Generative AI for Network Operations
LLM-based assistants for NOC engineers — surfacing insights from millions of events in natural language.
Urgency score — priority vs. other Telecom & Tower use cases. Inference latency requirement 2–15 seconds for production deployment. T3 Optimization priority classification. Cloud acceptable — batch or async workload.
Overview
Domain-adapted language models trained on network event logs, vendor documentation, and escalation history give NOC engineers natural-language query interfaces for complex network troubleshooting. 'Why did site X degrade at 14:32?' answered in seconds instead of minutes of log correlation. Domain-adapted LLM trained on operator event logs, alarm taxonomy, and escalation history. RAG layer over live event stream provides grounded, citation-backed responses. Natural-language interface replaces hours of manual log correlation. Conversational context maintained per engineer session for follow-up queries. Network topology and event data never leave operator infrastructure.
The Penalty Stakes
- General-purpose LLMs have no training data on operator-specific alarm taxonomy — 45–65% incorrect response rate in operator evaluations
- Public LLM APIs require sending network event data externally — proprietary topology and alarm patterns leave operator control
- RAG over live event streams requires low-latency retrieval infrastructure that cloud LLM APIs don't provide
- Token costs on public LLM APIs at NOC scale (10M+ events/day) are prohibitive without private inference
Business Impact
NOC productivity improvement; faster MTTR for complex incidents; analyst time savings
General-purpose LLMs underperform on network operations queries — domain adaptation on operator-specific event taxonomy is required for meaningful accuracy
Infrastructure Requirements
Domain-adapted LLM trained on operator's event logs, alarm taxonomy, and escalation history. RAG layer over live event stream for grounded responses. Deployed on private infrastructure — proprietary network topology never exposed to public APIs. Retrieval: vector search over event log corpus. Generation: domain-adapted LLM with citation of source events. Follow-up: conversational context maintained per engineer session. Token consumption is high — efficient inference silicon required.
- NEXUS Foundry trains domain-adapted LLM on your event taxonomy, vendor docs, and escalation history — not a generic base model
- RAG layer over live event corpus reduces hallucination rate from 45% to under 5%
- Private infrastructure hosting ensures network topology and alarm data never leave operator control
- Efficient inference silicon reduces per-query cost 60–75% versus public LLM API pricing at NOC scale
- NEXUS OS hosts the full RAG pipeline — retrieval, generation, and session context — on your infrastructure
- NEXUS Foundry trains a domain-adapted language model on your event taxonomy, vendor documentation, and escalation history. NEXUS OS hosts it privately — your network topology never trains a shared model or leaves your infrastructure.