OpenFrame

Anevry Cuts Script Deployment Time and Boosts Team Efficiency with OpenFrame

AI AGENTSCASE STUDYMSPNINJAONEOPENFRAMERMM
A

Anevry, Inc.

Government

Dave Haertel

Dave Haertel

Partner, Senior System Administrator

1-50

Employees

75

Managed Seats

Anevry Cuts Script Deployment Time and Boosts Team Efficiency with OpenFrame

Summary

Anevry switched from NinjaOneRMM and immediately noticed better performance across the board. Script deployment is faster, remote access is more reliable, and there's actually a clear roadmap of where the product is heading. They're also starting to roll out the AI agent to client devices - something their previous RMM didn't offer at all.

Challenge

Script deployment through their previous RMM was painfully slow, and when things broke, there was no accountability from the vendor. Mac support was a constant headache too. Getting remote access to work with Apple's security requirements meant jumping through hoops every time. For a small team, these friction points added up fast.

Solution

OpenFrame delivered where NinjaOne fell short. Script deployment and remote access are now significantly quicker and more efficient. But the real game changer is the AI agent, something their old RMM simply didn't have. For a lean operation managing 75 endpoints, having AI handle first-line troubleshooting means the team can focus on higher-value work.

Results

• Time saved: ~5 minutes per encounter
• Tasks automated: Script deployment, agent updates
• Team impact: Immediate efficiency gains - task completion time dropped and overall satisfaction improved
• Other wins: Feature set that actually exceeds their previous commercial solution

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Frequently Asked Questions

MSPs use AI to triage and route tickets, cut alert noise, schedule patches, assist L1 security work, and draft client reports. Kaseya's 2025 benchmark found 30% already use it to eliminate tedious tasks, with ticket triage the most common starting point.
Most MSPs start with AI features inside their existing PSA, RMM, and ticketing systems rather than standalone products. Common categories include AI ticket triage, alert correlation, scripting assistants, and AI-native all-in-one platforms like OpenFrame that run intelligence across the whole stack.
Start with a readiness assessment, not a tool purchase. Confirm your ticket history is clean and your RMM, PSA, and monitoring systems connect. Then pick one high-volume, low-risk workflow, usually ticket triage, and pilot it on internal tickets before any client sees it.
Automate high-volume, low-risk tasks first. Ticket triage and alert noise reduction top the list because they run constantly and a human still resolves the underlying issue. Save security approvals, billing changes, and client-facing actions for later, always with a human in the loop.
An AI agent for an MSP is software that reads a ticket, decides the action, performs it across your tools, and records the result without a technician driving each step. It differs from a chatbot or copilot by taking action, not just suggesting one.
Yes, for low-risk categories. MSPs report 10% to 25% of tickets closed without a tech opening them, covering password resets, MFA enrollment, and known installs. Anything needing judgment or touching production data still escalates to a human.
Common platforms include Thread for triage, Rewst and Power Automate for workflow automation, NeoAgent for L1 resolution, and ConnectWise zofiQ inside its PSA. OpenFrame runs agents natively inside an all-in-one platform rather than bolting them onto separate tools.
Deployment data on five-person service desks shows $78,000 to $130,000 in annual direct labor savings, roughly 30% fewer escalations, and 15% to 20% better SLA compliance. Savings come from reclaimed capacity, not headcount cuts.
No. AI absorbs queue triage and repetitive fixes, but novel failures, judgment calls like production failovers, and client communication stay human. Technicians shift from clearing alert queues to reviewing exceptions, project work, and higher-value client engineering.
They are safe when scoped. Run a six to ten week shadow-mode pilot, automate only proven low-risk categories, and keep L2 and L3 human. Agents that fail are usually the ones turned loose on the full queue with no review period.

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Deploy it. Love it.

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