I read the GTM Engineer job description. This is where I think the issue is. This is how I have solved it before. And this is how I would solve it for you - built the way the GTM Engineering team would build it: Next.js, the AI SDK, and v0, on the Vercel AI Cloud.
A top performer's best practice lives in their head and their sent folder. By the time it reaches the org, the deal it would have saved is gone.
At Corgi I ran product operations and built the internal systems the team worked from. Before that I founded a company and ran its operations end to end.
Route every GTM signal to the right motion with an AI SDK agent loop, turn observed top-performer behavior into a shipped, auditable play, and keep the reasoning attached to every decision.
Sample data throughout. Independent concept - not affiliated with or endorsed by Vercel.
Signals come in from Sales, Success, Marketing and Data. Pick one, route it, and watch an anecdote become a system with its reasoning attached.
The same system, built on the Vercel AI Cloud - the stack this role ships with every day.
The router UI and intake API routes, server components for the signal feed, with a preview deployment per iteration so Sales, Success, Marketing and Data review every change on a real link.
Each signal is classified to a motion by an AI SDK agent with structured output - motion, reason, confidence - so every routing decision is typed, logged and replayable.
Play drafts render as generated UI: the four-step system card and the outreach draft are composed from the observed-behavior template and reviewed before anything ships.
The audit trail runs on Vercel Observability across the agent loop; play performance - signal to meeting, signal to order - is tracked in Web Analytics, so the org sees which shipped plays actually drive revenue.
Every classification, every drafted action, every shipped play - on record.