Frontier-class AI. Inside your business.

It can on defined workflows when the right model, infrastructure, context, and controls are selected—but that result should be demonstrated, not assumed. RVAI benchmarks candidate private models against leading proprietary systems, including Claude Fable-class systems, using the client’s selected workflows and agreed acceptance criteria. This is not a claim of universal equivalence across every task, and RVAI is not affiliated with Anthropic.

The assessment defines representative tasks, evaluation criteria, quality thresholds, latency and concurrency requirements, and the comparison set. Candidate models are tested on those workflows before architecture is finalized, then regression-tested before production acceptance.

The architecture is designed so sensitive data can remain within the client’s chosen environment. Actual data flows depend on the approved deployment model, integrations, support procedures, and any third-party services the client elects to use; these are documented during architecture and security review.

Yes, where those systems provide an appropriate technical and security path. RVAI can design integrations for Microsoft 365, SharePoint, Teams, Slack, email, SQL databases, ERP and CRM platforms, file repositories, finance tools, and internal APIs. Integration scope is validated for each environment rather than assumed to be prebuilt.

The platform is designed to remain model-aware without making the business workflow unnecessarily model-dependent. Licensing, architecture, hardware compatibility, and workflow validation still matter, so model portability is planned and tested—not promised as a one-click swap.

It depends on model quality, concurrent users, response-time targets, context requirements, security, availability, and budget. The assessment produces a workload-specific architecture and hardware bill of materials rather than prescribing one platform in advance.

A typical program includes a two-to-three-week assessment, two to four weeks of architecture and planning, ten to twenty weeks of implementation, and two to four weeks of validation and launch. Phases may overlap depending on scope and client readiness.

Depending on scope, the platform can include SSO, role-based access, document-level permissions, data isolation, secrets management, audit logs, retention controls, approval gates, human escalation, monitoring, backups, and disaster recovery. Final controls are mapped to the client’s environment and requirements.

Yes. Agents can perform defined work continuously, but autonomy is bounded by permissions, tool access, audit logging, approval requirements, validation checks, and human escalation. The level of autonomy is selected workflow by workflow.

RVAI can evaluate the model against the existing benchmark suite, licensing and security requirements, infrastructure fit, and workflow behavior. An upgrade proceeds only after regression testing shows that the change meets the client’s acceptance criteria.

AI Infrastructure Assessments begin at $25,000. Scope and price are confirmed before work begins.

Private enterprise AI implementations typically range from $250,000 to $500,000+, depending on infrastructure, integrations, security requirements, production workflows, availability requirements, and organizational scope. Hardware, third-party infrastructure, and managed support are scoped separately.