Reduced ad-hoc requests by 87%.
Without relaxing a single governance control - governed self-serve, certified metrics, and an audit-ready trail, deployed in 3 weeks.
The challenge
The firm's data team had become a bottleneck. Every quarter-end brought a flood of one-off requests - variance breakdowns, regulatory cuts, board-deck numbers - and business users couldn't self-serve without risking figures that didn't reconcile.
Compliance made it harder still: any analytics tool had to keep data inside the warehouse, enforce who could see what, and produce a defensible audit trail for every number that reached a regulator.
The solution
Quaeris was deployed warehouse-native in days. The team defined their core metrics once in the governed semantic layer, so "net revenue" and "exposure" meant the same thing in every answer.
- Certified metric definitions versioned and owned, so numbers reconcile across every report.
- Role-based access enforced at query time - analysts see only what their role permits.
- Every answer cites its source metric and lineage, giving auditors an end-to-end trail.
- WarehouseData never leaves
- Semantic layerCertified metrics
- AI agentPlain-language Q&A
- Access controlRole-scoped + audited
Every question resolves through the governed path - sourced, role-scoped, and logged end to end.
The results
Within a quarter, business users were asking questions in plain language and getting governed answers in minutes. Ad-hoc requests to the data team dropped sharply, and not one number that reached the board or a regulator was unsourced.
Self-serve access expanded across finance and risk - without loosening a single control.
We finally gave the business self-serve analytics without giving up control. Every answer is governed, sourced, and audit-ready - and our team got its time back.
See what governed analytics looks like on your data.
Book a 30-minute demo and we'll show you a governed answer - sourced, role-scoped, and audit-ready - on a dataset like yours.