Data-resident cloud workspace and private AI

Your data under your control.

DCS helps non-US public-sector, regulated, and small-to-medium organizations reduce dependency, control AI risk, and move sensitive workflows toward environments they can govern, operate, audit, and change.

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The dependency problem

The next phase of cloud strategy is control.

The cloud productivity suite is no longer just email and documents. It is identity, storage, chat, video, records, workflow, search, compliance, endpoint management, automation, and increasingly AI. That consolidation creates convenience, but it also concentrates operational, commercial, and jurisdictional risk.

Commercial lock-in: contract, licensing, feature bundling, and roadmap leverage sits with the platform vendor.
Operational opacity: evidence often depends on vendor dashboards, standard reports, and provider assurances.
AI exposure: assistants inherit access to collaboration stores and can create new leakage paths.
Exit friction: records, links, permissions, workflows, and identities become hard to untangle.
Admin complexity: configurations become harder for administrators and easier for users to misapply.

The DCS value proposition

Compatibility first. Control where it matters.

Data residency is the start of the conversation, not the end. DCS combines location, jurisdiction, vendor dependency, cost, identity, access, records, export readiness, operational evidence, and private AI governance into a practical migration model.

Keep familiar Microsoft 365 and Google Workspace tools where they remain appropriate.

Move sensitive workflows first, with coexistence before disruptive replacement.

Bring identity, MFA, role-based access, audit logs, retention, and export readiness back under customer policy.

Use OpenDesk and Mistral-enabled private AI as deployable foundations where governance matters.

Private AI

AI inside your governance boundary.

ChatGPT, Claude, Gemini, Grok, and other AI services should be evaluated by evidence, not brand familiarity: which terms apply, who can access prompts and logs, whether content can train or improve models, which connectors can reach internal data, and who controls official or policy-shaping output. DCS points sensitive use cases toward controllable AI patterns, including self-hosted Mistral where appropriate.

DCS whitepaper

Regaining Control of Information Assets

Our latest whitepaper makes the board-level case for data-resident workspace, private AI, lower dependency risk, and a practical path away from de facto default platforms where control, cost, and governance matter most.

Whitepaper thesis

Data residency alone does not solve the control problem. Buyers also need vendor leverage, administrative control, export readiness, AI usage boundaries, cost governance, and trusted operations.

Download the whitepaper

Cost control

Control is also a cost strategy.

DCS does not assume open source is cheaper in every scenario. The stronger business case is leverage: reduce blanket premium licensing, avoid unmanaged AI add-ons, right-size cloud and SaaS usage, govern token consumption, and create credible alternatives before the next renewal or platform roadmap change.

License leverage

Identify users and workflows that do not need premium bundles, AI add-ons, or full-suite features.

AI spend control

Govern prompts, model routing, token usage, retrieval scope, and high-cost agent loops by workflow and data class.

Waste reduction

Expose duplicate tools, unused licenses, unmanaged storage growth, and overlapping cloud services.

Negotiating power

Use coexistence and selective migration to create credible alternatives instead of accepting default renewal economics.

Resources

Useful starting points for data-resident control.

These pages are written for buyers comparing the de facto default platforms with practical alternatives for sensitive data, communication, and AI workflows.

WhitepaperRegaining Control of Information Assets

The board-level case for dependency reduction, cost leverage, private AI, and trusted operations.

AssessmentMicrosoft 365 and Google Workspace Data Residency Assessment

How to map data location, admin access, AI exposure, licensing, and migration priority.

Private AIPrivate AI and Data Leakage Governance

Control prompts, logs, retrieval, model routing, and sensitive information exposure.

ImplementationOpenDesk Implementation Services

A practical path from M365 and Google coexistence to an open-source, data-resident workplace.

Europe is moving

Governments are pursuing data-resident digital strategies to reduce over-dependence on a small group of mostly US cloud, productivity, and AI providers.

Germany's model

ZenDiS, openDesk, and openCode show how public administrations can package open-source workplace tools and shared code into data-resident alternatives.

France's cloud doctrine

France's trusted-cloud approach and SecNumCloud certification emphasize jurisdiction, security, and protection from non-European extraterritorial access.

Start here

DCS Data Residency and AI Control Assessment

In 2-4 weeks, map dependency and residency, identify cost and AI exposure findings, shortlist sensitive workflows, and define the first practical implementation proposal.

Dependency and data-flow map
License, cloud, and AI cost baseline
Sensitive-workflow shortlist
Coexistence and migration plan

See the assessment offer

DCS delivery model

Assess, implement, and manage.

DCS moves from evidence to controlled migration and then into trusted managed operations, with private AI and data-resident workflows governed inside the customer's control boundary.