Start as a managed service that audits a team’s high-value LLM^? workflows, rewrites prompts, adds evaluation cases, creates a versioned prompt library, and trains users on approved patterns. The first package can deliver 5 to 10 production-ready workflows with acceptance tests, usage notes, failure modes, and a maintenance cadence. Over time, the repeatable parts can become templates, evaluation harnesses, governance checklists, and lightweight internal tooling.

Enterprises are moving from AI experimentation to production use, and job ads now mention prompt libraries, agent workflows, evals, feedback loops, safety, and workflow-specific training. The gap is operational discipline, not basic AI awareness.