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Project context

Telecom Support Triage & Tier-1 Resolution

AI support flow for a telecom operation that classifies incoming tickets, resolves repeatable Tier-1 issues, and hands complex cases to the right human queue with usable context.

Validated conceptTelecom support operation2025

Client identity kept confidential

Optical-fiber telecom equipment threaded into a network rack
38%
Tier-1 resolution
Issues resolved autonomously during validation
Measured
91%
Intent accuracy
Correct classification across tested categories
Measured
87%
Handover quality
Escalations rated complete by internal reviewers
Measured

Evidence

Measured during a focused validation on representative Tier-1 support issues. This was not a live production rollout.

The challenge

The solution

Technology stack

LangGraphAzure OpenAICRM IntegrationSentiment AnalysisPython

The result

The validation showed that a meaningful share of repetitive support volume can be taken out of the frontline queue without breaking the customer experience. The agent resolved 38% of tested Tier-1 issues autonomously, classified intent with 91% accuracy, and produced structured escalation summaries that reviewers rated useful in 87% of handovers.

Commercially, that means human agents spend less time on repetitive traffic and more time on the cases where technical judgment actually matters. It gives the operation a realistic path to phased rollout instead of another support demo that dies after review.

Next step

Want to explore where this could land first in your operation?

Start with one concrete process. In the AI Opportunity Scan we assess where AI adds value and what the best first route looks like.

Telecom Support Triage & Tier-1 Resolution Case Study | Laava