Trace and fix AI prompt drift by detecting, attributing, and locking prompts

Teams deploying LLMs into production increasingly face "prompt drift" where small changes in prompt phrasing, model version, or endpoint lead to silent regressions, broken SLAs, and auditability gaps; ML engineers, platform/ops teams, and compliance officers bear the pain of tracking down when and why outputs changed. This is amplified by multi-vendor LLM deployments and frequent backend updates, which make manual provenance and regression testing untenable. You could build a developer-focused platform that automatically detects output drift, attributes the root cause to specific prompt versions, model endpoints, or input changes, and offers a "lock" mechanism for immutable prompts plus CI/CD hooks, regression testing, and audit-ready provenance logs. The product would act as a unified provenance layer that compares outputs across vendors, sends actionable alerts, and provides fine-grained explainability for compliance and rollback. The market looks attractive now — a $6.0B addressable market (2M businesses × $3K ACV), with a market score of 90/100 and revenue potential 88/100 — driven by the shift from experimentation to production LLM use, multi-vendor complexity, and rising regulatory demands. You can stand out by prioritizing high-fidelity attribution, lightweight integrations with major LLM endpoints, and a frictionless prompt-locking workflow tied to developer tools, but be upfront about the operational challenge of maintaining integrations across evolving vendor APIs and the need to convince teams to adopt new prompt governance practices in their workflows.