Case study / Enterprise
Sovereign on-prem LLM deployment for a regulated financial institution
An open-weight model deployed inside the institution's own perimeter, evaluated against internal benchmarks, with audit logging and data-residency controls, handed over with operations runbooks.
A regulated financial institution wanted large-language-model capability for internal analysis and document work, under a mandate that customer and supervisory data could not leave its perimeter.
The most capable hosted models were off the table for the sensitive workloads. A hosted API could not satisfy the data-residency and audit obligations, and an upstream policy or contract change was an unacceptable single point of failure for a core capability.
- Open-weight model candidates evaluated against the institution's own benchmark set rather than public leaderboards
- Deployment inside the institution's perimeter, with sizing and quantization matched to the available hardware
- Audit logging, prompt filtering, and role-based access wired in from the first deployment
- Data-residency controls designed to survive a supervisory audit
- Operations runbooks for model updates, drift monitoring, and incident response handed to the internal team
The institution runs LLM-backed work on infrastructure it controls, with no dependency on an external API for the sensitive path. The capability cannot be revoked by an upstream policy change. Hookseek continues under Managed AI Operations.
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