Jaze FiberMap
The fault is found before the calls start.
FiberMap's AI engine reads NMS, ACS, BSS and AAA events against its own end-to-end model of the network. A cut or a degrading span is identified, placed on the fiber route, and turned into a ticket that already carries its location.
NMS, ACS and AAA events correlated against the network model
Affected
—
Location
—
Ticket
—
How a fault travels
From the first event to a technician on site.
Detection, classification and dispatch happen in one sequence, so the manual triage step between a network event and a technician disappears.
Signals collected
NMS, ACS, BSS and packet flow data aggregated continuously.
Events correlated
The AI engine reads them against the mapped network.
Issue classified
Outage, fiber fault, CPE or configuration issue.
Root cause located
The span or device at fault, placed on the fiber route.
Technician assigned
Dispatched with the cut location or the part to replace.
Resolution
The first site visit starts from a diagnosis.
Capabilities
Detect, locate and act.
AI fault analysis
A diagnosis, not a description
- Events correlated across multiple data sources at once
- Root cause identified rather than symptoms listed
- Actionable diagnostic insight returned to the support team
- Predictive analysis and continuous machine learning
Fiber cut detection
Which span, and where along it
- Cuts identified against the mapped fiber route
- The affected core link named, not just the area
- Subscribers who are down listed with the fault
- Outages visualised on the map for faster rectification
Proactive maintenance
Before the complaint arrives
- Anomalies and fault states identified as they emerge
- Equipment parameters and power levels watched over time
- Degrading segments surfaced ahead of failure
- Network uptime and reliability improved
Integrations
Where it meets your support workflow.
IVR flow
The call already knows
- The subscriber identified the moment they call
- Network status analysed in real time during the call
- Issues resolved automatically where that is possible
- A ready-to-act summary passed to the support agent
Chatbot
A complaint that starts a diagnostic
- Chat connected directly to the AI engine
- Real-time diagnostics triggered by the conversation
- Corrective action started rather than a note taken
- Ticket flow automated from the first message
Helpdesk
Tickets that arrive analysed
- Incoming tickets classified by fault type on arrival
- Root cause and diagnostic insight attached to the ticket
- Field technician allocated automatically
- Fault location or faulty equipment included in the assignment
Find the cut before the phone rings.
A walkthrough of AI fault detection against your own network data sources and support workflow.