I’m working on an article about how companies evaluate AI reliability in real production environments beyond benchmark scores. I’m looking to speak with companies actively using AI internally or in customer-facing workflows, not AI vendors or model providers. I’m especially interested in operational insights from SaaS companies, fintech platforms, ecommerce brands, marketplaces, healthcare companies, enterprise software teams, and other organizations deploying AI at scale. The goal is to better understand how teams monitor model quality, handle hallucinations, validate outputs, and build human review processes as AI systems become more integrated into daily operations. Questions include: What evaluation metrics matter most in production environments today? How often are models manually audited or reviewed by humans? What types of errors escape automated evaluation systems? Which industries require continuous human review or validation layers? What operational lessons surprised teams most after deployment?