Supervisory answers, ALCO packs, and risk analysis with an audit trail attached
Give finance, treasury, risk, and audit teams governed AI analysis across core systems, risk warehouses, policy documents, and team conventions, while keeping data in your tenancy.
See it work
Watch a supervisory request become a defensible answer
A DataQube session drawn from real customer conversations: a CRE concentration request answered with read-only queries, validated definitions, and an exportable evidence chain — methodology included.
Supervisory CRE exposure request
Demonstration based on real customer conversations — all data fictional.
Use cases
Your recurring workflows, automated.
Investment teams don't need another chatbot.They need the recurring work they already do—performance attribution, mandate reviews, and investment committee preparation—to become faster, repeatable, and fully traceable.
Supervisory data requests
Translate regulatory questions into governed analysis with SQL, methodology definitions, file citations, and reviewer-ready provenance.
- Exportable evidence chain for every figure
- Validated methodology memory
- Shorter back-and-forth with reviewers
Treasury and ALCO analysis
Rebuild liquidity, funding, and stress packs on schedule, with every data window and assumption change visible.
- Standing packs staged for approval
- Scenario deltas flagged before meetings
- Prior distributed versions retained
Policy-controlled self-service
Let teams answer their own questions while information barriers, inherited grants, and explicit denials remain enforceable.
- Single sign-on groups mapped to workspace roles
- Denials logged with the policy that fired
- Read-only data access enforced at the source
Workflow
From industry question to auditable answer
The page is organized around outcomes, but the operating model is always the same: ask, resolve evidence, publish a traceable answer.
Receive a request
A supervisor, CFO, or risk committee asks for a number and the methodology behind it.
Resolve the evidence
DataQube runs approved queries, recalls validated definitions, cites policy documents, and records every auth decision.
Export the trail
The final answer can be exported as a reviewer-readable chain from question to queries, rows, methodology, and sign-off.
Stand out with AI that runs on your data
“A governed path from supervisory request to defensible answer, with each answer connected to its source data, access decision, methodology, and audit record.”
Representative workflow example · Public Financial Institution
Read the customer story ->Featured products
The DataQube modules behind the workflow
Each industry page surfaces the platform capabilities most relevant to that buyer, while keeping one product architecture underneath.
Audit and permissions
Every data read, denial, approval, and configuration change is captured in immutable structured logs.
Tool catalog
Core banking, risk warehouse, files, and internal services are connected once and governed centrally.
Scheduled agents
ALCO, liquidity, and regulatory packs rerun with the owner's grants and workspace scope.
Memory
Reporting-date conventions, restatement boundaries, and product mappings become validated institutional memory.
Ecosystem
Connect the estate you already operate
The buyer question is practical: does this fit the controlled infrastructure, identity, logging, and internal tool estate already in place?
Kubernetes
Helm into your cluster; images through your registry
Identity
Your single sign-on groups mapped to roles and workspace policy
SIEM
Structured audit events shipped to your logging estate
Internal tools
Your own services exposed as governed, permissioned tools
FAQ
Questions industry teams ask first
Does DataQube need data to leave the bank?
No. DataQube runs in your infrastructure or tenancy, with no telemetry, diagnostics, or model calls required to leave your perimeter.
Can different departments share the platform safely?
Yes. Workspaces, inherited grants, and org-wide policies define scope; explicit deny rules always win and are auditable.
See your data answer questions - without leaving your infrastructure
Thirty minutes with an engineer: live product, deployment options, and your security team's questions answered by someone who wrote the code.