AI Infrastructure Assessment
A paid, two-to-three-week assessment that determines whether private enterprise AI is technically and financially justified.
ViewServices / Full-lifecycle implementation
A complete path from business case to production.
RVAI combines infrastructure architecture, model evaluation, enterprise integration, workflow implementation, production validation, user enablement, and ongoing support.
Engagement architecture
One accountable implementation path.
The work begins by proving whether private AI is justified. When it is, RVAI carries the program through architecture, infrastructure, platform, workflow, validation, launch, and managed operations.
Private AI Infrastructure
Client-controlled inference infrastructure designed around reasoning quality, concurrency, latency, security, licensing, and budget.
ViewEnterprise AI Platform
The secure enterprise layer through which employees, applications, and governed agents access private models and company information.
ViewAI Agents & Workflow Automation
Production agents that perform defined work across company systems under explicit permissions, approvals, audit logs, and escalation rules.
ViewManaged AI Operations
Ongoing infrastructure, model, workflow, security, and governance support after production launch.
View01 / Service
AI Infrastructure Assessment
A paid, two-to-three-week assessment that determines whether private enterprise AI is technically and financially justified.
Assessment deliverables
- Priority use cases and workflow requirements
- Model evaluation and client-specific benchmarking
- Current AI cost, ROI, and payback analysis
- Architecture, hardware bill of materials, and implementation plan
- Production acceptance criteria and fixed implementation proposal
- Data, integration, and security requirements inventory
- Implementation schedule and fixed implementation proposal
02 / Service
Private AI Infrastructure
Client-controlled inference infrastructure designed around reasoning quality, concurrency, latency, security, licensing, and budget.
Capabilities may include
- On-premises or private-cloud deployment
- Apple Silicon, NVIDIA, or AMD infrastructure when appropriate
- Distributed inference, model serving, and optimization
- Capacity, concurrency, network, and availability planning
- Monitoring, recovery, and model lifecycle management
- Secure remote administration, monitoring, alerting, backup, and recovery
RVAI remains model-agnostic. Large open-weight models, including Kimi-class systems, may be evaluated when appropriate. Model and hardware selection is based on client-specific workflows, licensing, security, concurrency, latency, infrastructure, and budget.
03 / Service
Enterprise AI Platform
The secure enterprise layer through which employees, applications, and governed agents access private models and company information.
Capabilities may include
- SSO, role-based access, and user provisioning
- Internal chat, governed APIs, and company knowledge search
- Document-level permissions, data isolation, and secrets management
- Audit logging, retention controls, and usage monitoring
- Approval gates, human escalation, backup, and recovery
- Administrative, retention, backup, and disaster-recovery controls
04 / Service
AI Agents & Workflow Automation
Production agents that perform defined work across company systems under explicit permissions, approvals, audit logs, and escalation rules.
Capabilities may include
- Finance reporting, forecasting, and management analysis
- Document, contract, policy, and research workflows
- Internal knowledge and executive briefing preparation
- Data reconciliation and controlled communications
- Integration with enterprise systems and internal APIs
- Controlled email, document, and enterprise-system actions
“Autonomous” does not mean unrestricted. Agent authority is defined through permissions, tool access, validation, approval requirements, audit logs, and human escalation.
05 / Service
Managed AI Operations
Ongoing infrastructure, model, workflow, security, and governance support after production launch.
Capabilities may include
- Infrastructure monitoring and security updates
- Model upgrades and benchmark regression testing
- Performance optimization and capacity planning
- Workflow modification and new agent development
- User support, backup validation, and governance reviews
- Backup validation, governance review, and user support
Potential integrations
Connect AI to approved systems of record.
RVAI can engineer integrations where the client’s systems provide an appropriate technical and security path. Integration scope is established during assessment and planning.
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
These are integration capabilities—not a claim that a prebuilt connector already exists for every product, version, or configuration.
The first service is the decision gate
Know what should be built before committing to the build.
The assessment determines technical feasibility, workflow fit, economic justification, architecture, risk, schedule, and production acceptance criteria.
Schedule an AI Infrastructure Assessment