By admin 
Your NOC is drowning in false alarms? Critical incidents hide behind thousands of duplicate tickets. As a result, your engineers are burned out from clicking through noise all night.
Here’s how you can improve your telecommunication software by cutting your alert volume by 70% and start sleeping again.
Most people think a Network Operations Center is just a fancy help desk. It’s not.
We monitor servers, routers, network links, and applications 24/7. We patch systems, manage firewalls, analyze bandwidth usage, and fix problems before users even notice them. When monitoring tools detect issues, we create alerts, categorize them, and dig into root causes.
That’s the theory. Reality looks different:
- 10,000+ alerts per day from a mid-sized ISP network
- One misconfigured threshold generates 50 duplicate tickets
- Single device failure triggers 6 separate alerts
- Manual triage becomes endless whack-a-mole
Here’s what happens when your team processes thousands of low-priority alarms every shift:
| Problem | Impact | Our Experience |
| Duplicate alerts | Wasted time | 30% of daily tickets were duplicates |
| False positives | Missed critical issues | Nearly missed a major outage buried in noise |
| Manual sorting | Slow response times | MTTR averaged 40 minutes |
| Constant interruptions | Engineer burnout | 60% team turnover in one year |
After installing an AIOps platform to correlate events and suppress duplicates, our alert noise dropped 30% overnight.
Large language models can now read ticket descriptions, correlate them with documentation, and suggest likely root causes. They act like a junior engineer who never sleeps – answering “How do I fix this?” at 2 AM when your documentation is scattered across wikis and PDFs.
But there’s a massive problem.
LLMs don’t know facts. They predict the next word based on training patterns. When information is missing, they make stuff up. This isn’t a bug – it’s how they work.
I tested this firsthand. I asked an AI tool about an obscure RF impairment affecting our microwave links. It confidently explained routing protocol behaviors instead. The answer sounded authoritative but was completely wrong.
In casual conversation, hallucinations are annoying. In network operations, they’re dangerous:
- Wrong router reboot because the AI mixed up vendor models.
- Incorrect cable assignments based on outdated documentation.
- Bad configuration commands that could break production systems.
Here’s the six-step approach that eliminated 90% of our AI hallucinations:
Consolidate alarms from every monitoring tool into a single schema. Use AIOps to correlate related events and suppress obvious duplicates.
Before cleanup:
- Cisco router: “Interface down”
- SNMP monitor: “Link failure detected”
- Bandwidth tool: “Traffic dropped to zero”
After correlation:
- Single alert: “GigE0/1 interface failure on Router-NYC-01”
Create a searchable repository containing:
- Network runbooks and procedures.
- Vendor documentation and firmware guides.
- Network diagrams and topology maps.
- Historical incident reports and resolutions.
Quality beats quantity. One accurate runbook is worth ten outdated wiki pages.
Instead of letting the AI guess answers, make it look up information first.
How RAG works:
- Convert the alert description into a search vector.
- Pull relevant documentation snippets from your knowledge base.
- Use those snippets as context for the AI response.
- Generate answers based on your actual data, not training assumptions.
Every AI recommendation must include documentation references. This builds trust and makes verification simple.
Bad response: “Try restarting the BGP process.”
Good response: “Based on Cisco troubleshooting guide v2.4, section 3.2: Restart BGP process using ‘clear ip bgp *’ command. This resolves 80% of neighbor state issues.”
Treat AI like a copilot, not an autopilot:
- AI proposes classification and remediation steps.
- Engineer reviews and approves before execution.
- Gradually automate low-risk incidents.
- Keep complex cases human-handled.
Build a test suite using real historical alerts and known outcomes:
| Metric | Before AI | After RAG Implementation |
| False positives | 25% | 5% |
| Auto-resolution rate | 30% | 40% |
| Mean time to resolution | 40 minutes | 8 minutes |
| SLA violations | Monthly | Quarterly |
Data quality is everything. Garbage documentation produces garbage recommendations. Invest time in cleaning and structuring your knowledge base first.
Start with safe bets. Pilot on non-critical alerts like interface utilization warnings. Measure performance before expanding to critical systems.
Transparency builds trust. When the AI cites specific documentation, engineers can verify and correct suggestions easily.
Security considerations:
- Don’t expose sensitive configurations in shared knowledge bases.
- Use role-based access controls.
- Anonymize customer data in training examples.
Compliance requirements:
- Document every change to your triage system.
- Ensure AI recommendations don’t violate regulatory policies.
- Maintain audit trails for all automated actions.
- Chasing full autonomy too early. Fully autonomous NOCs don’t exist yet. Focus on augmenting human capabilities, not replacing them.
- Ignoring edge cases. Your knowledge base needs to handle unusual scenarios, not just common problems.
- Skipping validation. Test the system extensively before trusting it with critical alerts.
Alert fatigue kills productivity and burns out good engineers. Generative AI can help, but only when grounded in real documentation and human oversight.
Start by cleaning your alert data and building a solid knowledge base. Implement RAG to eliminate hallucinations. Measure improvements and adjust accordingly.