The Official SaaStr Podcast: SaaS | Founders | Investors
00:08:49 · Jeanne DeWitt Grosser
DealOne ingests every call, generates notes with action items, and posts coaching suggestions directly to internal Slack channels. It proposes CRM field updates. It tracks competitive mentions and objections over time. And it runs postmortems on every closed lost deal. And all of this happens where we already work. For us at Vercel, that's generally in Slack. When a rep goes into a channel and mentions at DealOne with a question, the agent does a Slack real time search, queries D0, another agent of ours for data, as a sub agent, pulls Gong transcripts, searches our internal knowledge base, and streams in answers back into the Slack channel. The rep never leaves Slack, and the agent never has a UI. For DealOne to work, Gong needed an accessible API. Salesforce needed webhooks we could actually compose into automated workflows. Because those external products had developer surfaces, everything worked. If they didn't, we would have gotten rid of the tool. Next, there's the Playbook Platform. We kept seeing a pattern in our best reps. They knew to reach out when a customer's usage spiked or when a high value prospect ended up on our pricing page. They did this instinctively, but it didn't scale. It depended on which rep owned the account and whether they followed through on their signals in their dashboard. The Playbook Platform automates all of that. A signal fires like a usage spike or a high intent pricing page visit, and the platform matches it to a play, generates personalized outreach, and surfaces it to the rep for a single click review. The best reps plays available to everyone are automatically triggered. The before, we should reach when you should spiked, but it never happened consistently. The after, an 11 out of 10 experience on every account, every time. Notice, no separate UI to log into. Signals flow in. Drafted Outreach flows out. Reps review in their existing workflow. We built it all headless by design. The companies that win in the agentic era will be the ones whose products have headless, composable architecture. You've seen this signal coming from the best enterprise SaaS companies out there. Box, Notion, Salesforce. They're all making major bets on their developer platforms right now. So the to do here is simple. Even if the work isn't, go build the developer surface areas now, MCP servers, webhooks, APIs. Next, data.
00:11:48 · Jeanne DeWitt Grosser
Probably the most popular agent at Vercel used by the entire company is D0. D0 is a data analyst agent that everyone at Vercel can access via Slack. Questions that used to take a week to ticket and get an answer back to from the data science team can now be answered in less than a minute. It'll answer things like, what was our token volume through AI gateway last week? What companies do we have logo rights for in The UK? Who are our highest revenue startups... Startup customers using sandboxes? D0 powerfully democratizes data across the company. To do this, D0 translates natural language questions into SQL queries against our analytics infrastructure, letting anyone on the team answer questions without writing code or waiting for the data tea. To power D0, we invested heavily in what we call the knowledge phase. It's a structured, queryable representation of our business that all our agents can reason over. Our head of data science built a semantic layer on top of the model to use our... On top of a model of our revenue, breaking down the business into the smallest causal units. Enriched with third party and first party signals, that layer is what powers D0. It's what makes DealOne's analysis actionable rather than just interesting. And it's what makes the Playbook Platform contact the right companies with the right message. Every agent runs on top of it. Without it, agents hallucinate or give generic answers. With it, agents are grounded in our data. Finally, agents change the build by calculus. With the rise of SaaS, enterprise software was a procurement exercise. But now robust applications and agents can be built and run at scale in a matter of weeks. Companies are building for their own unique workflows. To give you some perspective, the lead qualification agent that I opened with costs us under $5,000 a year between infrastructure and tokens to run. It takes about 20% of one engineer to maintain it. That's a 32 x ROI. We used to have 10 people in their salaries. It's now $5,000, and it operates twenty four seven with faster speed to lead and all the quality of human equivalent metrics. That said, building isn't automatically the answer. So every quarter, I ask the same question of my GTM engineering team. Has another company out there built something better, faster, or cheaper? And if the answer is yes, our agent's gonna go.
00:14:47 · Jeanne DeWitt Grosser
So how did we actually do all of this? We had a GTM engineer, as I mentioned, shadow one of our best SDRs for a few days, shoulder to shoulder looking at their screen together, watching every single tab she opened, LinkedIn, built with, the company website, CRM, Slack history. The engineer converted each of those data sources into a step in a tool calling workflow. We documented it, made the workflow legible before touching any AI. After building our first version, the agent ran in production in shadow mode for six weeks. Our best SDR reviewed every single output to create a feedback flywheel. And then finally, she couldn't improve it anymore. The architecture reflects exactly what she was doing manually, but now it performs like the ninetieth percentile rep 100% of the time. We applied the same framework to 30 different SDR workflows from event follow-up to PQA flows to specific time based campaigns. SDR quotas increased by 30% this quarter. And, again, the lead agent was not hard to build. A single engineer prototyped this over the weekend, and it was in production six weeks later. Then there's Vertex. This is our customer service agent, like our internal Sierra, that powers vercel.com/help. Vertex costs us $300 a month in infrastructure plus 12,000 in tokens. It's 150 k a year. It operates at significant scale handling thousands of technical cases a week. And three engineers work on it. Three. We started with an off the shelf solution, but weren't seeing enough results. We built something house in two months and had it in production. Compare that with some of the agentic support companies out there with a 150 engineers running the equivalent workflows at dramatically higher costs. The difference isn't magic. It's that we used infrastructure built for agents from the start rather than discovering the cost problem after we'd already committed to an architecture. All of our agents, lead agent, the Playbook Platform, DealOne, D0 run on Fluid Compute. The infrastructure that powers our internal agents is the same infrastructure that our customers deploy their own agents and applications to. Most rules of cloud infrastructure are broken by agents. Fluid runs high performance mini servers and compute only triggers when needed. Plus, it reuses existing resources before spinning up new ones. So early adopters cut compute costs by up to 85%.
00:17:46 · Jeanne DeWitt Grosser
When you hear us saying that we purpose built infrastructure for this next generation of the web, Fluid is a big part of that. Everyone wants to talk about what agents can do. Fewer people want to talk about how complex it gets when they're running at scale. If your agentic prod... Product succeeds, usage goes up. If usage goes up, that profile matters. Infrastructure choices matter a lot more than many teams realize at the beginning. Go to market is getting closer to consulting than sales. The executives I'm talking to right now aren't asking me to pitch them on Vercel. They're asking me to help them figure out where to start with agentic AI, what best practices look like, and what architecture should be, what breaks at scale. Perspection scale is what reveals whether architecture is actually durable. That's one of the reasons we think there is a significant difference between building an agent demo and building something in production at scale. So to recap, one, build your developer surface area now. APIs, MCP, p servers, webhooks. The things that let your product to be composed into an agentic workflow without a human in the loop. The companies that win in the agentic era will be the ones whose products are in the stack. The ones whose composable API layer won't... The ones who aren't won't have the composable API layer to be called. Two, invest in your data foundation, the knowledge base, the clean data warehouse, the semantic layer. None of it is exciting to build, but all of it is load bearing. The agents you build will only be as good as the data underneath them. And as we learn, that data in front of smart people that make the... Will make the data better itself, not just the agent that uses it. And three, the average builder on your team today should be able to out ship any vendor who's trying to sell you the same outcome. And if they can't, you can start experimenting today with a few hours carved out of the week. That's the new build versus buy. I truly believe that the companies that treat their go to market as a product, that document it, design it, iterate, and then automate it piece by piece are the ones that will end up on the right side of the ships to agents. That's what we're building at Vercel, and we hope you build the same too.
00:20:40 · Sam Blond
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