From Searching
Six Tools
to One
Case File.

Dozens

Alarms per fault

One core link, every customer behind it

1

Case file

Every version, every piece of evidence

T1

Overlay

Tier 1 authority. Raises requests.

T2+

Inlay

Authority you set, scaled by risk

0

Tools replaced

Your systems stay the record

The Search.
Before the Work.
Every Single Time.

Telecom operators run on dozens of disconnected tools: monitoring, ticketing, inventory, customer systems. When something breaks, people spend most of their time searching those tools, reconciling records that disagree and assembling evidence before any technical work begins.

A single fault can trigger dozens of alarms. A core link goes down, every customer link behind it alarms, and sometimes a second core link that depends on the first goes down too. Someone has to find the affected customers by hand, attach the evidence, log the ticket and keep customers informed.

Rule-based automation helps only while the data is clean and every system follows the expected format. Real operations data drifts constantly, and when it does, rules fail silently.

AT A GLANCE

The Search Before the Work

When a fault lands, the first job is not fixing it. It is finding out what it touches: which customers sit behind the failed link, which tickets already exist, what the monitoring saw and when it started.

That work is done by hand, across several tools, while customers wait and the SLA clock runs. It is slow, repetitive and error-prone, and it happens on every single incident.

Records That Disagree

Every tool keeps its own copy of the truth. Monitoring, inventory and ticketing each describe the same circuit, the same customer and the same link in their own format, with their own IDs, and they drift apart over time.

So one fault becomes a cascade of similar-looking alarms and duplicate tickets, and the root cause is buried under them. Someone has to decide which record is right, usually in the middle of the incident.

Rules That Fail Silently

Rule-based automation is cheaper and more predictable where data is clean, and we use ordinary code there too. But rules only work while every system follows the expected format.

When one record drifts, rules usually fail without a sound: they stop matching, or they map a customer to the wrong link, and nothing flags it. Inconsistent alarm formats, free-text ticket notes and undocumented dependencies are exactly where rules break, and where AI earns its place.

You buy Overlay, Inlay, or both. Each one does the work of a human role and holds that role's authority, no more. Authority widens only as trust is earned.

Both run on the data layer, which connects to your existing tools without replacing them, and both feed the intelligence store, which keeps what the system learns. You own it.

Every Tool Connected. Every Record Linked.

The data layer connects to the tools you already run (monitoring, ticketing, inventory, customer systems), converts their formats into one consistent shape, and links records that refer to the same thing: a customer link to its core link, an inventory ID to a monitoring ID.

It keeps a synced mirror, a history of what was seen and when, and an audit trail. It takes no actions, and your own systems remain the owners of the data. Connectors, ID normalisation, linking and sync are ordinary, deterministic software. Where rules work, we use rules.

The Tier 1 Work, Done Before Anyone Starts Searching.

Overlay does the work of a tier 1 NOC engineer. It investigates alarms, correlates them to a single root cause, gathers evidence, checks external factors such as weather, identifies which customers are affected, logs and updates tickets, and keeps customers informed. If your monitoring already logged the fault, Overlay enriches that ticket instead of creating a duplicate.

When records conflict, it does not pick a winner. It attaches every version, escalates to tier 2 with the evidence, and holds detailed customer updates until the facts are confirmed, sending only a holding message that carries no uncertain facts. Like a human tier 1, it does not change the network or your systems of record. It raises requests instead.

Deeper Analysis. Fixes Within the Authority You Set.

Inlay takes escalations with a complete case file. It performs the deeper technical analysis, proposes fixes, corrects systems of record such as inventory, and makes network changes only where you have authorised it.

Its permissions depend on two things: the risk of the action and the impact on the customer. An inventory correction is low risk, a service restart medium, a core configuration change high. You set the thresholds. Low-risk actions on non-critical customers can run automatically with a log. Higher-risk actions need one-tap approval, and the highest need a named senior engineer and a rollback plan. Data corrections wait for evidence and pass through an approval queue.

Every Outcome Remembered. Every Lesson Reversible.

The intelligence store records what the system learns: which tool is reliable for which field, how your network's failures tend to cascade, which proposals your engineers approved or rejected, and which fixes actually worked.

Learning comes only from reviewed outcomes. It is versioned, and you can inspect it and roll it back. It is what makes the system better at your environment over time, and because it is built from your incidents, it belongs to you.

The chart compares Entalinity with a manual NOC and with rule-based automation across six things operations teams care about. It shows the shape of the difference, not a benchmark. The pilot measures the real numbers on your incidents.

One Operator.
Measured Results.

We are looking for one operator for a focused pilot: one incident type (for example backhaul or core link failures and their downstream customer impact), with Overlay only.

Overlay gets read access plus tier 1 ticket and customer-update authority. No network changes in the first phase. Targets are agreed with you before we start.