StackRail Releases APRF Framework to Close AI Production Readiness Gaps

StackRail has published the AI Production Readiness Framework (APRF), a vendor-neutral, machine-readable methodology designed to help engineering teams safely deploy large language model features in production environments. The framework uses hard pass/fail gates rather than averaged maturity scores, meaning a single failed mandatory check blocks a release entirely. APRF covers eight domains and 27 pillars, with 40 core gates for customer-facing AI and 61 gates for regulated systems, plus additional checks for agents, RAG, voice, and coding use cases. It cross-references existing standards such as NIST AI RMF, ISO 42001, and OWASP LLM Top 10, but focuses on actionable controls engineers can implement immediately via YAML, CI pipelines, and self-attestation. The release responds to real-world AI failures — including prompt injection attacks, leaked secrets, and runaway API costs — that current governance frameworks do not adequately prevent at the code level.