OpenFrame

LNC DATA Slashes RMM Costs While Boosting Technician Efficiency by 20%

AI INTEGRATIONCASE STUDYCOST SAVINGSMSPOPENFRAMERMM
LNC DATA LLC

LNC DATA LLC

Healthcare

Vasile Gavrila

Vasile Gavrila

CEO

1-50

Employees

600

Managed Seats

LNC DATA Slashes RMM Costs While Boosting Technician Efficiency by 20%

Summary

LNC DATA found their biggest wins with OpenFrame's AI integration. Their technicians use Mingo to run backend commands and troubleshoot issues faster than ever before, cutting down the back-and-forth that usually eats up support hours. The vulnerability access feature also gives them real-time visibility into security gaps across their healthcare clients - critical when you're dealing with HIPAA-sensitive environments.

Challenge

Like most MSPs, LNC DATA was watching their RMM tool costs climb year after year while getting the same (or less) value in return. Managing 600 endpoints in healthcare means you can't cut corners on tooling, but paying premium prices for proprietary solutions was squeezing their margins. The math just wasn't working anymore, and something had to change.

Solution

The AI became the team's go-to feature almost immediately. Instead of manually digging through logs and running diagnostic steps one by one, technicians use OpenFrame's AI to execute backend commands and troubleshoot issues directly. It's not about fancy automation workflows yet - Vasile's team hasn't even set those up. The value is in the AI doing the heavy lifting on day-to-day troubleshooting, getting to root causes faster without the usual guesswork.

Results

• Time saved: ~8-10 hours per week in reduced troubleshooting time across the team
• Ticket resolution: 30% faster average resolution time on common issues
• Vendor cost reduction: On track for 50-60% reduction in RMM tooling costs compared to previous stack
• Team efficiency: Technicians handling 20% more tickets without adding headcount

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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.
Set a baseline before rollout, then track tickets closed per technician, mean time to resolution, percentage of tickets resolved with no human touch, technician hours reclaimed, and cost per ticket. AI-driven automation commonly cuts operational cost per ticket by 25 to 40%.
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.
Start with telemetry hygiene: full agent coverage, consistent naming, centralized metrics. Then run predictive monitoring alongside existing thresholds until the team trusts it, and add auto-remediation for your most common ticket types. Expect the labor savings to land within a few months, not weeks.
Published industry data shows automated analysis and remediation cutting resolution times 40-60%, MTTR dropping 60% or more in strong first-year deployments, and predictive maintenance lifting uptime 10-20%. Results depend heavily on telemetry quality and how many remediation runbooks you automate.

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