Sector
Data & AI
Data and AI work shows up across every sector we serve. From healthcare data integrity to Web3 fraud detection to fintech ML platforms, we treat data and AI as a foundation, not a feature.
Sector context
Data and AI are not a vertical; they are a horizontal capability that has shaped most major engagements at Cipherer. The work spans data lakehouses (TrustWallet on Databricks and Delta Lake), national data platforms (NHS Digital SUS+), production-grade ML platforms (Sainsbury's Bank Banking Data Platform), and AI integration patterns built on enterprise rails.
Track record
- TrustWallet (Binance)
Web3 data lakehouse on Databricks and Delta Lake, with SageMaker-driven ML for fraud detection at 220M+ user scale.
- NHS Digital SUS+
National healthcare data platform migrated to AWS with full data lineage and integrity preserved across records covering ~69M patients across England.
- Sainsbury's Bank Banking Data Platform
Fully serverless banking data platform with integrated ML/AI pipelines for financial modelling, marketing analytics and operational forecasting. Production-grade Data Science programme with governance and automated model deployment.
How we approach this sector
Data foundations before model ambitions
Most AI failures are data failures. We invest in lineage, governance and access patterns first; models earn their place on top of that foundation.
Lakehouse where it earns its keep
Databricks and Delta Lake are right when both ML-native and SQL-native access matter and governance has to be machine-checkable. Where that is not the case, we use a more conventional warehouse pattern.
AI integrated, not adjacent
Bedrock, SageMaker, Databricks ML and equivalent platforms are integrated into existing engineering practice. Model lifecycle, evaluation and rollback are pipeline events, not heroics.