Get on the path to cloud confidence.

Fraud detection & risk models.

Deploy new fraud detection models on AWS using LaunchDarkly feature flags to gate releases by customer segment. Teams can validate accuracy and performance in production before broad rollout, reducing false positives and customer friction.

Regulatory compliance.

Use Guarded Releases with automated rollback and CloudTrail Lake audit logs to ensure every update to trading platforms or payment systems can be traced, audited, and reversed instantly if thresholds are breached.

Customer engagement experiments.

Financial institutions can run A/B tests on digital banking features (like mobile onboarding flows or savings recommendations) with full isolation between test and control groups, measuring engagement while preserving compliance boundaries.

Clinical decision support.

Gradually roll out new diagnostic algorithms or patient monitoring dashboards to a subset of clinicians. If issues arise (e.g., model drift, latency spikes), roll back instantly to the prior version without interrupting care delivery.

HIPAA-ready governance.

Enforce strict access controls for feature management with SSO/MFA, ensure PHI-sensitive features are only available in compliant regions, and provide audit trails to satisfy HIPAA and FedRAMP requirements.

Iterative healthcare tools.

Test new note-taking or telehealth tools with small patient cohorts, collecting usability and reliability metrics before scaling system-wide, reducing the risk of introducing instability into critical care workflows.

Content moderation.

LaunchDarkly flags allow teams to trial new moderation models (e.g., toxicity detection in chat) on AWS in real time, targeting by geography or user tier to comply with local policies and measure impact before full rollout.

Personalized recommendations.

Streaming and gaming companies can test algorithm changes on a subset of users, monitoring engagement and churn before extending to their global user base. Real-time rollback prevents widespread quality drops.

A/B testing at scale.

Media platforms can run parallel experiments on ad placement, subscription offers, or UI changes, measuring effects on revenue per user, session length, and retention—all without pausing deployments or requiring parallel code branches.