning structure matters," he explained. "Put decision/TLDR at bottom, not top of output. Forces 'thinking fast and slow'—avoids snap judgments."
When prompts ask for the decision first, models tend to jump to conclusions. When prompts require reasoning before the decision, models engage in more thorough analysis. This mirrors Daniel Kahneman's System 1 vs. System 2 thinking: fast, intuitive judgments versus slow, deliberate reasoning.
For agent builders, the implication is practical: structure your prompts to require reasoning chains before conclusions. The additional tokens are worth the accuracy improvement.
Shadowing Top Performers
Another critical development insight: shadow your best performers to capture what they actually do, not what they say they do.
"Best SDRs have tricks they've tried sharing but team stuck in old ways," Bredvick observed. "Discovered LinkedIn enrichment necessity through shadowing."
The gap between stated and revealed preferences is well-documented in behavioral research. Top performers often can't articulate their decision-making processes—the expertise has become automatic. Direct observation captures these hidden heuristics.
For Vercel, this meant watching their best SDRs work, noting which data sources they consulted, which signals they prioritized, and which shortcuts they'd developed. These observations became agent capabilities.
Technical Architecture
Vercel's lead agent architecture demonstrates how modern AI infrastructure enables rapid development.
Tech stack:
- Next.js for the application framework
- Vercel AI SDK for model integration
- Workflow Dev Kit for process orchestration
- AI Gateway for model management
The AI Gateway advantage. Vercel's AI Gateway enables load balancing across 10-12 different models. This provides redundancy ("fallback logic prevents single provider downtime issues") and the flexibility to route different tasks to optimal models.
Agent workflow:
- User submits form → workflow initiates
- Lead enriched via Clearbit integration
- Research agent investigates lead using available tools
- Qualification agent scores based on defined criteria
- Email generated for qualified leads
- Slack notification to remaining SDRs for approval
The human-in-the-loop step is intentional. Rather than fully autonomous operation, the agent handles research, scoring, and draft communication—then routes to humans for final approval. This maintains quality control while capturing the efficiency gains.
Knowledge base architecture. The agent draws on a TurboPuffer vector database containing account-level collections with all sales calls, emails, and Slack communications. Bredvick noted the cost advantage: "$64/month vs much higher" compared to alternatives like Pinecone.
The agent automatically generates search queries against this knowledge base, retrieving relevant context for each lead qualification decision.
The Data Quality Imperative
One theme recurred throughout Bredvick's presentation: data quality determines agent quality.
"Agents only as good as data you give them," he stated directly. "Requires amazing data team foundation."
This has implications for organizations considering similar transformations:
- CRM hygiene matters. If your lead data is incomplete or inconsistent, agent performance will suffer.
- Enrichment is essential. Clearbit integration wasn't optional—it was required for accurate qualification.
- Historical data enables testing. Organizations without clean historical records can't validate agent performance before deployment.
The counterpoint to "build fast" is "build on solid foundations." Vercel's six-week timeline was achievable because their data infrastructure was already mature.
Prompt Engineering as Software Engineering
Bredvick described a development process that treats prompts as production code:
- Version control all prompts in Git. Every change tracked, reversible, auditable.
- Use AI to refine prompts when they become unwieldy. Meta-prompting to improve prompt quality.
- Focus on edge cases where AI disagrees with humans. The disagreements reveal the gaps in your prompt engineering.
This discipline matters because prompts are the primary interface between business logic and model behavior. Treating them with the same rigor as application code ensures maintainability and enables systematic improvement.
Results and Business Impact
The quantitative results validate the approach:
- 100% efficiency improvement: 8 touchpoints per lead reduced to 4 touchpoints
- Human-level accuracy: The agent converts leads at the same rate as the best human SDRs
- 9 SDRs redeployed: From inbound qualification to higher-value outbound prospecting
- 24/7 coverage: No more leads sitting in queues overnight
- $1,000 annual cost: The entire lead agent runs for roughly $1,000 per year—versus over a million dollars in SDR salaries
But the qualitative implications may be more significant. As Grosser framed it: "I think we're getting to a point where with layering in agents, ideally, we finally get salespeople to a point where they're actually spending 70% of their time interacting with humans."
The current reality for most sales teams: 30% of time on customer-facing activities, 70% on administration, research, and qualification. AI agents flip this ratio. More human contact means deeper relationships, better customer understanding, and ultimately, better outcomes.
Beyond Lead Qualification: The Deal Bot
The lead agent was just the beginning. Vercel has since expanded their agent approach to other go-to-market functions—most notably, deal analysis.
Grosser described building a "deal bot" that runs against their Gong transcripts. The initial use case: lost opportunity reviews. After Q2, the team ran the agent against their top losses for the quarter, sorted by deal size.
The results were revelatory. "The biggest loss that quarter, according to the account executive, was lost on price," Grosser explained. "And when you ran the agent over every Slack interaction, every email, every Gong call, it said, actually, you lost because you never really got in touch with the economic buyer."
The agent identified that when the AE discussed ROI and total cost of ownership, the prospect's reactions suggested they didn't buy the math. The real loss reason wasn't price—it was an inability to demonstrate value. That insight drove changes to how Vercel quantifies and communicates ROI.
The team now runs this analysis against all lost opportunities in real-time, feeding insights into Slack channels for each customer. The bot prompts AEs: "You're this far into the sales process and you haven't talked to an economic buyer. You should think about that." Or: "You just got off that call with an economic buyer. Didn't sound like it went that well. Here's some things to consider."
What's particularly notable: the lost bot took two days to build. The rapid iteration from concept to production—six weeks for lead qualification, two days for deal analysis—suggests the limiting factor isn't technical capability. It's identifying which workflows are ready for automation.
Framework for Enterprise Leaders
Based on Vercel's experience, organizations considering similar transformations should focus on five priorities:
1. Identify Pattern-Based, High-Volume Workflows
Not every sales function is ready for agent automation. Look for:
- Repetitive decision patterns
- High volume (enough to justify investment)
- Clear success criteria (verifiable outcomes)
- Time-sensitivity (response speed matters)
Inbound lead qualification checks every box. Account planning might not—the decisions are less structured, the patterns less clear.
2. Validate Before You Deploy
Vercel's shadow testing methodology should be standard practice:
- Back-test against historical decisions
- Run shadow mode parallel to human operations
- Compare outputs systematically
- Iterate on disagreements
The six-week timeline included this validation. Don't skip it to save time—you'll spend more time fixing production issues.
3. Invest in Data Infrastructure
Agent performance depends on data quality. Before building:
- Audit CRM data completeness and accuracy
- Implement enrichment tools (Clearbit, LinkedIn, etc.)
- Build or access knowledge bases with historical context
- Ensure data team capacity for ongoing maintenance
4. Keep Humans in the Loop—Strategically
Vercel's agent drafts communications and routes to humans for approval. This hybrid approach:
- Maintains quality control
- Builds trust through transparency
- Allows humans to catch edge cases
- Creates feedback loops for improvement
Full autonomy may be possible eventually. Start with human oversight.
5. Find the Right Builder
"The person who built the lead agent was a single GTM engineer," Bredvick noted. "He spent maybe 25, 30% of his time on this."
The build didn't require a large team—it required the right person. Vercel's GTM engineers came from an interesting background: they were sales engineers with computer science degrees—front-end developers who moved into technical sales roles. This hybrid profile combines deep sales process understanding with the technical capability to build production systems.
The ideal profile:
- Technical capability: Can build with modern AI tooling, write production code
- Sales domain understanding: Knows what good qualification looks like, understands the art and science of sales
- Integration skills: Can connect CRM, enrichment, and communication tools
One capable engineer working 25-30% on the project achieved production deployment in six weeks. And because they understand sales, they can identify when the agent's output doesn't match what a 20-year sales veteran would do—and iterate accordingly.
The Broader Implications
Vercel's transformation points to a larger shift in enterprise operations.
Sales teams are early, not unique. The pattern—identify repetitive knowledge work, validate against historical performance, deploy with human oversight—applies across functions. Customer success, support, recruiting, finance—any domain with pattern-based decisions is a candidate.
Speed compounds. Organizations that deploy agents first gain experience, build capabilities, and identify new opportunities. The six-week deployment timeline suggests how quickly competitive dynamics could shift.
The human premium increases. As agents handle routine work, human time becomes more valuable. The organizations that help their people spend that time on high-value activities—building relationships, solving complex problems, driving strategy—will outperform those that simply reduce headcount.
The Bottom Line
Vercel's SDR transformation demonstrates what's possible when AI agent development is approached with discipline: shadow testing, prompt engineering rigor, solid data foundations, and strategic human oversight.
The numbers are compelling: 10 SDRs to 1, six weeks to production, conversion rates maintained. But the methodology is transferable. Any organization with pattern-based workflows, historical data for validation, and the right technical resource can follow a similar path.
The future of sales—and knowledge work more broadly—isn't replacing humans. It's freeing them to do what humans do best: build relationships, exercise judgment, and create value that only human connection can deliver.
"The target that every sales leader articulated is now within reach: salespeople actually spending 70% of their time interacting with humans."
That's the promise of AI agents. And Vercel just proved it's achievable.
At OuterEdge, we help organizations implement AI agents for go-to-market, operations, and customer-facing functions. Our approach mirrors what Vercel demonstrated: validate before deploying, build on solid data foundations, and keep humans in the loop for quality assurance. If you're ready to transform high-volume workflows with AI agents, book a strategy call to discuss your implementation roadmap.