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Stay ahead of the network you run.

TelaiSys™ reads the SCADA, OSS/NMS, power, and fiber systems your team already runs, and turns what those systems already know into a forecast, not just an alarm.

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30–60%

Fewer network outages.

42,000-site Tier-1 MNO
20–40%

Fewer truck rolls.

Sub-second ingestion

Sub-second ingestion for critical alarms over OPC UA, MQTT/Sparkplug B, and Kafka.

10–25%

Edge energy saved, by shifting load against measured risk rather than habit.

  • Read-only in Phase 1
  • SOC 2 Type II
  • ISO 27001 controls

* Expected values

The problem

All telemetry,
no foresight.

Something is always failing somewhere, and no single system says so. A battery drifts on one dashboard, a cabinet runs warm on another, a utility feed flickers on a third. Nothing connects the three.

So the failure arrives as an alarm, after the fact. Root cause becomes a hunt across five consoles, and a truck rolls on a guess. During a regional event, the alarm that mattered is buried under thousands that did not.

Your team reads those consoles well. The infrastructure was never wired to speak as one.

The reframe

You never lacked the data. You lacked the one place it connects.

With and without

The same network,
read two ways.

How it goes today
With TelaiSys™
Where the data sits
SCADA, OSS, power, fiber, and tickets each hold one piece of the same site. Nothing connects them.
One model reads SCADA, OSS, power, environment, and fiber together, and holds each site's state in one place.
When you find out
The first sign of trouble is the outage itself, then a manual hunt for why.
A live failure probability and time window per site, hours to days ahead.
How alarms arrive
Thousands fire an hour during a regional event. Operators tune out.
Alarms cluster under their root cause, ranked by severity, with the evidence attached.
Why a truck rolls
Dispatches for issues a remote diagnosis could have closed, the largest controllable opex line.
Tickets carry cause, fix, parts, and skill required before the dispatch decision is made.
How power runs
Generators run longer than they need to, batteries cycle conservatively, and the waste compounds across sites.
Load-shift recommendations checked against SLA and thermal limits, automatically.

The forecast

Your team sees it coming.

Five domains, one model. Every site carries a live failure probability and a time window.

Site 8921
0%failure probability, 12–18 hours out

Signature: battery SOH decline, cabinet thermal drift, micro-outages on the utility feed. Three systems, one correlation.

Lead time: 14 hours before the outage

Battery, thermal, power, and network data built that forecast together. No single system could have.

When prediction is not enough

And when an alarm fires,
the fix arrives with it.

No forecast catches everything. When an alarm fires, the model traces the chain — HVAC to temperature to RF throttle to KPI drop — and names the cause, not the loudest symptom. Related alarms cluster under it, and the fix reaches ServiceNow inside the ticket.

Site down → one ticket, not a scramble
Probable causeClosest historical matchFixParts likely neededTechnician skill required

For everything else, ask the copilot. “Which sites are at risk from tonight's storm” gets an answer backed by telemetry, not a guess.

Get started

The forecast is ready
when you are.

The next failure arrives as a forecast, or as an outage report. The data that decides which is already in your systems. It has just never spoken as one.

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