GenAI Scaling: Moving Beyond "Guardrails" to Deterministic Remediation

Despite the hype, a recent MIT report indicates that 95% of enterprise AI pilots fail to reach production. In highly regulated industries, probabilistic "guardrails" are simply not enough. Join us for a deep dive into the new architectural frameworks required to trust Generative AI in mission-critical environments.

Key Takeaways:
The "Last Mile" Problem: Why standard RAG pipelines and prompt engineering often fail to meet the strict compliance standards of Banking, Insurance, and Healthcare.

Policy-as-Code: How to apply a Deterministic Policy Engine to non-deterministic models, ensuring 100% adherence to complex organizational SOPs.

Remediation vs. Blocking: Moving from binary guardrails (that just block users) to intelligent remediation that fixes outputs in real-time.

Real-World Scale: Case studies on how Fortune 500s are reducing model fallibility from >50% to <4% to finally unlock production scale.