Use case 01
The expert who is always on shift
From depending on the specialist to institutional knowledge available to the whole team, on every shift, regardless of who opened the watch.
ALM-4821
Router CORE-01 — Interface down
ALM-4822
Switch A01 — Peer unreachable
ALM-4823
Switch A02 — Peer unreachable
ALM-4824 … +37 more
Downstream devices not responding
Where do I start? What is the root cause? I need to escalate to the specialist.
In most operations centers, the most valuable knowledge lives in no system at all. It lives in the heads of two or three people who have been watching that infrastructure for years.
The problem is those people cannot be on every shift. And when they are not, the analyst on watch does what they can: reviews the alarms one by one, tries to remember whether this happened before, and if things get complicated, escalates. The specialist takes the call at 2am with incomplete information. The customer waits.
EVA does not replace the expert — it makes the expert available to the whole team, at all times. Every shift starts with a hypothesis already formed, the context gathered and the knowledge of what worked before built into the recommendation.
The full trajectory
Four moments. One transformed operation.
Noise reduction is the entry point, not the destination. Behind it there is a progression: first the team understands better, then it decides better, then the whole area operates with the same criteria and that improvement becomes visible to the customer.
- 01What is going on, and why?
First moment
Understand what is happening
"I came into the shift, there are 41 active alarms. I don’t know whether it’s a real problem or noise. The expert is not available."
The analyst opens the shift facing a volume of signals that does not distinguish real degradation from the noise of a device that over-reports. Before acting they have to understand, and today that stretch depends on the experience of whoever is on watch.
EVA normalizes the sources, groups what is related, compares against dynamic baselines and turns the result into a readable hypothesis — with the affected service, the assets involved and the topology context already included.
- Assisted diagnosis — from initial noise to a first hypothesis with affected-service context.
- Technical consolidation — a unified view of event, asset, topology and related ticket in one place.
- Report interpretation — extensive data turned into actionable reading, not a table to export.
The analyst does not start from zero. They start where the expert would after their first ten minutes.
- 02What do I do with what I know?
Second moment
Act better on what was understood
"I know there is a problem, but I don’t know whether to start with the router or the switch. Last year this was solved by someone who is no longer on the team."
Understanding is not enough if the next investigation depends on who picked up the case. The root cause engine evaluates alert density and cascade impact over the topology, estimates the probability of the responsible component and ranks the candidates.
The models are retrained on the client’s own resolution history — they learn how that team solves problems, not an industry average. The specialist who used to be the only one who knew where to start now has an equivalent available at 3am.
- Investigation support — a short list of candidates ranked by probability, not a hundred alarms with no hierarchy.
- Decision-making — business impact in plain sight: which customers, which services, which SLA at risk.
- Less repeated investigation — what is already solved and documented stops being investigated from scratch every time it recurs.
Fewer reassignments across tiers, fewer reopenings. Decisions are made with the real impact on the table.
- 03How do we stop depending on the shift?
Third moment
Let the whole team do it, not only the expert
"The new analyst has been here two months and still does not know the infrastructure well. On night shifts he is the only one left."
In most operations, knowledge lives in the people with the most years. Every new hire starts from zero, every shift rotation is a partial loss of context, and the quality of the response varies depending on who signed in.
EVA’s conversational copilot acts as that always-available expert: it explains the incident, queries the topology, suggests the investigation path and remembers what happened the last time something similar was seen. The immutable log records every action — the next shift starts informed, not blind.
- Shorter learning curve — a new analyst operates with backup from their first shift, without needing the expert beside them.
- Standardization and traceability — common classification criteria and a full audit trail of every intervention.
Knowledge stops being individual property. It becomes installed capability of the area.
- 04How does the service feel it?
Fourth moment
Project the improvement toward the customer
"The customer calls asking about their service and we have no clear information to give them. We are still investigating."
The customer does not ask about network topology. They ask about their service: whether it is available, when it will be back, how often this happens. That translation — from technical event to communicable business impact — is done today by a person under pressure, with fragmented information.
By enriching every event with location, customer and service identifiers, EVA means communication no longer gets built from scratch. It is generated with the context already included and arrives before the customer has to ask.
- Faster, more consistent response — less variability in who delivers the information, regardless of shift or experience.
- Foundation for proactive operation — silent degradation detected before impact, while there is still room to intervene.
The operation stops responding to what already failed. The effect shows in MTBF, not only MTTR.
What EVA makes available on every shift
The knowledge that used to live in two people
When the entire team has access to the same level of context and interpretation as the most experienced specialist, the quality of the operation stops depending on that person’s calendar.
| Moment | Without EVA | With EVA |
|---|---|---|
| Shift start | The analyst reviews alarm by alarm looking for the root cause | Starts with a formed hypothesis and the context gathered |
| Knowledge | Lives in the two or three most senior experts on the team | Accessible to the whole team at all times |
| Quality | Varies depending on who is on shift and their experience level | Common criteria and AI backup always available |
| Escalations | The specialist takes the call when things get complicated | The copilot suggests the path before escalation is needed |
| Communication | The customer waits while the team investigates without context | The response is generated with service impact already included |
Let’s talk about your operation
No generic proposal, no filler slide deck — with your real operation on the table.