Confusing API docs -> AI-generated 10-minute interactive onboarding

APIs with terse, inconsistent docs slow adoption and create friction for platform engineering, developer advocates, and third‑party integrators who often spend days debugging or building bespoke SDKs instead of shipping features. Across an estimated 2.0M engineering teams globally, developer-tools and developer-experience spending totals about $44.0B annually, so this is both a widespread operational pain and a commercial opportunity for any product exposing a programmable surface. A practical product would generate a context‑aware, LLM‑assisted 10‑minute interactive onboarding per API: auto‑extracted quickstarts, runnable SDK snippets, guided playthroughs with live request/response sandboxes, and telemetry‑driven step suggestions tailored to the user’s errors. Positioning as a self‑serve PLG add‑on with a target ACV of roughly $22k aligns with current buying behavior and the industry focus on first‑call success metrics. Macro tailwinds—API‑first adoption, rapidly improving code‑capable LLMs, and a priority on measurable developer time‑to‑value—plus a market score of 92/100 and revenue potential of 88/100 make this an attractive near‑term opportunity. To stand out you need engineering rigor: retrieval‑augmented generation against canonical API schemas and corpora, automated validation of generated snippets (unit/integration tests), and coupling onboarding to runtime telemetry so flows adapt to real failure modes. The strengths are tangible ROI levers—fewer support tickets, higher activation, and clear expansion paths—but be honest about the work: LLM hallucinations, ongoing maintenance as APIs change, and a medium‑competitive landscape where SDK/docs vendors already exist. A pragmatic path is PLG for public APIs to prove activation lift, then add enterprise controls (SAML, private corpora, audits) for higher ACV; those milestones give measurable KPIs to decide whether to scale investment.