IT operations dashboard showing ticket queues and SLA metrics

Business Operations & Automation

IT Operations Automation for a Cross-Border Business: NXTGEN Industries

NXTGEN Industries was managing IT support through Slack messages and email with no ticketing system, no SLA, and no audit trail. We built a full IT operations infrastructure in 90 days — JIRA ticketing, P1-P4 SLAs, knowledge base, and automated escalation.

Renz Gutierrez Belda

IT Support Specialist / Co-Founder

9 min read

October 14, 2024

Client

NXTGEN Industries

Industry

IT & Business Services (Melbourne, Australia)

Duration

22 months (ongoing retainer)

Engagement

Fractional Leadership

Day One: What the Audit Found

When Renz conducted the initial IT operations audit, the picture was clear: every IT request went to a single Slack channel monitored by one person. There was no ticket numbering, no priority classification, no documented resolution. The same issues recurred month after month because there was no record of how they had been resolved before. The time from a staff member reporting an issue to IT becoming aware of it averaged 4.2 hours — because the channel was noisy and the right person did not always see the message. The IT team was operating entirely on memory and goodwill.

The Infrastructure We Built

Adam established the strategic IT operations framework — team structure, escalation paths, and the SLA tier framework. Renz built and deployed the technical infrastructure. JIRA Service Management was configured with four issue type templates: Hardware, Software, Access, and Network. Each template had mandatory fields appropriate to its category, so every ticket arrived with the information needed to begin resolution. P1 through P4 SLA clocks were configured: P1 at 15-minute response and 4-hour resolution, P2 at 1-hour and 8-hour, P3 at 4-hour and 3-business-day, P4 at 1-business-day response. Automated Slack notifications fired for every new P1 and P2 ticket, tagging the on-call engineer directly.

The Knowledge Base That Changed Everything

Within the first 30 days of the ticketing system being live, Renz analyzed the ticket data and identified the 15 most common issue types. For each one, he wrote a resolution procedure in plain language — not technical documentation, but a step-by-step guide that any competent team member could follow. These 15 articles reduced the average resolution time for known issues from 2.4 hours to 28 minutes. By month 6, the knowledge base had grown to 47 articles covering every documented issue type, and first-contact resolution reached 74%.