# What Control Center does

Control Center is the platform MSPs use to scope, price, build and bill AI managed services across the clients they already have. It does nine things.

## Client business context

Every client’s business documented and kept current. Every answer starts from it.

- Techs work every ticket with the client’s business in view.
- No interviews to run and no wiki to keep current.
- New AI opportunities show up on their own.

How it works: Control Center reads each client's apps and maps how their business runs, then every desk in your MSP works from that one record.

[Client business context on the Capabilities page](https://www.resultsiq.ai/capabilities/#c-doc)

## AI managed services

Find, value, price, build and bill AI services for every client.

- AI finds, values, prices, builds and bills each service.
- Your team makes one decision: approve it.
- New recurring revenue on every client, at your margin.

How it works: AI finds the opportunity, values it, prices it, builds it and bills it. The MSP approves it once.

[AI managed services on the Capabilities page](https://www.resultsiq.ai/capabilities/#c-rev)

## Automation

Build a workflow once and run it for every client, with approvals and a run log.

- Build it once and sell it to every client.
- No automation engineer to hire.
- Tested on live data before any client sees it.

How it works: Describe the job in plain English, AI drafts the workflow, it is tested on live data, and every client gets it.

[Automation on the Capabilities page](https://www.resultsiq.ai/capabilities/#c-auto)

## Technology standards

Your standard on every client, checked every morning, with drift flagged.

- Problems get fixed before the client calls.
- Junior engineers deliver like your senior ones.
- Every gap becomes a project you can sell.

How it works: Your standard is checked against every client each morning. Drift becomes a ticket you fix today or a project you sell.

[Technology standards on the Capabilities page](https://www.resultsiq.ai/capabilities/#c-std)

## Reports and QBRs

Reports and QBRs from live data, in each client’s terms, on demand.

- QBRs ready in minutes, not days.
- Every client gets one, every quarter.
- Risks explained in business terms, with the fix priced.

How it works: Live data from every tool and the client's business context become a QBR in their terms. Your vCIO approves it and sells the next project.

[Reports and QBRs on the Capabilities page](https://www.resultsiq.ai/capabilities/#c-rep)

## Roadmaps and budgets

A priced roadmap and budget for every client, refreshed every QBR.

- A 12-month plan and budget for every client.
- Clients see every cost coming.
- AI spend alerts at 80%, and runs never stop.

How it works: Approved work goes on a dated roadmap and a 12-month budget, and AI spend is tracked against the budget you sold.

[Roadmaps and budgets on the Capabilities page](https://www.resultsiq.ai/capabilities/#c-road)

## Billing and AI margin

Every AI run metered and priced, billed through your PSA at your margin.

- AI services bill themselves, with zero labor hours.
- See the margin on every client and every workflow.
- Waste is found and fixed, so your margin grows.

How it works: Every AI run is metered. Your cost plus your margin sets the price, and the line lands on the invoice in your PSA.

[Billing and AI margin on the Capabilities page](https://www.resultsiq.ai/capabilities/#c-cost)

## Control Center MCP

Your team’s AI on every client, every tool and every API.

- Answers about any client, across every tool, in seconds.
- Any answer can become an automation.
- Same permissions as the login, and a person approves every action.

How it works: Your tech asks in Claude or ChatGPT. Control Center MCP checks their role and clients, reaches every tool and answers with the source.

[Control Center MCP in the docs](https://www.resultsiq.ai/docs/mcp/)

## Security and governance

Role-based access, approvals and an audit trail for every AI run.

- Your AI account and keys, and nothing trains on client data.
- You decide exactly what AI can touch.
- An audit trail for every action, ready for any auditor.

How it works: An AI agent asks to act, your policy decides, a person approves anything risky, and every action is logged with its cost.

[Security and governance on the Capabilities page](https://www.resultsiq.ai/capabilities/#c-sec)