Private AI for real enterprise work.
From private model runtime, enterprise knowledge and RAG, to agentic workflows, tool use, system integration, permissions, auditability, and human control, I provide AI systems that can operate in real business environments, expand over time, and support enterprise work—not just a locally installed model.
Private AI is about more than running a model locally.
It gives the organization greater control over data boundaries, knowledge access, tool permissions, network access, and operating rules. AI can move deeper into real work while remaining within clearly defined organizational boundaries.
Data Boundary
Process sensitive information within an agreed architecture and operating environment, reducing unnecessary external exposure.
Knowledge Ownership
Turn project files, policies, templates, historical experience, and enterprise knowledge into reusable AI context.
Workflow Integration
Move AI from isolated chat interactions into real business workflows and repeatable operational processes.
Control & Governance
Design permissions, tool access, network access, approvals, auditability, and human intervention around enterprise policies.
From runtime foundations to knowledge, workflows, tools, and governance.
Service levels are defined primarily by system capability, execution authority, and governance depth—not by a fixed number of users or departments.
Private AI Platform
- Private model deployment
- Web / API access
- Basic configuration and status management
- Testing and operational capability
Enterprise Knowledge & RAG
- Enterprise knowledge base
- Document and project material ingestion
- RAG and contextual retrieval
- Knowledge scope and permission boundaries
Agentic Workflow
- Simple and multi-step workflows
- Agent coordination
- State management and task routing
- Verification, retry, and human intervention
Tools & System Integration
- Files and APIs
- Approved web search
- Databases and business systems
- Enterprise tool integration
Governance & Control
- Permission control
- Logs and auditability
- Approvals and Human-in-the-loop
- Exception and high-risk action control
Deployment & Handover
- Installation and configuration
- Functional and scenario testing
- Operating and management guidance
- Ongoing optimization and upgrades
Service levels are defined by what the AI can do—not by how many people use it.
Each level introduces a different capability boundary, operational risk surface, and governance requirement: from stable runtime, to enterprise intelligence, to controlled execution.
Private AI Platform
Establish a private AI foundation that can be operated, tested, managed, and maintained reliably.
- Private model runtime
- Web / API access
- Basic platform and configuration management
- Status monitoring and basic logs
- Testing and basic operations
- Deployment and operations documentation
Knowledge & Workflow AI
Adds enterprise knowledge, RAG, and controlled workflows while intentionally limiting autonomous execution.
- Everything in Foundation
- Enterprise knowledge base / RAG
- Document understanding and knowledge retrieval
- Simple workflows
- Basic agents
- Basic human confirmation points
- No autonomous external web search
- No general-purpose tool calling
- No autonomous creation or modification of enterprise files
Enterprise AI Operating System
Adds tools, system connectivity, advanced agentic workflows, and full governance so AI can execute real work within defined boundaries.
- Everything in Standard
- Approved web search
- Tool calling
- File creation and modification
- API / database / enterprise system integration
- Multi-agent and advanced workflows
- Verification, retry, and re-plan
- Permissions, approvals, and Human-in-the-loop
- Full auditability and traceability
Capability boundaries are also risk boundaries.
Standard is not simply a reduced Enterprise tier. It intentionally limits the action surface. Enterprise introduces real execution capability and therefore requires a more complete governance system.
| Capability | Foundation | Standard | Enterprise |
|---|---|---|---|
| Private Model Runtime | ✓ | ✓ | ✓ |
| Platform Testing & Operations | ✓ | ✓ | ✓ |
| Knowledge Base / RAG | — | ✓ | ✓ |
| Simple Workflow | — | ✓ | ✓ |
| External Web Search | — | — | ✓ |
| General Tool Calling | — | — | ✓ |
| File Creation / Modification | — | — | ✓ |
| Enterprise System Integration | — | — | ✓ |
| Advanced Agentic Workflow | — | Limited | ✓ |
| Audit & Traceability | Basic Logs | Basic Records | Full Audit |
Standard is intentionally constrained, not incomplete.
Many organizations primarily need knowledge retrieval, document analysis, report generation, policy lookup, project-material summarization, and knowledge reuse. They may not need AI to autonomously operate files, access the web, or control enterprise systems.
Private deployment is not security by itself. Policy is where security begins.
Private deployment can reduce data exposure, but effective security depends first on the organization’s policies, rules, responsibilities, and operating discipline, followed by architecture, configuration, permissions, infrastructure, and actual operating practice.
Without clear data classification, access rules, responsibility assignment, approval requirements, and exception-handling procedures, technical architecture alone cannot guarantee security.
Move from “AI that can chat” to AI that can solve real work problems.
Adoption can begin with knowledge-intensive, repetitive, and relatively well-defined work, then expand toward deeper automation and system execution where appropriate.
Designed to grow without rebuilding from zero.
An organization can begin with a stable private platform, add knowledge and workflows, and later expand toward tool use, system execution, and full governance.
Runtime
Establish a stable, maintainable, and testable private AI foundation.
Intelligence
Add enterprise knowledge, RAG, and controlled workflows so AI can understand and assist work.
Execution + Governance
Add tools, systems, and governance so AI can execute real work within clearly defined boundaries.
Enterprise deployments generally receive newly released standard platform capabilities without additional feature licensing fees. Custom development, dedicated integrations, major infrastructure changes, third-party services, and selected premium capabilities may be quoted separately.
Be clear about what this service is—and what it is not.
This Service Is
- Private AI platform deployment
- Enterprise knowledge and RAG integration
- Workflow and agentic workflow design
- Controlled automation and enterprise system integration
- Permission, audit, and human-control design
- Enterprise AI capability building
This Service Is Not
- A generic chatbot
- A one-click AI appliance that requires no management
- Unlimited autonomous AI
- A promise that AI can replace all employees
- A guarantee of absolute data security
The technology stack serves the system—not the other way around.
Components are selected according to the enterprise environment and target use case. Technology is an implementation means; the goal is a system that can operate, scale, and be governed.
Pricing is based on deployment tier and implementation scope.
Final pricing typically depends on the service level, hardware and model selection, data scale, integration complexity, customization, migration work, and support scope—not simply on the number of users.
Start with one real business scenario.
If your organization is exploring private LLM deployment, enterprise knowledge bases, RAG, agent workflows, or AI automation, begin with one high-frequency, clearly defined, measurable workflow.