The problem
A fast-growing cloud-native startup had deployed multiple AI models in production with no monitoring, no security controls, and no visibility into model drift or data quality degradation. Two of their models had been silently producing incorrect outputs for weeks before anyone noticed.
What we did
Implemented a full AI security and monitoring layer across all production models. Built automated drift detection, data quality checks, and adversarial input testing into every model pipeline. Established governance controls for model updates and version management.
The outcome
Silent model failures eliminated. All models now monitored continuously in real time. Regulatory audit readiness achieved — critical for the startup's upcoming Series B due diligence.
