Financial AI Challenges
In finance, AI is only deployable when you can prove control: where data was processed, which policies applied, how outputs were produced, and how changes are governed. Most programs fail when they depend on centralizing sensitive data, creating new copies, and stitching together un-auditable pipelines.
Auditability and Model Governance
Finance needs defensible AI: traceable inputs, controlled execution, and evidence-ready outputs for internal risk functions and external regulators.
Traceability across data, code, and execution
Controlled change and deployment pathways
Evidence for audits and operational resilience
Data Gravity and Legacy Estates
Core banking systems, market data stacks, and regulated stores cannot be refactored on a vendor timeline. AI must integrate without forced migration.
Integration across heterogeneous platforms
Avoid new copies of sensitive datasets
Modernization without disruptive rewrites
Operationalization Under Control
The hard part is not the model. It is repeatable, governed execution in production across teams, environments, and security boundaries.
Standardized patterns instead of one-off pipelines
Controlled deployment across on-prem and cloud
Operational resilience without platform sprawl
Built on an open, proven foundation
Scalytics builds on Apache Wayang, the cross-platform data processing framework created by our founding team and now an Apache Top-Level Project. Instead of moving data into centralized infrastructure, Scalytics brings compute to your data so you can deploy AI and analytics across distributed sources without violating compliance boundaries.


