Riff (Growth Triage Tool)

The problem

Growth teams at developer-tools companies drown in ideas. They arrive constantly and from everywhere — a founder’s tweet, a Discord message, an AI-news headline, a “we should blog about this.” They pile up and get lost, and the few that survive still need someone to answer the same questions every time: Is this worth doing? For which audience? How would we even measure it? — and then actually write the first draft.

That triage-and-first-draft work is the real bottleneck between “good idea” and “shipped.” Most tools attack the wrong end of it: they give you another board to maintain, not the work itself.

The core design decision

The output is a brief, not a database row. Riff doesn’t try to be a project manager or a CMS. It produces the one artifact a marketer actually needs to act on: a structured brief that says what this is, who it’s for, how you’ll know it worked, the plan to ship it, and a first draft — rendered right in the app, with one-tap Copy brief (as markdown) so it can live wherever the team already works.

Metrics conditional on marketing type

A brief is only useful if it answers “how would we know this is working?” The AI first classifies each idea into a marketing type, then selects the metrics that apply:

Every North-Star metric ladders to the same real goal: activated, paying users, while vanity metrics (impressions, follower counts) are tagged as leading indicators, never goals.

AI-native architecture

• Structured outputs for the classification. A raw idea → a strict JSON schema (category, funnel stage, effort, marketing type, the three metrics, a goal, a plan) using the model’s structured-output mode — so a live demo can’t produce a malformed classification on stage.
• The AI selects from a taxonomy; it doesn’t invent one. The metrics framework — which leading indicator and North-Star metric belong to which marketing type — lives in plain, unit-friendly TypeScript. The model chooses from that catalog and tailors the specifics to the idea. Judgment where it’s needed; deterministic structure where it isn’t.
• Right-sized model calls. One call classifies (fast, low reasoning effort); a second drafts the actual asset (more effort). Nothing more.
• House-style enforced on every draft. A voice layer (developer audience, concrete over hype, no claims beyond the source, a banned-hype-word list) is injected into every drafting prompt, with two audience registers (accessible/dev-first vs deep/technical). Designed so a memory layer could later learn each writer’s real voice.

Engineering notes

• Shape: Next.js 16 (App Router; middleware is now proxy.ts) + React 19 — one page, one API route (/api/submit does classify → draft server-side).
• Security: the Anthropic API key never leaves the server; the browser only ever calls /api/submit. A password gate (proxy.ts) fronts the whole app.
• No database by design: each brief is a live session artifact, and the Copy-brief export means the output lives wherever the user wants — not locked inside the tool.
• Ships continuously: push to main → Vercel auto-deploys.

What’s next

A memory layer so drafts learn each writer’s voice over time (the “learns your team’s voice” the app already gestures at), and an optional one-click push of a finished brief into whatever tracker a team already uses — kept deliberately as an export, never a lock-in.