Telecom software for support AI, self-service and network analytics

Yes, provided it can reach the right systems. Once connected to billing, CRM and network status, an assistant can verify the customer, explain charges on the latest bill, show remaining data, check for known outages at their address and log a fault with diagnostics attached. Anything outside agreed rules, such as a disputed charge above a set amount or a customer who needs extra support, passes to an agent with the full conversation and account context.

Through a protective API layer that the assistant calls instead of touching core platforms itself. That layer uses the APIs your BSS, OSS and CRM already expose, adds adapters for older interfaces, and caches frequently requested data so a busy support channel does not overload billing or provisioning. The assistant only sees narrow, permission-checked actions, such as reading a balance or booking an engineer, and each one is logged for audit.

Yes, if your OSS and device management platforms expose those actions through an API. The assistant can trigger a line or service test, read the result, check the customer's router through your remote management platform, for example one based on TR-069, and guide the customer through a restart or settings check. Only when those steps fail, or the test points to a network fault, does it offer an engineer appointment, with the diagnostics attached to the job so the visit starts with evidence.

We treat them as high-risk actions with extra checks. A SIM replacement or port-out request requires step-up verification, such as a one-time code to an existing contact method plus knowledge or document checks, and is screened for risk signals like a recent password reset or an unfamiliar device. Suspicious requests pause for an agent to review. Customers are notified on every channel on file, giving them a chance to stop a request they did not make.

Monitoring tools tell engineers what is failing now; analytics shows patterns over time and their effect on customers. By combining alarms, performance counters, trouble tickets and usage data in one warehouse, we can show which sites fail repeatedly, which faults generate the most support contacts, where capacity is running short and how network experience relates to complaints in each area. Anomaly detection models can then flag unusual behaviour before it becomes a wider outage.

Yes, churn models are a common use of the same data. They combine signals such as repeated faults, dropped calls, slow speeds, billing disputes, contract end dates and recent support contacts to score each customer's risk of leaving. The useful part is the reason behind each score, so retention teams can act on the cause, for example fixing a recurring fault rather than only offering a discount. Models are validated on historical churn before anyone acts on them.

We build them offline-first. Job details, site records, checklists and reference documents download to the device before the engineer travels, and everything captured on site, including photos, optical power readings, equipment serial numbers, parts used and customer sign-off, is stored locally and synchronised when a connection returns. Conflicts are resolved by agreed rules, and dispatchers can see when each job last synced, so they know how current the information in front of them is.

Yes, when it is designed for incident-scale demand, which is when it matters most. The assistant and portal run on autoscaling cloud infrastructure, read outage status from a cache instead of querying core systems for every customer, and switch to an outage mode that answers the most common question first with the latest update. Load tests simulate incident traffic before launch, and our SRE engineers set alerts on latency and error rates.