MANAGED PRIVATE AI FOR BUSINESS
Customer records. Internal documents. The knowledge your company has built over years. Before you put any of it into AI, you need to know where it goes and who can access it.
Digital Seed Vault provides a managed, dedicated AI environment for your business. We work with you to define the privacy arrangements, choose a useful first application, and support the people using it.
Dedicated: $3,499/month · Dedicated Plus: $5,999/month · Custom: quoted
MONTHLY SERVICE PLANS
Every option provides a managed, dedicated environment. Choose a standard plan or discuss a configuration tailored to your needs.
What changes: Dedicated and Dedicated Plus differ in GPU count, with the same privacy approach and defined service scope. Customized capacity and support are agreed in your proposal. Performance depends on the model and workload.
Before you begin: we agree on users, workloads, support hours, logging, retention and data handling. Onboarding, integrations, custom development and nonstandard support are quoted separately. Dedicated capacity does not by itself mean on-premises hosting, physically separate hardware or regulatory certification.
Yes. We assess the additional workload, validate the two-GPU configuration, and agree on the move to Dedicated Plus at $5,999 per month before making the change. Model behavior and application compatibility are checked during the upgrade.
WHAT PRIVATE SHOULD MEAN
Dedicated compute is one part of the answer. Access, data handling, and the people operating the service matter too. We agree on these before deployment.
PUTTING IT TO WORK
Perhaps your team spends hours finding answers in procedures, preparing routine correspondence, or summarizing long documents. Those are useful places to begin—once the information is approved for the environment.
We start with one real task and sample material you are comfortable using. Together, we assess the answer quality, the checking required, and whether it actually helps. Your people remain responsible for the final work.
The service includes customer-dedicated inference infrastructure and defined consulting and support. Application connections and document workflows are scoped around what you need.
As a workflow proves useful, you can add approved material, other teams, or application connections. If demand calls for more capacity or a different model, we test the change and agree on the cost first.
Our API approach is designed to keep your application’s connection stable as the underlying infrastructure changes. Compatibility and model behavior are checked before an upgrade.
A deliberate start
Define the task, users, information and how results will be checked.
Document the hosting, access, data handling and responsibilities.
Test quality and capacity with approved or synthetic examples.
Provision the environment and hand off the operating arrangements.
Add approved work only when the evidence supports it.
PRIVACY IN EVERYDAY WORK
They can see where AI would help. They need a suitable place to use it.
Illustrative micro-case studies, not accounts of actual customers or measured results.
Private law firm
A small practice wants help organizing correspondence and building a first-pass chronology. Client information and matter boundaries make a general-purpose account an uncomfortable starting point.
A possible workflow: work from an approved document set in a scoped private environment, with access limited to the appropriate team. An attorney checks the summary against the source documents.
The aim: less time assembling the file, with professional judgment and confidentiality requirements guiding its use.
Medical practice
A practice manager wants consistent first drafts of routine administrative correspondence and clearer summaries of internal procedures. Patient information needs careful handling.
A possible workflow: demonstrate the task using synthetic or de-identified examples, then assess the deployment and required agreements before considering patient data. Staff review every output.
The aim: a more manageable administrative workload. Clinical decisions stay with qualified professionals.
Small engineering firm
Engineers spend time searching specifications, design notes, and change records. Those documents contain the firm’s own methods and its clients’ intellectual property.
A possible workflow: use an approved project collection to locate requirements and draft change summaries, with project-specific access. An engineer verifies the findings against the original records.
The aim: easier reuse of project knowledge while keeping design review and release decisions with the engineering team.
Government contractor
A contractor wants help summarizing requirements and organizing proposal material. Different projects carry different restrictions on where information can be processed and who may access it.
A possible workflow: begin with public solicitations or approved sample material. Assess the specific contract, hosting, access, and security requirements before introducing restricted information.
The aim: less repetitive preparation within an approved workflow. Suitability for restricted or classified work is not assumed.
Every example starts with a review of the actual information, access requirements, and intended use. A dedicated environment alone does not establish regulatory compliance or contract eligibility.
IF YOU ALREADY USE MICROSOFT
Microsoft Copilot for work has enterprise privacy protections. Microsoft states that prompts, responses, and organizational data accessed through Microsoft Graph are not used to train its foundation models.
Privacy alone does not make DSV the better choice for every business.
If you mainly want assistance in Microsoft 365 and its available controls meet your needs, Copilot is worth evaluating. It can also be extended with connectors and agents.
DSV is for businesses seeking dedicated inference capacity, a model and environment selected for their workload, and a provider to help manage and apply it. The distinction is the operating arrangement and support—not a blanket claim of stronger security.
The two can serve different needs within the same business.
Microsoft’s documentation: Privacy and security · Connectors and agents. Comparison concerns Copilot for work, not a custom Azure deployment.
Common questions
Model providers, processing paths and training terms are documented in the agreed configuration. The answer depends on that configuration.
Not by default. Hosting location and infrastructure are part of the proposal; dedicated capacity does not automatically mean an installation at your site.
Yes, when their classification and the agreed processing arrangement permit it. Ingestion and integrations may require separate work.
That cannot be established by a dedicated GPU alone. We must review the actual requirements, controls and agreements; some uses may be excluded.
That depends on the model, task, context length and simultaneous usage. We validate a representative workload before recommending capacity.
A CONVERSATION, FIRST
A general description is enough to begin. Please keep confidential documents and sensitive details out of the initial email.
We can work through the requirements
and see whether the service is a fit.