Industries · Telecom
Banao builds and deploys AI across telecom operations — network anomaly detection, churn prediction, contact-centre automation, field-service routing, and billing-dispute resolution — for operators, MVNOs, and broadband providers.
Every system below runs against live OSS/BSS, network telemetry, and CRM data. We hand over deployed systems wired into your stack, not a model sitting in a notebook.
Elisa— network telemetry anomalies flagged and triaged before they reach the call centre.
Each item below has a number attached — a churned subscriber, a truck roll, a disputed invoice, or a missed outage. We start where the cost is already on your P&L.
Models over live network telemetry and alarm streams that separate a real fault from routine noise, so the NOC sees the outage that matters before subscribers start dialling in.
Subscriber-level risk scoring across usage, billing, and care history, surfaced to retention teams with the reason for the risk — not just a score nobody trusts.
Intent routing, call summarisation, and resolution assistance over your IVR and CRM, so agents spend the call solving the problem instead of typing notes after it.
Constraint-based dispatch that matches technician skill, parts, and SLA windows to the next job — cutting truck rolls and the second visit that should have been the first.
Document and usage models that read the invoice, the contract, and the meter together, so a dispute is classified and most of the way resolved before it lands on an agent.
Pattern models over CDRs and provisioning data that catch SIM-box fraud, revenue leakage, and subscription abuse while there is still revenue left to protect.
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The deployments are live; we will not publish a number before it is verified.
Elisa
A European telecom operator ran network alarms through static thresholds — a wall of alerts the NOC learned to ignore. Banao built anomaly models over live telemetry that rank what is actually breaking, wired into the existing alarm pipeline so nothing new had to be installed in the network.
a regional broadband operator
Disputed invoices were read by hand against contracts and usage logs, one agent at a time. Banao deployed a document model that reconciles the invoice, the plan, and the meter on intake — so the agent opens a case that is already categorised with the likely cause attached.
Banao operates a ~300-person engineering company on its own AI products before any client sees them. InterviewGod screens our own hires. Vikaas runs our own demand generation.
A telecom stack punishes systems that only work in a demo — alarms storm, data arrives late, and the call centre never sleeps. Running our own operation on the same kind of AI means the version that reaches your network has already been broken and fixed on ours.
Plenty of vendors will sell an operator a model on principle. We would rather tell you where it won't pay back — it is why network and care leads take our second call.
You have been pitched AI by every network vendor with a logo. We start by proving the cost of the problem, not by quoting a platform.
On-site with your NOC, care, or revenue team. You walk out with a prioritised list of AI opportunities, baseline ROI maths, and a go/no-go per opportunity — yours to keep either way. If you proceed, the Sprint cost is credited against the build.
Data engineering first, then the model. We build the ingestion off your OSS/BSS, telemetry, and CRM as a deliverable, and integrate with the systems your agents and engineers already use.
Deployment with human override at the point of action, plus the dashboards your NOC and care teams open daily. The model keeps improving as each day's calls, tickets, and alarms feed back in.
Our OSS/BSS is years old and half custom. Does that rule us out?
No — it is the normal case. Banao integrates with legacy BSS, mediation layers, and home-grown provisioning over whatever interface exists, including flat-file and batch feeds. The model needs the data signal, not a modern API. We run an integration audit in week one.
We can't expose live network telemetry easily. Can we still start?
Yes. We can begin on historical CDRs, ticket logs, or billing extracts while the live feed is opened up in parallel. Getting access to the data is part of the first two weeks of work, not a precondition you have to solve before calling us.
We tried an analytics platform from a network vendor and it stalled. Why is this different?
Most telecom AI dies on adoption — the NOC keeps trusting its old thresholds and the care team ignores the score. Our delivery includes the workflow change for the team that has to act on the output, treated as a deliverable rather than a slide at the end.
How do we prove ROI before committing budget?
That is what the AI Discovery Sprint produces — fixed price, two weeks, and you keep the ROI model whether or not you continue. Worst case you have a free assessment of where AI pays in your network; best case you have your board business case.
How fast can a system reach production?
A typical path is a 2-week Sprint, a 6–8 week build, and a 4-week production rollout. Banao's ~300-engineer bench means delivery starts in weeks, not the months an internal hire or a network vendor's queue would take.
Bring your worst churn cohort, alarm storm, or billing-dispute backlog. In 45 minutes we'll map the AI opportunity and the ROI maths behind it.
Book a 45-min scoping call
