AI infrastructure assessment
A short paid assessment that identifies the best use cases, tests model options, reviews security and integrations, estimates costs and benefits, and gives you a clear implementation plan.
ViewServices / Enterprise AI / Richmond, Virginia
Enterprise AI on infrastructure your company controls.
RVAi builds an internal AI system for your employees, connects it to approved company information and tools, and runs it on infrastructure your company controls.
Enterprise AI services
What we build, from the first assessment through ongoing support.
We start with the work you want AI to help with. Then we choose the right model and infrastructure, connect approved company systems, test it with your users, launch it, and provide dedicated software development support afterward.
AI infrastructure
Dedicated hardware or private-cloud capacity sized for the number of employees, the work they need to do, response speed, security, and budget.
ViewEnterprise AI platform
A secure internal system where employees can use enterprise AI with approved company information, tools, and applications.
ViewAI agents and workflow automation
Automated AI workflows that can research, analyze, prepare work, and take approved actions within clear limits and review rules.
ViewDedicated operations
Ongoing software development and operating support for the AI, automation, integrations, reporting tools, and internal systems that matter most.
View01 / Service
AI infrastructure assessment
A short paid assessment that identifies the best use cases, tests model options, reviews security and integrations, estimates costs and benefits, and gives you a clear implementation plan.
Assessment deliverables
- Priority use cases and workflow requirements
- Test leading private models on real company tasks
- Estimate current AI spend, expected savings, and payback
- Review the company information, systems, and security requirements
- Recommend the hardware or private-cloud setup and expected cost
- Define the schedule, success measures, and implementation proposal
The initial consultation is complimentary. Assessment scope and pricing are confirmed in writing before work begins.
02 / Service
AI infrastructure
Dedicated hardware or private-cloud capacity sized for the number of employees, the work they need to do, response speed, security, and budget.
Capabilities may include
- On-premises or private-cloud deployment
- Apple Silicon, NVIDIA, or AMD infrastructure when appropriate
- Configure the models and tune the system for the required speed and workload
- Size capacity for expected users, busy periods, networking, and uptime needs
- Set up monitoring, backup, recovery, and controlled model updates
We choose the model and hardware based on the work, number of users, required speed, security needs, and budget.
03 / Service
Enterprise AI platform
A secure internal system where employees can use enterprise AI with approved company information, tools, and applications.
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
04 / Service
AI agents and workflow automation
Automated AI workflows that can research, analyze, prepare work, and take approved actions within clear limits and review 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
Automated workflows only receive the tools and permissions they need. Sensitive actions can require approval, and every action is logged.
05 / Service
Dedicated operations
Ongoing software development and operating support for the AI, automation, integrations, reporting tools, and internal systems that matter most.
Capabilities may include
- Dedicated software development support
- Internal tools, integrations, reporting, and workflow automation
- Reduce dependence on costly point solutions where the business case supports it
- Model upgrades and benchmark regression testing
- Infrastructure monitoring, security updates, backup validation, and user support
Your own AI vs. subscriptions
Models you can run on your own systems are now competitive on serious business work.
Companies no longer have to assume that every capable AI system must come from a premium hosted subscription.
| Evidence | Kimi K3 | Premium frontier comparison |
|---|---|---|
| AA-Briefcase, July 21 snapshot | 1,543 overall Elo; 1,754 analytical-quality Elo | Fable 5: 1,574 overall; 1,744 analytical quality |
| Current AA-Briefcase leader | K3 remains the highest-ranked open-weight entry in the published leaderboard | Opus 5 Max: 1,721 overall Elo |
| Arena WebDev, July 27 snapshot | K3 Max: 1,682 | Opus 5 Max: 1,725; Fable 5: 1,629 |
| API list price per 1M input / output tokens | $3 / $15 | Opus 5: $5 / $25; Fable 5: $10 / $50 |
| Deployment control | Full weights available under the Kimi K3 License | Proprietary service access |
Capability evidence
Models you can run yourself are no longer a generation behind.
AA-Briefcase evaluates realistic knowledge work involving spreadsheets, presentations, interfaces, and complex source files. K3’s July evaluation finished close to Fable 5 overall and slightly ahead on analytical quality. Opus 5 has since taken the overall lead, but the old assumption that useful open-weight models sit far behind the frontier is no longer defensible.
Blind human preference tells the same story on frontend work. Arena’s July 27 WebDev snapshot placed K3 Max at 1,682, ahead of Fable 5 at 1,629 and behind only Opus 5 Max at 1,725.
Fireworks tested K3 and Fable through the same agent harness across approximately 1,030 software, terminal, algorithmic, multilingual, and legal tasks. The models were close on average; an oracle router selected K3 for 72% to 96% of task traffic and reached 93% accuracy overall. The operating majority can go to the cost-optimized model while the premium endpoint becomes the exception path.
The cost structure has changed
Subscription costs add up quickly at company scale.
Claude Enterprise is currently listed at $20 per user per month, billed annually, with usage across Chat, Claude Code, and Cowork charged separately at API rates. At 500 seats, the access layer alone is $120,000 per year before usage.
K3’s list output-token rate is one-third of Fable 5’s and 60% of Opus 5’s. That does not make every K3 workflow cheaper. On AA-Briefcase, K3 averaged $10.57 and 56.4 minutes per task because it used long reasoning traces, 83 turns, and approximately 120,000 output tokens per task.
The economics depend on the work: context reuse, agent duration, latency requirements, concurrency, and tool activity. The correct decision is measured routing and workload-specific capacity planning, not a blanket claim that one model always wins.
What ownership changes
Running AI on company-controlled infrastructure gives the business more control.
Self-hosting is added where data control, continuity, customization, version stability, or sustained demand justifies it.
Company data stays inside the system
In a fully private deployment, prompts, documents, outputs, and information used by automated workflows remain on infrastructure controlled by the client. The outside model provider is not in the data path.
A model vendor cannot switch it off
An owned model cannot be withdrawn because a vendor changes its access policy. The company still needs reliable hardware, backups, and operating support.
You decide when the model changes
The deployed model stays on the tested version until the company approves an upgrade, so workflows and controls do not change without warning.
The system can be adapted to your business
Where the license permits it, the model can be tuned and combined with company information, tools, workflow rules, and purpose-built interfaces.
Adding a user does not add another model license
More users can require more computing capacity, but creating another employee account does not automatically create another model subscription.
You can change models without rebuilding everything
Employee access, permissions, company-data connections, workflows, logs, and controls can stay in place while the underlying model changes.
Vendor access risk is not theoretical. Anthropic suspended Fable 5 and Mythos 5 for all users on June 12, 2026 after new export controls took effect, then restored access after those controls were lifted. An owned model can still suffer an infrastructure failure; it cannot be turned off because the model vendor changes who may use it.
Owned infrastructure
Enterprise AI can run on hardware the company owns.
A July 2026 public MLX demonstration loaded K3—2.78 trillion parameters and 1.42 TiB at MXFP4—across four 512GB M3 Ultra Mac Studios connected through a Thunderbolt 5 mesh.
The reported run used approximately 420.8GB of resident memory per machine, generated about 2 tokens per second for one stream, reached 21.2 aggregate tokens per second across 32 concurrent streams, and drew 559 watts on average with a 977-watt peak for the cluster.
The takeaway is practical: private AI infrastructure can support high-value workflows and persistent agents when it is sized to the work. RVAi Consulting measures demand, concurrency, and response-time requirements before recommending hardware, so clients invest in the capacity they actually need.
What this means for buyers
You do not have to rent every AI workload from a model vendor.
The premium now buys convenience, elasticity, and immediate access to the absolute leading model. It no longer buys exclusive access to enterprise-grade intelligence.
For companies with enough recurring demand, sensitive information, or strategically important workflows, the default can reverse.
Run everyday work on infrastructure you control. Use paid frontier models only where testing shows they are worth it.
Research sources
The argument is tied to current, reviewable evidence.
Benchmarks, prices, licensing terms, and hardware availability change. RVAi revalidates the model and infrastructure set during each assessment.
01Agentic knowledge workK3 on AA-Briefcase
02Current benchmark leaderAA-Briefcase leaderboard
03Blind developer preferenceArena WebDev leaderboard
04Task-level routingFireworks routing study
05Enterprise billingClaude Enterprise pricing
06Model rightsKimi K3 License
07Vendor access interruptionFable 5 redeployment notice
08Four-node Mac deploymentMLX K3 demonstration
Potential integrations
Connect AI to approved systems of record.
RVAi engineers integrations where the client’s systems provide an appropriate technical and security path. Scope is established during assessment and planning.
Productivity
- Microsoft 365
- SharePoint
- Teams
- Slack
Business systems
- ERP systems
- CRM systems
- Finance platforms
- Reporting platforms
Data and 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.
Enterprise AI and dedicated operations
Scope the system around your business.
The initial consultation is complimentary. We confirm the assessment, implementation, infrastructure, and support scope in a written quote before work begins.
Request an enterprise quote