NetSingularity
NetSingularity
Complete lifecycle coverage · Stage 04

Every fault, ticket and SLA breach tracked until the network is whole again.

When something goes wrong, resolution should not depend on who is online or which inbox holds the update. NetSingularity keeps every issue visible, assigned and moving, from first alert to verified close, with the root cause written down while the engineer who found it is still looking at it.

Explore AI capabilitiesSee the resolution loop
NetSingularity issues to resolve lifecycle with incident detection, assignment, SLA tracking, and closure
The resolution reality

Most incidents are not solved slowly.
They are solved twice.

The expensive failure in incident management is not a long MTTR on a hard fault. It is the same fault recurring every month, diagnosed from scratch each time, because the last engineer who fixed it closed the ticket with the word “resolved” and moved on. The knowledge existed. It was never captured, so it never compounded.

Symptom

Tickets logged by hand

A human reads an alarm, decides it matters, and types a ticket. Under load, that step is where incidents quietly go missing.

Symptom

Priority set by whoever is free

Routing follows availability rather than fault type and location, so the right specialist sees it third rather than first.

Symptom

Closure without evidence

No captured cause means no recurrence detection, no vendor accountability, and no answer when the regulator asks what happened.

The resolution loop

First alert to verified close.
And the return path that stops it happening again.

Correlation decides what becomes a ticket, not a person. The SLA clock runs underneath the whole lifecycle rather than being checked at the end. And closure is where the learning is captured, which is the only reason recurrence detection has anything to work with.

Raw alarms, every vendor, every domainCorrelationdedupe · group · trace to causeOne incidentwith its cause and evidence attachedHundreds of symptoms in.One thing to work on out.~60% of alarm noise removed before anyone is pagedno human logs thisAuto-createTicket raised from thecorrelated incident.Zero manual loggingPrioritise & routeBy fault type andlocation, to the teamthat owns itAssign & workNamed owner, visiblestate. Never parkedin somebody’s inboxVerify restoredService state confirmedhealthy. A fix that didnot hold is not a closeClose with causeRCA and resolutionnotes captured at close,not reconstructed laterSLA COUNTDOWN. Running from the moment the incident exists, not from when someone noticedPre-breach alert fires before the clock runs outRecurrence detectionPatterns across repeat incidents surfacedautomatically. The fault you keep paying forpatterns feed correlation and preventive action, so the third occurrence never happensPOST-INCIDENT AUDIT TRAILEvery state change, assignment, action and note retained. For compliance reporting, for vendor accountability, and for thequestion that always comes later: what exactly happened, who decided it, and on what evidence.
Two things in this picture do the work. The funnel at the top means a ticket represents a cause rather than a symptom, so the queue length reflects real problems. The dashed return path at the bottom is the one most tools lack. Closures captured with their cause become recurrence patterns, and recurrence patterns become correlation rules and preventive action. Without capture at close, that return path has nothing to carry.
Issues to resolve

What the stage actually delivers.

Automated ticket creation

From correlated alarms

Tickets are raised from correlated incidents rather than typed by an engineer, so nothing depends on someone being awake and nothing is lost because the shift got busy.

Priority-based assignment

By fault type and location

Routing follows what the fault is and where it is, not who happens to be free, so the specialist who can close it sees it first rather than after two hops.

End-to-end resolution tracking

SLA countdown and breach alerts

The clock starts when the incident exists, runs visibly through every state, and raises a pre-breach alert with time left to act, rather than reporting the breach afterwards.

Root cause captured at close

While the engineer is still looking

Cause and resolution notes are recorded as part of closing, not reconstructed at the post-incident review a fortnight later from three people’s memories.

Recurrence detection

Patterns surfaced automatically

Repeat incidents are grouped and surfaced as a pattern, turning “we fix this every month” from tribal knowledge into a measured cost with an owner.

Post-incident audit trail

Compliance and vendor accountability

A complete record of state changes, decisions and evidence. Usable for regulatory reporting, and usable in the conversation with an OEM whose equipment keeps failing.

Where AI takes over

The loop runs faster when
agents work inside it.

Every stage above can run with humans doing the work and the platform keeping the record. It runs considerably faster when the reasoning and the routine execution are handled by agents, under governance your engineers set, and can withdraw.

Sherlock

Reasoning agent

Delivers root cause across topology, telemetry and change history in under two seconds, with the evidence path attached. That is what makes capture-at-close realistic rather than aspirational.

ProcBot

Action agent

Clears known fault types through pre-approved runbooks inside a defined blast radius, with rollback on every action. Anything without a defined reversal stays advisory.

NOC Copilot

Engineer’s agent

Lets L1 and L2 ask about live network state in plain language and get a sourced answer, instead of assembling one from four dashboards.

Read about AI governed operations
Measured on live estates

What operators got.

Figures from a Tier-1 Indian mobile operator running RAN fault management fully on-premises, across roughly 12.5 million alarms a day.

~60%Alarm noise removed through correlation and deduplication
~50%Faster MTTR across defined incident families
~40%Improvement in L1 and L2 resource efficiency
20 to 40%Fewer incidents through proactive prediction

Under 2 seconds to root cause

Multi-hop fault chain tracing across domains, with every conclusion source-linked and auditable.

100% traceable

Every decision carries a transaction ID, with immutable audit logging and role-based access across the whole lifecycle.

Zero public-LLM dependency

Inference runs on the operator’s own GPUs. No alarm, topology or subscriber detail leaves the perimeter.

Results measured on one operator's estate are not a forecast for yours. The figures worth agreeing before anything is signed are your current alarm-to-incident ratio and your MTTR baseline.

Spanning every stage

Where this hands off.

Resolution quality is decided upstream. A diagnosis is only as good as the topology it reasons over, and that topology was created during rollout.

Worth a conversation?

Bring us last quarter's ticket export.

We will tell you your real alarm-to-incident ratio, how many of your tickets closed with no recorded cause, and which fault families you paid to diagnose more than three times. That last number is usually the one that changes the conversation.

Explore AI capabilities

Worth a conversation
with your team?

Tell us where you're losing the most ground. We'll show you exactly where Netsingularity fits. And where it doesn't. A structured technical walkthrough on your use case, your data patterns.

Share the use case, team context, and email. We'll follow up with a focused walkthrough for your network lifecycle priorities.