Cost & usage
Know which workflows create spend, what they deliver, and where the next budget decision belongs.
An accountable operating partner
Managed AI Operations
An accountable operator for the AI your business depends on.
Begin with the $7,500 Blueprint; ongoing operations starts at $5,000/month.
Quick answer
What is Managed AI Operations?
Managed AI Operations is the ongoing work of keeping business AI useful, reliable, governed, and economically accountable. An outside operator manages model and vendor choices, integrations, cost visibility, evaluation, incidents, and improvements alongside your team. It connects the technology you already pay for to the business workflows, owners, and outcomes it needs to support.
Your provider supplies the model. We own the operating work.
The model is one part of a business system. The decisions around it need an accountable operator.
Models & routing
Match the work to a suitable model. Review changes before they alter the behavior your team relies on.
Governance & control
Give policies an owner, document decisions, and keep access and data handling connected to everyday work.
Reliability & response
Watch execution, review failures, coordinate escalation, and make recovery a repeatable process.
Integration & handoffs
Keep AI connected to your CRM, service desk, business systems, and the people responsible for the next step.
Evaluation & improvement
Review output against agreed criteria and turn operating evidence into a prioritized improvement backlog.
The operating model
A practical path from clarity to continuous improvement.
01 / Understand & improve
Private betaElevated AI GatewayRouting · Budgets · Access · EvidenceOptional control layer. Start with your existing tools.Blueprint
Two weeks to map the environment, baseline cost, and implement one improvement.
$7,500 fixed02 / Operate & optimizeVirtual AI Ops
A named operator, a regular review cadence, and an owned improvement backlog.
Starting at $5,000/month03 / Add control when justifiedManaged Private AI
A tailored deployment when your requirements make private AI the right choice.
Optional · scoped implementationWhat changes when someone owns the operation?
| Operating question | Provider API alone | With an agreed operating service |
|---|---|---|
| Who tracks cost? | Usage billing within that provider account. | Cost attributed to workflows, with a baseline and budget decisions. |
| Who checks quality? | Your team designs and runs its own evaluation. | A defined review process and improvement backlog. |
| Who handles failures? | The provider handles its platform; you own the connected workflow. | An operator coordinates the AI workflow, escalation, and recovery within scope. |
| Who manages change? | Your team assesses model and integration changes. | Changes are reviewed, tested, and coordinated with your IT team or MSP. |
Start with evidence, then choose the operating scope.
Start with a concrete result
AI Operations Blueprint
$7,500 fixed
Two weeks, with one agreed improvement implemented.
Start the $7,500 Blueprint- Current AI environment review
- Usage and cost baseline
- Operating architecture and responsibility map
- One agreed, implemented improvement
- Prioritized next steps and handover
100% of the Blueprint fee is credited toward a qualifying implementation or managed-services agreement.
Fit
Built for teams with real operating work.
- Companies already using AI in production
- Teams with several models, vendors, or connected systems
- Organizations without a dedicated internal AI operations team
A named operating owner. Visible cost, quality, and reliability. A prioritized improvement backlog.
Outside this scope: buying a model subscription alone; a one-off prompt with no operating owner.
Quick answers
What does an AI operator actually manage?
Model and vendor choices, routing, budgets, integration reliability, output evaluation, governance evidence, incident coordination, and the improvement backlog. Scope and responsibility boundaries are agreed with your team.
How is this different from MLOps or AIOps?
MLOps concentrates on the machine-learning development and deployment lifecycle. AIOps commonly means applying AI to IT operations. Managed AI Operations is our business operating service for the AI systems your organization uses, including provider APIs and existing software.
Do we need private AI?
No. Existing provider APIs may be the right architecture. We recommend private AI only when privacy, residency, control, customization, or measured economics justify it.
Can you work with our IT team or MSP?
Yes. Your team or MSP keeps its core IT responsibilities. Elevated AI owns the agreed specialist AI operating work and coordinates changes, incidents, and vendors with them.
Is Gateway required to get started?
No. Elevated AI Gateway is in private beta. Engagements can start with your existing platforms and controls; a gateway is introduced only when it fits the architecture.
The operating journal
Understand the work before you commit.
Make the next step concrete
Put an operator behind your AI.
Start with a two-week Blueprint. Leave with an operating plan and one improvement already in place.