Banking · capital markets · insurance
Banks and insurers are under two incompatible pressures: ship agentic products fast, and prove the same data discipline the regulator expects of core systems. SoverAI gives you regional isolation, defensible model routing, and audit you can file — not a shadow AI stack in a public chat product.
7
Jurisdictions on platform
<50 ms
p50 design target
24/7
Sensible ops (tiered)
0
Silent cross-border failovers
Regulators expect the same level of control over AI as for core systems — with clearer lineage and data boundaries than most cloud-native stacks default to.
Triage and narrative generation must not leak customer artifacts through a global retriever, or the entire risk model of your branch network unravels in a single integration mistake.
A mis-routed model version or a vector index that straddles venues is a market abuse and resiliency issue, not a bug you fix next sprint.
The board and regional supervisors will ask: where is the data, who approved the exception, and can we reproduce the decision. That answer cannot live in a spreadsheet that AI grew.
A single control plane: jurisdiction-first routing, agent guardrails, and evidence-grade audit you can hand to a supervisor without re-architecting your estate.
Patterns we see in diligence and architecture reviews with regulated customers — not exhaustive, but designed to be concrete for internal steering committees.
Mapped to the frameworks your legal and second-line teams already use — with exportable evidence bundles per region.
Regulatory context: RBI, DPDP, MAS, FCA, and US prudential expectations where you operate — framed as a programme your second line can test, not a slide that only marketing believes.
We will map your data classes, model catalog, and regulator expectations to a deployable control-plane design — with exportable artifacts for legal and security stakeholders.