AI for operators who serve millions of subscribers in Bangla.
Telecom runs on volume: calls, chats, alarms, offers and retailers. AI can take on much of the routine work, if it is built for Bangla and for the systems you already run.
| Use case | The problem today | What we build |
|---|---|---|
| Bangla customer-care agents | Call centres and chat channels handle very high volumes of routine requests about balances, packages and SIM issues. | Bangla and English assistants that resolve routine requests, look up account context and hand over cleanly to a human agent. |
| Call summaries and quality review | Supervisors can review only a small sample of calls, and agents spend time writing notes after each one. | Automatic transcription, summaries and quality scoring of calls in Bangla, with trends surfaced for supervisors. |
| Churn and next-best offer | Retention offers go out broadly and late, after the subscriber has already decided to leave. | Models that flag subscribers at risk early and recommend the offer most likely to keep them, measured against a control group. |
| Network-operations assistant | Engineers search runbooks, past tickets and alarm histories by hand during incidents. | An assistant that groups related alarms, finds similar past incidents and drafts the likely fix for the on-call engineer. |
| Retail and distribution analytics | Recharge and SIM sales depend on a large retailer network that is hard to see in detail. | Forecasting and anomaly detection on retailer activity, so field teams focus on the outlets that need attention. |
Scale
Systems are designed for high-volume traffic from day one, with cost per conversation measured as carefully as accuracy.
Language
Customers write and speak in Bangla, English and both at once. We test on your real transcripts, not translated benchmarks.
Integration
AI is only useful if it can see the account, the package and the ticket. We integrate with billing, CRM and OSS systems through your existing vendors.
Common questions
Can a Bangla customer-care agent really handle real conversations?
For routine, well-defined requests, yes, provided it is grounded in your account systems and tested on real transcripts. We design every assistant with a clear boundary and fast handover to a human for everything outside it.
Where does subscriber data go?
Wherever your policies allow. We can deploy models on your own infrastructure so call recordings, transcripts and subscriber data never leave it.
How do you prove a churn model is working?
By measuring retention against a control group that did not receive model-driven offers, agreed with your commercial team before launch.
Start with your leadership team.
The Executive AI Workshop gives your leaders a shared view of where AI pays off in your business, what stands in the way, and what to do in the next 90 days.
Request a workshop