Churn signals arrive as fragments
Symptom
Repeated faults, credits, usage drops and complaint themes sit in different systems, with no shared view before a renewal or cancellation decision.
Telecom Intelligence Lab
AI solutions for telecom operators, tested before scale
Tickets, usage records, alarms and capacity data can show where AI may pay off. We help you choose the two or three uses worth testing, then build on your data, systems and brand.
4 minutes · results on screen · no email needed
The challenges we see
Four patterns come up in almost every one of the operators and network providers we work with. If one sounds familiar, that is usually where to start.
Alarm volume hides the incident
Symptom
One equipment or service failure can create a long alarm trail, leaving engineers to decide which event is primary and which are symptoms.
Capacity is measured, not sold
Symptom
Utilisation dashboards show spare capacity by site or time window, but buyer demand, service constraints and price floors live elsewhere.
Inference demand needs an operating model
Symptom
Customers want models closer to users, while site capacity, latency, cost, tenancy and support obligations are not measured together.
Practical AI uses
Four uses we would consider first. Each one runs on data most teams already have.
Use case 01
Alarm correlation and fault triage
- What it does
- Groups time-linked alarms into a candidate incident, ranks likely causes and shows past fixes an engineer can verify.
- Data it needs
- 6–12 months of alarm, incident and ticket history with resolution codes or notes.
- Time to first value
- 10–14 weeks
Use case 02
Churn-risk early warning
- What it does
- Combines service faults, usage changes and complaint themes into a review queue, so retention teams can act before the next billing or renewal decision.
- Data it needs
- Tickets, usage and billing records with stable account IDs and outcome labels where available.
- Time to first value
- 8–12 weeks
Use case 03
Spare-capacity offer preparation
- What it does
- Combines site and time-window utilisation with price floors and service constraints, then drafts offers for a person to approve.
- Data it needs
- Site-level utilisation, current price floors, service constraints and buyer or product rules.
- Time to first value
- 12–16 weeks
Use case 04
Private inference hosting pilot
- What it does
- Serves one approved customer model from selected sites or regions, with latency, cost, usage and availability reported against agreed targets.
- Data it needs
- Site capacity, latency measurements, one model workload, traffic sample and security constraints.
- Time to first value
- 12–16 weeks
How we implement
Four steps, with a decision point after each. You can stop after any of them and keep what was built.
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Diagnose
Review tickets, alarms, usage, capacity and operating constraints with network, customer operations and IT.
You get: a ranked list of use cases with expected value, delivery effort, data gaps and a measurement plan
-
Pilot
Work in one region, domain or customer segment, with a baseline and holdout or before-and-after measure agreed before build.
You get: a working workflow and a go / no-go report
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Integrate
Connect to the ticketing, monitoring or capacity stack, add human review and access controls, and train the teams who use the output.
You get: a production workflow with owners, controls and a runbook
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Scale
Extend to further regions, domains or customer segments only when the measure holds, with your team leading and us supporting.
You get: a rollout playbook your team can own
Expected outcomes
These are estimates, not client results. We confirm or revise each one against your own baseline during the diagnosis.
- Estimate for a scoped alarm-triage pilot. Assumes incidents have usable timestamps and resolution notes, and engineers review the suggested cause using the same baseline process.
- Estimate for one region or network domain. Depends on current weekly triage volume, the hours recorded today and whether the workflow removes manual correlation rather than adding a review step.
- Estimate for a customer-risk pilot, not a guarantee. Assumes retention teams act on the queue, have an appropriate intervention and measure results against a comparable holdout group.
- Estimate based on pilot and integration cost divided by the expected monthly value of measured time savings, avoided service work or retained revenue. The result depends on adoption and the value assigned to each outcome.
Questions network, customer operations and strategy leads ask
Something else on your mind? Ask us directly.
Do we need to centralise our data first?
No. A first project can start with controlled exports or read-only access from the ticketing, billing, monitoring and capacity tools you already use. The diagnosis checks joins, history, permissions and data quality before build.
Will this work with legacy network equipment?
Often, if the equipment exposes usable alarms, logs or counters. We start with what is already available, and the pilot records missing fields and manual steps rather than hiding them.
Who owns the models and the data?
You do. Models, code and data stay in your environment or your cloud account. We don't reuse your data to train anything for other clients.
How much of our team's time does a pilot take?
Plan on about four hours a week from an operations lead, plus a few hours from IT at the start for access, security and deployment decisions. Engineering and data teams join where the scoped use case needs them.
What if the pilot misses its target?
We agree the target and baseline before we start. If the pilot falls short, you get a written explanation of why, and you are not committed to the next phase.
Reviewed by Faruk Karagöl, CTO ·
Find out which use case fits your network
18 questions, about 4 minutes. You see your score and top three gaps before we ask for anything.
