Private AI for financial services
Research, monitoring and client-service automation that keeps regulated data, positions and strategies inside your perimeter — with the audit trail your supervisors expect.
Common deployments
Research & analysis
Summarisation and extraction across filings, research and internal documents.
Compliance monitoring support
Assistants that surface relevant policy passages and flag items for review.
Client service drafting
Response drafting grounded in approved product documentation.
Report automation
First drafts of recurring reports from structured internal data.
Fraud-analysis support
Case-note summarisation and pattern description for investigation teams.
Code & strategy privacy
Coding assistants for quant teams whose IP cannot touch external APIs.
Compliance posture
- Data, prompts and outputs remain in-perimeter for regulatory review
- Complete, tamper-evident audit logging to your own systems
- Access control aligned with existing entitlement systems
- Deterministic cost base — no usage-driven vendor spend to explain
- Vendor-concentration and outage risk removed from a critical capability
Why on-premise here
Public AI endpoints create a data-processing relationship your compliance team must defend. Keeping inference inside your infrastructure removes that transfer entirely: prompts, documents and outputs never leave systems you control, and audit logs live in your own SIEM.
Frequently asked questions
How does this fit with our outsourcing and vendor-risk rules?
Because inference runs on your infrastructure, there is no material outsourcing of data processing to assess for that workload — the engagement is professional services, and everything we build is handed over.
Can trading or research IP stay fully isolated?
Yes. For the most sensitive teams we deploy separate, even air-gapped, instances so strategies and code never share infrastructure with general workloads.
What about model risk management?
Deliverables include evaluation suites, model documentation and change logs designed to slot into your existing model-risk framework.