Infrastructure
Client-controlled on-premises or private-cloud compute, networking, model serving, monitoring, backup, and recovery.
Platform / Models, data, systems, and controls
Your AI capability, assembled as one enterprise platform.
RVAI designs the operating environment through which approved employees, applications, and governed agents securely use private AI and company information.
01 / Architecture layers
The model is one layer—not the whole system.
Useful private AI depends on infrastructure, enterprise context, identity, workflow controls, operational discipline, and a model proven to fit the work.
Models
Open-weight models evaluated for reasoning quality, latency, concurrency, licensing, security, and workload fit.
Enterprise data
Permission-aware access to approved documents, databases, repositories, APIs, and business systems.
Employee access
Internal chat, company knowledge, analytical tools, and governed APIs protected by enterprise identity and access controls.
Agents & workflows
Defined tools, permissions, approval requirements, logs, validation, and human escalation for repeatable enterprise work.
Operations & governance
Usage monitoring, retention, model lifecycle management, regression testing, administration, and recovery procedures.
02 / Model selection
The model is a decision, not a religion.
The strongest system for one workflow may not be the strongest for another. RVAI evaluates candidate models using the client’s actual work and balances reasoning quality with infrastructure cost, latency, concurrency, licensing, security, context, and operating requirements.
The architecture is designed to support model lifecycle management without pretending model replacement is effortless. Every change still requires compatibility review, licensing review, and workflow regression testing.
Candidate private models may be benchmarked against leading proprietary systems, including Claude Fable-class systems where appropriate. The defensible standard is workflow-specific performance, not universal equivalence. RVAI is not affiliated with Anthropic.
03 / Deployment models
Place the system where your requirements say it belongs.
Final architecture is established during assessment and planning after security, performance, data-flow, availability, and operating requirements are understood.
On-premises
Infrastructure deployed within facilities or environments the client controls.
Private cloud
Isolated cloud infrastructure for organizations that prefer a cloud operating model.
Hybrid architecture
A designed combination of private infrastructure, enterprise systems, and approved third-party services where requirements justify it.
04 / Enterprise context
Connect AI to approved sources of truth.
RVAI can engineer permission-aware integrations with company systems where access, security, licensing, and technical feasibility permit.
Productivity
- Microsoft 365
- SharePoint
- Teams
- Slack
Business systems
- ERP systems
- CRM systems
- Finance platforms
- Reporting platforms
Data & content
- SQL databases
- File repositories
- Internal APIs
- Company knowledge
Named systems are examples of integration capability. Each client environment is scoped; a prebuilt connector is not assumed.
05 / Governance
Control what the system can see, do, and retain.
Governance is implemented as operating controls—not a policy slide. Identity, permissions, data access, tool authority, approval requirements, logging, retention, escalation, and recovery are mapped to the deployment.
Architecture follows evidence
Define the workload before choosing the stack.
The assessment benchmarks models, inventories data and integrations, maps security requirements, models economics, and produces the proposed infrastructure architecture.
Schedule an AI Infrastructure Assessment