AI Ticket Analysis and SLA Reporting Case Study

AI ticket analysis and SLA reporting

Turn ticket histories into a report your team can defend

We built a production workflow that extracts support-ticket evidence, calculates SLA performance with fixed rules, uses AI for triage, and generates a prioritized Excel review pack.

Discuss a reporting workflow
SLA review workbookReady for review
CaseStatusPriority
Ticket 1042BreachedReview now
Ticket 1048At riskHigh
Ticket 1051MetRoutine
Review summaryEvent historyRaw data
Complete evidenceTicket fields, actions, timestamps, and status changes
Fixed SLA rulesCompliance results never depend on an AI opinion
Review-ready outputExceptions rise to the top of the Excel workbook

The bottleneck was reconstructing reliable evidence

Reviewing one support ticket was manageable. Reviewing a monthly batch meant rebuilding timelines, checking severity-specific deadlines, finding missed follow-ups, and turning the findings into a consistent customer-facing report.

Rules decide. AI explains.

Each part of the system has a clear responsibility and a safe fallback.

Verified calculations

  • Severity-specific resolution targets
  • Initial-action requirements
  • Contact and no-contact follow-up cycles
  • Elapsed time and missing update windows
  • Met, at-risk, or breached status

Evidence-based triage

  • Concise case summaries
  • Problem categorization
  • Recommended next actions
  • Customer-ready event descriptions
  • Confidence and review cues

From a live ticket to an Excel review pack

  1. 01ExtractRead ticket fields and the complete action history.
  2. 02CalculateNormalize events and apply the SLA clock.
  3. 03TriageGenerate summaries from verified evidence.
  4. 04ValidateReject wording that alters timestamps or events.
  5. 05ReportCreate the prioritized Excel review pack.

Designed to survive real operations

Incomplete records, AI failures, and interrupted browser sessions remain visible and recoverable.

01
Checkpoint recoveryThe batch resumes from its saved position after a browser restart.
02
Failure visibilityFailed tickets remain in the queue instead of disappearing.
03
Deterministic fallbackVerified evidence remains available when generated wording fails validation.
04
Source traceabilityReviewers can move from the summary back to the original timeline.

The result: a clear queue for human review

The team can start with breached, at-risk, and inconsistent cases while retaining the evidence behind every result.

Need a report people can verify?

We can map the evidence, rules, AI boundaries, review process, and final output.

Book a discovery call

Explore our automation consulting services or see how we build production n8n workflows.