Submissions, returns and examination findings were provided under a confidentiality expectation. Routing them through an external model — masked or not — is a conversation nobody wants to have with the entity.
Chakali for financial regulatorsSupervise with AI.
Supervise with AI.
Keep the evidence trail intact.
Supervisory work runs on information the regulated entities gave you in confidence, and produces positions that have to withstand challenge years later. Chakali keeps that material inside your boundary and keeps the reasoning behind every output on the record.
01Why this is harder here
The constraints are not
a matter of preference.
Draft positions, pending decisions and supervisory concerns are material non-public information. The boundary around them has to be architectural, not procedural.
A supervisory judgement may be contested long after it was made. “The analyst used an AI tool” is only defensible if you can show precisely what it saw and what a person decided.
A regulator dependent on a commercial provider for the analysis behind its decisions invites a question it would rather not answer.
02What teams actually build
Real work, inside the boundary.
Your teams compose these from the Skills and Tools that installed Solutions provide. Each one runs under the same identity, policy, approval and evidence model.
Supervisory analysis and returns review
Work across submissions, prior findings and peer comparisons to surface what merits attention, with every observation traceable to the document that produced it.
Policy and circular drafting
Draft consultations, circulars and guidance grounded in your own published positions and legal framework, reviewed in your institution's register before anything is issued.
Cross-jurisdiction comparison
Compare how other authorities have treated the same question, with citations to the sources you trust rather than whatever the open web offered that day.
Consultation response synthesis
Consolidate hundreds of responses into themes, dissents and recommended treatments — with the individual submissions behind every theme still one click away.
03The control model in your terms
Every consequential step
has a name against it.
- 01Analyst identity and mandate
Requests carry who is asking and under which supervisory mandate, so access follows the role rather than the network.
- 02Entity scoping
Retrieval is bounded to the entities and periods the analyst is authorised for — information walls hold inside the AI, not only around it.
- 03Deterministic policy
Rules set by the institution decide what may proceed. A model is never the thing that grants itself access to market-sensitive material.
- 04Accountable sign-off
Supervisory positions and external communications stop for the named decision owner before they go anywhere.
- 05Examination-grade record
Sources, models, tools, guardrail results, approvals and timings retained as one connected record and exportable for internal audit or an external reviewer.
04Where it runs
Deployment follows your risk position.
Inside the central bank's own data centre, integrated with existing identity, logging and recovery controls.
For the most sensitive supervisory and financial-stability work, operating with no connectivity at all.
Where the institution already operates an approved private cloud, with the same control model applied at that boundary.
Which modelsAnything touching regulated-entity submissions or market-sensitive material runs closed, on models inside your boundary. Cross-jurisdiction research over published regulation and public consultation papers is a different data class, and can be permitted an approved external model under policy — separated by workspace, and visible in the evidence record if it is ever questioned. Compare the deployment patterns →
Chakali for financial regulators