Do you train models using our data?

No. We do not train models on your data.

Does on-prem AI require an internet connection?

No. The default design is local AI inference without a public model API. Deployments can run with no internet connection when models, packages, and updates are prepared for offline operation.

Can the system work with internal documents?

Yes. The system can search approved internal documents and return answers with source references inside your environment.

Can you support proprietary source code?

Yes. Private coding workflows can support repository search, code explanation, test generation, and documentation without sending code to public AI services.

Which AI models do you support?

We design for model flexibility. The right model depends on the use case, hardware, context size, license terms, privacy requirements, and inference cost.

Is on-prem AI cheaper than an API?

Not automatically. The advantage is control and predictability: hardware, model, and operating costs can be estimated and reviewed instead of relying only on variable token usage.

Do we need to purchase new hardware?

Not always. We first evaluate your existing servers, workstations, storage, and network constraints. If new hardware is needed, we provide sizing guidance.

Can updates be performed offline?

Yes. Offline update packages and documented update procedures can be part of the deployment plan.

What happens after the first deployment?

You receive results, architecture notes, known limitations, and a production recommendation. If the initial workflow works, the deployment can scale to more teams, data sources, integrations, and applications.

Private AI.
Under your control.

Q: Can you ingest data from Microsoft, Google, shared drives, or databases?

Yes. A deployment can connect approved sources such as Microsoft 365, Google Workspace, shared drives, source repositories, databases, and internal document systems.

Q: Can people use it inside existing tools?

Yes. Private AI can be exposed through internal assistants, VS Code, Claude Code, Excel workflows, or custom tools built around the workflow.

OnPremWorks deploys private AI systems for engineering knowledge, proprietary code, financial analysis, internal documents, and sensitive operational work.

For teams that want the usefulness of AI without giving up data sovereignty, security review, or predictable operating costs.

Discuss a Deployment View Deployment Options

On-prem or private cloudLocal AI inferenceOffline-ready deploymentsData sovereignty by design

Data sovereignty boundaryNO PUBLIC AI API

Controlled environment

OFFLINE READY

Sensitive inputs

Docs

Microsoft / Google

Source code

Databases

Financial data

Private AI inference

Local model

Retrieval

Access control

Audit logs

Internal AI surfaces

VS CodeClaude CodeExcel pluginsCustom tools

ON-PREM OR PRIVATE CLOUDDATA STAYS INSIDE YOUR BOUNDARY

The implementation gap

The blocker is rarely interest in AI. It is control.

The teams asking for AI usually know where it would help. The hard part is putting it close to sensitive work while satisfying the people responsible for security, infrastructure, budget, and long-term support.

The useful data is usually the sensitive data

Specifications, source code, financial models, contracts, and internal records are exactly where AI helps, and exactly what should not be pasted into outside services.

IT and security have valid concerns

Identity, network boundaries, access control, logs, model behavior, updates, and support ownership all matter before AI becomes part of daily work.

API usage can be hard to forecast

Public model APIs are easy to start with, but usage-based pricing can make team-wide adoption difficult to budget, explain, and govern.

What OnPremWorks delivers

A working private AI workflow inside your data boundary.

Keep control of sensitive work

Run inference inside infrastructure you control and define where documents, prompts, outputs, logs, and indexes are stored.

Make cost easier to understand

Move from open-ended API usage toward known infrastructure, model, and operating costs that can be planned and reviewed.

Fit the security process

Design around existing identity, network, approval, and support practices instead of asking IT to accept a black box.

Leave with an operable system

Receive deployment documentation, configuration records, operating procedures, and a production recommendation.

Specific security capabilities depend on the selected architecture, operating environment, and deployment scope.

Connect and deliver

Use the systems your teams already work in.

The first deployment should prove value inside the existing operating environment, not create another tool that employees have to remember.

Connect existing sources

Ingest approved data from Microsoft 365, Google Workspace, shared drives, source repositories, databases, and internal document stores.

Deliver AI into daily tools

Expose private AI through internal assistants, VS Code, Claude Code, Excel workflows, and custom tools built around the work people already do.

Expand only when it holds up

After the first workflow is evaluated, add teams, data sources, integrations, and applications based on measured internal demand.

Typical applications

Private AI for work that should stay inside the boundary.

The pattern is the same across teams: connect approved internal material, run inference locally, and expose the result through a workflow people already use.

Engineering Knowledge

Specifications, design history, test reports, procedures, and issue investigations.

Requirement lookup
Design rationale retrieval
Test evidence search
Lessons-learned review

Proprietary Code

Repository understanding and coding assistance for code that cannot be sent to public AI tools.

Codebase search
Code explanation
Test generation
Developer documentation

Controlled Documents

Internal records, quality documents, review material, procedures, and corrective actions.

Document comparison
Risk review support
Procedure lookup
Drafting support

Financial Analysis

Financial workbooks, operating data, forecasts, and planning material that need tighter data control.

Variance analysis
Scenario review
Planning support
Executive document review

How it works

From candidate workflow to evaluated deployment.

Assess

Identify the workflow, users, data sensitivity, security boundary, existing infrastructure, and success criteria.

Design

Select the deployment architecture, hardware, models, retrieval approach, access controls, and evaluation method.

Deploy

Install the system, connect approved data sources, configure the workflow, and test against real internal questions.

Handoff

Train users and administrators, document the deployment, identify production gaps, and define the next phase.

Initial engagement

A private AI deployment starts with one defined workflow.

One team. One security boundary. One evaluated result.

The first engagement should answer practical questions: does the workflow help, can IT support it, are the costs understandable, and what would production require?

After the first workflow

Scale only where the deployment has proved useful, supportable, and appropriate for the data involved.

Engagement deliverables

Workflow and security-boundary assessment

Model and infrastructure recommendation

Private AI environment installation

One approved document, knowledge, coding, or analysis workflow

Basic user access configuration

Evaluation against agreed test questions

Architecture and configuration documentation

Administrator and user handoff session

Production-readiness recommendation

Discuss Scope

The engagement is scoped around a real workflow, a defined data boundary, and measurable feedback from the people who will use or support it.

Deployment options

Use the infrastructure model that fits your environment.

On-prem AI is not one architecture. The right shape depends on the data boundary, expected usage, available hardware, support model, and whether updates need to work offline.

Existing Infrastructure

Deploy on supported organization-owned servers or workstations.

Best for: organizations that already have appropriate computing, storage, and IT support.

Dedicated On-Prem System

Deploy on a purpose-built workstation or server located inside your environment.

Best for: teams that want a clearly defined private AI appliance or internal service.

Isolated Environment

Support environments with limited or no internet connectivity using controlled deployment and update procedures.

Best for: sensitive engineering, regulated, or operational environments.

Hardware, availability, security, and performance requirements are defined during the assessment.

Control by design

Security begins with architecture, not marketing claims.

OnPremWorks designs the deployment around the actual data boundary, network environment, identity systems, operational requirements, and risk tolerance.

Controlled storageLocal model executionRole-based accessApproved data-source boundariesNetwork isolationAudit loggingModel and component documentationControlled update proceduresBackup and recovery planningOffline update packages

Operating boundary

01Identity

02Approved data

03Local inference

04Audit trail

Architecture is the security control.

OnPremWorks does not claim that every local deployment is automatically secure. Security depends on the complete architecture, configuration, operating process, and environment.

Designed for teams with valuable internal knowledge and a real implementation owner.

Strong fit

Sensitive documents, models, or code
Clear internal workflow
Security or IT review required
Need for data sovereignty
Predictable cost matters
Existing systems to connect
Measured deployment before broader rollout
Internal owner for support

Implementation accountability

A technical assessment led by the team accountable for deployment quality.

The assessment focuses on architecture, security boundaries, model and inference tradeoffs, operating procedures, and handoff requirements before a broader rollout.

FAQ

Practical answers for private AI planning.

Contact

A useful conversation starts with the environment.

Share the workflow, the data boundary, the systems involved, and what would make IT or security comfortable with the deployment.

Email:

contact@OnPremWorks.com

San Jose, California

Discuss a Private AI Deployment

The email template asks for the primary workflow, current data sources, preferred internal tools, security or adoption blockers, and timeline.

Microsoft 365 or Google WorkspaceShared drives or document storesDatabases and source repositoriesVS Code, Claude Code, Excel, or custom tools

Open Email Template

No website account is required for new inquiries.

Private AI.Under your control.