customer_support · saas · workflow

Vercel's internal data agent d0 democratizes analytics access across the company

Running AI agents in production requires expertise in security, observability, reliability, and cost optimization that is rare even as building becomes easier, creating a growing shadow IT problem. Vercel's own data access was previously limited to professional analysts, leaving engineers, marketers, and executives unable to self-serve answers from the data warehouse.

How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User asks question in Slack
A user asks a natural-language question in Slack, which kicks off the d0 agent workflow.
Tools used
SandboxesFluid computeAI GatewayVercel WorkflowsAI SDKSlack · partnerSnowflakeClaude OpusPython
Outcome

One person built d0 in a few weeks using 20% of their time, where the same work would have normally taken months of engineering effort. d0 now gives engineers, marketers, and executives self-serve natural-language access to the data warehouse. Vercel's customer support agent handles 87% of initial questions autonomously, and a lead qualification agent lets one SDR do the work of 10.

Results
Volume87%
Source

https://vercel.com/blog/anyone-can-build-agents-but-it-takes-a-platform-to-run-them

How we source this →

Grounding & classification
Source type: platform led case
34 fields verified against source quotes, 2 dropped as unverifiable.
agentic workflowai agentcontent generationenterprise searchsupport agentknowledge basesupport ticketmetric backedproduction runtime claimedtools describedworkflow describedsoftwareautomation ratedeflection rateemployee productivitytime savedplatform led caseback office opscustomer supportlead processingsales opsagentic task executionautonomous resolution