05
Platform / Operate

Proactive operations
powered by AI

Monitor your entire infrastructure from a single pane. AI detects anomalies, predicts failures, and recommends actions before issues impact service — turning reactive operations into proactive intelligence.

85%

Fewer outages

60%

Faster resolution

24/7

AI monitoring

05
Capabilities

Intelligent infrastructure
operations

SPEC

From anomaly detection to automated remediation — AI manages your operational complexity.

01
Monitoring

Real-Time Monitoring

Every sensor, every device, every connection point monitored continuously. AI correlates signals across your entire network.

02
Anomaly

Anomaly Detection

AI learns normal patterns and detects deviations before they become outages. Early warning gives you time to act.

03
Prediction

Failure Prediction

Machine learning models predict equipment failures days or weeks in advance based on sensor trends and historical patterns.

04
Remediation

Automated Remediation

When issues arise, AI generates remediation plans with crew assignments, material requirements, and estimated resolution times.

4.D

Operational Memory

SPEC

Deep context about your operations

Jax agents learn from your natural language instructions about your unique processes and edge cases. E.g. “reserve 20% spare fiber capacity on all feeder cables by default.”

codex.memory / rules
>

“Reserve 20% spare fiber capacity on all feeder cables by default”

Parse indicatorparsed → structured rule
scope:feeder_cable.*
constraint:spare_capacity 0.20
priority:default
origin:operator instructionverified
Active across 847 feeder segments
12 rules loaded
4.D₂

Codex.Memory

Every deployment makes
the next one smarter.

Field observations, OTDR measurements, crew performance, and anomalies flow into a persistent memory layer. Patterns crystallize into learnings that shape future network designs automatically.

01
Field Stream
09:14Splice loss CL-007: 0.08 dB (within budget)
09:21Cable bend radius violation detected at pole #42
09:33Rocky terrain — trenching depth reduced to 40cm
09:47Feeder attenuation 0.22 dB/km — nominal
10:02Zone B completions 18% faster than forecast
10:15Duct congestion at main road crossing — rerouted
10:28Main line pressure drop 0.4 bar at node W-14 — leak flagged
10:41Transformer load at 91% — automatic load-shedding triggered
Processing
02
memory.learnings
94

Optimal splice window: early morning

327 deployments

87

Rocky terrain: use micro-trenching

89 deployments

91

Reserve 20% feeder capacity in growth zones

156 deployments

78

Pole route > underground where frost depth > 1.2m

43 deployments

82

Crew pairs outperform larger teams on drops

214 deployments

829 deployments analyzed · 5 active learnings · 94% peak confidence · Always improving.

05
How It Works

Monitor, detect,
predict, resolve

Step 01

Monitor

Continuous ingestion of sensor data, device telemetry, and environmental signals across your infrastructure.

Step 02

Detect

AI correlates signals across data sources to identify anomalies and emerging patterns.

Step 03

Resolve

Automated remediation plans deploy with crew assignments and material logistics.

Ready for proactive
infrastructure operations?

See how Nexma Operate transforms reactive maintenance into AI-powered proactive operations.