Delivery process / From evidence to operations

Production begins with evidence.

The engagement starts by defining the work, requirements, economics, and acceptance criteria. Infrastructure and workflows are then implemented and tested against those standards before production launch.

Five phases

A measured route to production.

Published durations are planning ranges. Phases may overlap depending on project scope, dependencies, procurement, client readiness, and the work that can proceed in parallel.

01

Assessment

2–3 weeks

Define the business case, priority workflows, model requirements, architecture, economics, risks, and acceptance criteria.

Representative work and outputs

Priority use cases
Model benchmark plan and results
Current AI cost and ROI analysis
Data, integration, and security inventory
Infrastructure architecture and hardware bill of materials
Acceptance criteria and fixed implementation proposal

02

Architecture & Planning

2–4 weeks

Finalize infrastructure, security, integrations, delivery sequencing, capacity, governance, and the production plan.

Representative work and outputs

Final infrastructure and network design
Identity and access design
Integration and data-flow design
Governance and agent-control model
Backup, recovery, and operations plan
Implementation sequencing and responsibilities

03

Implementation

10–20 weeks

Deploy infrastructure, configure model serving, build the platform, connect company systems, and implement priority workflows.

Representative work and outputs

Inference infrastructure and model serving
Enterprise access and administration layer
Approved data and system integrations
Priority agents and workflows
Monitoring, logging, backup, and controls
Documentation and operating procedures

04

Validation & Launch

2–4 weeks

Benchmark on agreed workflows, test security and controls, train users, resolve gaps, and complete production acceptance.

Representative work and outputs

Workflow and model acceptance testing
Security and permissions testing
Benchmark regression results
Operational readiness review
User and administrator training
Production acceptance and transition

05

Managed Support

Ongoing

Monitor the system, maintain models and controls, support users, review governance, and add valuable workflows over time.

Representative work and outputs

Infrastructure and security maintenance
Model evaluation and controlled upgrades
Benchmark regression testing
Performance and capacity optimization
Workflow modifications and new agents
User support and governance reviews

Production acceptance

No production claim without production criteria.

Acceptance criteria are agreed before implementation. Model quality, workflow behavior, permissions, escalation, security, operational readiness, and other in-scope requirements are validated before launch.

Business workflows are represented by agreed test cases.
Quality and operating thresholds are documented before the build.
The production system—not only a lab model—is tested.
Gaps are resolved or explicitly accepted before launch.

Implementation discipline

Built with the client, transferred to the client.

RVAI leads the technical and implementation work while client leaders and system owners supply decisions, access, requirements, subject-matter expertise, security review, and acceptance authority.

01

Clear ownership

Architecture, decisions, dependencies, controls, risks, and acceptance responsibility are made explicit.

02

Working documentation

The deployment includes architecture, configuration, workflows, operating procedures, and support documentation.

03

Operational transfer

Users and administrators are trained for their roles before production operations begin.

Phase one

Start by defining what production must prove.

The AI Infrastructure Assessment produces the benchmark evidence, architecture, economics, schedule, risk view, and acceptance criteria needed for an informed implementation decision.

Schedule an AI Infrastructure Assessment