AI Model Consistency

Engineering teams are searching for solutions to reduce hallucination and output drift in production LLM deployments, driven by the shift from demo to real-world use. The intent is purely informational—teams are diagnosing the problem, not evaluating vendors. With no dominant competitor yet, the opportunity lies in building evaluation frameworks or guardrails that surface consistency metrics during development.