Private, compliance-ready AI – deployed in your environment, under your control.

Duration: 3–6+ weeks (typical)

For regulated and data-sensitive organizations (finance, healthcare, legal, public sector) that want AI capability but cannot send data to public SaaS models or US-hosted services.

Many organizations face real constraints: data residency requirements, EU AI Act compliance (the high-risk provisions take full effect on 2 August 2026), and the risk of sending sensitive data through US-controlled infrastructure where the CLOUD Act applies regardless of server location.

This is not “install an LLM and hope” – we deliver a complete, production-ready setup: model/runtime, security baseline, retrieval layer, monitoring, and a first real use case.

Typical use cases

Internal knowledge assistant with source citations (policies, procedures, technical documentation)
Secure document processing (summaries, classification, extraction)
Case-handling support – pre-scoring, drafts, next-step suggestions (e.g. AML alert triage, claims routing)
Compliance-ready search across internal repositories
Controlled automation with a human in the loop

Validated in practice

Validated on 1,000 real production documents in a regulated Norwegian industry. A 9-billion-parameter open-source model matched the world’s best commercial API (Anthropic Claude Opus) to within 1.6 percentage points – before any fine-tuning, at roughly 1/300 of the inference cost. With domain-specific fine-tuning, the model improves beyond what any general-purpose API can achieve on your specific task.

The compounding advantage

When you use a cloud API, the model learns nothing from your usage. With a sovereign model, every expert correction becomes training data. After 6–12 months you don’t just have a cheaper AI – you have a model that outperforms any general-purpose API on your specific workflow. And you own every part of it.

Engagement structure

Phase 1 – Diagnostic audit (2 weeks)

Benchmark of 5–8 open-source models on the client’s actual data. Deliverable: hard numbers on accuracy, latency, and cost compared with the current approach.

Phase 2 – Migration and optimization (6–8 weeks)

Deploy the model on sovereign infrastructure, build a validation layer, integrate with existing systems, validate accuracy.

Phase 3 – Managed improvement (ongoing)

Quarterly retraining based on production corrections, accuracy/drift monitoring, infrastructure management, compliance documentation.

What this is NOT

A generic AI strategy workshop
A ChatGPT wrapper without data controls
A promise of 100% accuracy

Who it’s for

Regulated/data-sensitive organizations (finance, healthcare, legal, public sector)
Organizations with strict data residency and compliance requirements
Teams that need AI capability with security and auditability as the top priority

What happens next?

Expand to more workflows with Internal AI tools, or productize it into a customer-facing initiative with MVP Enterprise.

Sovereign AI and managed models