Public AI creates data exposure concerns
Employees need useful AI tools without copying sensitive information into external services.
Private AI.
Inside your business.
Deploy on-premises AI assistants for engineering knowledge, internal documents, proprietary code, and sensitive business analysis on infrastructure your organization controls.
Protect AI privacy and data sovereignty while giving employees practical tools they can use.
On-prem or private cloudLocal AI inferenceOffline-ready deploymentsData sovereignty by design
Data sovereignty boundaryNO PUBLIC AI API
Customer-controlled environment
OFFLINE READYSensitive 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
Your teams want AI. Your sensitive data cannot leave.
Employees are already experimenting with AI, but proprietary documents, source code, product specifications, customer information, financial data, and strategy documents often cannot be sent to public AI services. OnPremWorks closes the gap between a promising proof of concept and a reliable private AI workflow.
Private AI infrastructure is complex
Models, GPUs, access controls, retrieval systems, networking, and ongoing updates must work together.
Pilots need an operational path
A demo is not enough. Employees need a workflow that is tested, documented, and supportable.
What OnPremWorks delivers
A working private AI workflow inside your data boundary.
Keep control of your data
Run on-premises AI inference on customer-controlled infrastructure and define where documents, prompts, and outputs are stored.
Start with one valuable workflow
Focus the first deployment on a clear business problem rather than attempting a company-wide platform rollout.
Avoid unnecessary model lock-in
Use a private LLM architecture that can support different models as capabilities, costs, and requirements change.
Leave with a maintainable system
Receive deployment documentation, configuration records, operating procedures, and a production recommendation.
Specific security capabilities depend on the selected architecture, customer environment, and deployment scope.
Connect and deliver
Use the systems your teams already work in.
The pilot should prove value inside the existing operating environment, not require a separate AI portal 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 plugins, and custom tools built around the workflow.
Scale after pilot success
After a successful pilot, expand by adding teams, data sources, integrations, and applications based on measured internal demand.
Typical applications
Private AI for work that cannot be pasted into a public chatbot.
Engineering Knowledge Assistant
Connect internal specifications, design documents, test reports, procedures, and historical project material.
- What requirement applies to this component?
- Where was this issue previously investigated?
- Which test reports support this design decision?
Private Coding Assistant
Provide AI-assisted coding and repository understanding for teams working with proprietary source code.
- Code explanation and repository search
- Test generation
- Documentation support
- Local coding-tool integration
Quality and Technical Documentation
Help technical teams organize and analyze structured engineering documents, quality records, review material, and corrective actions.
- Requirements comparison
- Risk-review preparation
- Lessons-learned retrieval
- Technical report drafting
Private Business Analysis
Analyze sensitive marketing, financial, operating, and strategy data without moving it into public AI services.
- Financial variance analysis
- Private market and sales analysis
- Strategy and planning support
- Board or executive document review
How it works
From blocked use case 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 customer questions.
Handoff
Train users and administrators, document the deployment, identify production gaps, and define the next phase.
Pilot program
Start with a Private AI Pilot
One workflow. One team. One clearly defined deployment.
Designed to determine whether a private AI workflow can create practical value inside your environment before a broader implementation.
After a successful pilot
Scale inside your organization by adding more teams, data sources, internal tools, and applications based on demand.
Pilot deliverables
Use-case and security-boundary assessment
Model and infrastructure recommendation
Private AI environment installation
One approved document, knowledge, or coding workflow
Basic user access configuration
Evaluation against agreed test questions
Architecture and configuration documentation
Administrator and user handoff session
Production-readiness recommendation
The pilot is built around a specific workflow, a defined security boundary, and measurable internal user feedback.
Deployment options
Use the infrastructure model that fits your environment.
Existing Infrastructure
Deploy on supported customer-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 the customer 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 customer’s actual data boundary, network environment, identity systems, operational requirements, and risk tolerance.
Customer-controlled storageLocal model executionRole-based accessApproved data-source boundariesNetwork isolationAudit loggingModel and component documentationControlled update proceduresBackup and recovery planningOffline update packages
Customer 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 customer environment.
Designed for organizations with valuable technical information and a real implementation owner.
Strong fit
- Engineering or technical organization
- Sensitive documents or proprietary code
- Clear internal workflow
- Executive or IT sponsor
- Internal infrastructure owner
- Need for greater data control
- Measured pilot before broader rollout
- Existing systems to connect
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.
Start with a pilot
Where should private AI prove value first?
Tell us about the documents, code, databases, shared drives, or internal workflow that cannot be handled through a public AI service.
Clicking the button opens a prefilled email to:
contact@OnPremWorks.comSan Jose, California
Start with a Private AI Pilot
The email template asks for company size, primary use case, current data sources, preferred internal tools, adoption blocker, and timeline.
Microsoft 365 or Google WorkspaceShared drives or document storesDatabases and source repositoriesVS Code, Claude Code, Excel, or custom tools
Open Email TemplateNo form submission or website account required.