Muse gives an MSP's engineers 1,500+ hours back every month.
How Netrio used skie.io Muse to enrich, diagnose and route high-volume MSP tickets, and create real-time visibility into service risk. Results measured December 2025.
Netrio is a North American managed service provider headquartered in McKinney, Texas, serving more than 1,000 small and mid-market clients with managed IT, cybersecurity, cloud, connectivity and voice services from offices in Texas, Minnesota, New York, Northern Ireland and India. Formed in 2024 by combining Netrio, SUCCESS Computer Consulting and PCA Technology Group, and expanded in 2025 with the acquisition of Agio, it handles thousands of tickets a month across ServiceNow, ConnectWise and Zoho.
At MSP scale, the cost of every ticket is set less by the fix than by the preparation around it: finding the right context, running the first checks, and getting the work to the right engineer.
The business trigger
Growth was outrunning the hours available for investigation.
Netrio's ticket volume was growing faster than any team could staff for, and every ticket carried preparation work before an engineer could add real value. The opportunity was to give engineers that time back and turn operational data into faster, more consistent action.
Three platforms, one picture
ServiceNow, ConnectWise and Zoho each held part of the context; engineers assembled it by hand before they could act.
Preparation before resolution
Thousands of tickets a month needed context and first diagnostics before expert work could begin.
Routing on experience
Dispatch relied on the people who knew each account best, which made scaling and onboarding slower than Netrio wanted.
Signals worth seeing sooner
Customer sentiment surfaced in reviews rather than in real time; Netrio wanted to spot service risk earlier.
The skie.io approach
Muse prepares each ticket before an engineer begins.
skie.io deployed Muse across Netrio's existing operational platforms rather than replacing them. Engineers retain decision authority; automation gathers the evidence, applies approved logic and preserves operational oversight.
Enrich
Collects the operational context required to understand each ticket from across the service platforms.
Diagnose
Runs automated checks and prepares findings before an engineer opens the ticket.
Route
Prioritizes and dispatches work by context and service level rather than by who happens to know the account.
Measure
Tracks time reclaimed, automation performance and customer service signals continuously, so the operating model improves with the data.
Results
Measured MSP automation at scale.
By December 2025 Netrio had converted automation into measurable capacity, handling more volume without proportional headcount growth.
Business impact
- 1,500+ engineering hours reclaimed every month
- 30 minutes saved per customer review through automated preparation
- Continued growth without proportional headcount expansion
- A path to protecting customer retention through earlier service insight
Operational impact
- 60,000+ tickets automated every month
- 49+ automations live in production across ServiceNow, ConnectWise and Zoho
- Consistent routing by context and SLA across teams and platforms
- Faster onboarding, since dispatch no longer depends on institutional knowledge
Service impact
- Engineers begin with context and diagnostics already prepared
- Real-time customer signals surface dissatisfaction before it becomes churn risk
- Lower cost to serve: routine investigation is automated before engineering time is applied
Ongoing relationship
An operating model that keeps compounding.
The automations are not a one-time deployment. Muse runs inside Netrio's day-to-day operations under skie.io's governance model: approved logic, monitored performance, and engineers in control of every decision that matters.
Governed automation
Approved logic and full operational oversight; engineers keep decision authority.
Measured performance
Time reclaimed, automation performance and customer signals tracked continuously.
Expanding coverage
New ticket types and platforms are added to the same foundation, and the measurement layer shows what each one returns.
What comes next
From ticket volume to AI-orchestrated MSP operations.
Muse connects fragmented service data, automation and performance insight into an operating model that scales without proportional headcount growth. The next step is extending the same model from ticket handling to proactive service management, using the customer signals Muse already surfaces.
Automate the work around every ticket
Turn fragmented service data into faster resolution and scalable capacity.
Muse puts AI agents to work on the high-volume operational processes that consume your teams' capacity, governed from day one.