Turn generic AI into specialist personas using simple markdown

Many developer teams and product owners building verticalized assistants struggle to turn generic large language models into reliable domain experts; current approaches rely on brittle prompt engineering, ad‑hoc scripts, and expensive engineering time. Across an addressable market of roughly 20 million developers and AI teams spending an average of $3,000 per year (≈$60.0B TAM), these pain points manifest as slow onboarding, inconsistent outputs, and high maintenance costs. You could build a markdown‑first persona format and toolchain that converts human‑readable persona docs into versioned prompt layers, tool‑call declarations, and runtime configs compatible with multiple LLMs and agent frameworks. Deliver an open‑core CLI/SDK, a CI/CD friendly test harness, and a lightweight registry so teams can treat personas as code—diff, review, test, and roll back changes like any other artifact. Offer a minimal web UI and enterprise features (RBAC, audit logs) to ease adoption without heavy integration work. This market looks attractive now because composable AI, prompt‑as‑code practices, and demand for verticalized assistants mean teams are already primed to manage persona artifacts alongside source; the opportunity scores highly (Market Score 90/100, Revenue Potential 85/100) and competition is medium. To stand out you must prioritize simplicity and interoperability—a human‑readable standard, multi‑model compatibility, and tooling that slots into existing CI/CD pipelines—while being honest about challenges: API fragmentation across models, competing standards, and a multi‑quarter enterprise sales cycle. If you can demonstrate clear developer time savings and more predictable agent behavior, this approach can capture incremental tooling budgets, but execution on integration, testing, and governance will determine success.