Telecom · Call-centre automation
Telecom call centres absorb enormous query volume — outage status, billing clarification, plan change, SIM swap — from callers who want a fast answer, not a queue. Handle time climbs because agents juggle system tabs and scripted flows while the caller waits.
Banao builds and deploys the AI layer between the inbound call and the agent: a voice model that resolves tier-1 fully, an agent-assist layer that surfaces the right context during live calls, and post-call automation that closes tickets without manual entry.
Each item targets a measurable cost line — contact volume, handle time, after-call work, or audit cost — not a generic AI capability.
A voice model trained on your actual call transcripts handles outage status, billing summary, plan information, and SIM swap without transferring to an agent. Callers get a fast answer; agents take the calls that genuinely need them.
During a live call, the assist layer pulls billing history, open tickets, network status, and next-best-action suggestions from your CRM and BSS in real time — so the agent reads one screen instead of three systems.
After the call ends, the model writes the summary, tags the intent, and raises the ticket in your CRM. After-call work that typically takes three to five minutes drops to seconds.
When a subscriber calls during a known outage in their area, the IVR detects the match and plays a status update before the caller waits. It reduces queue length on the calls where the answer is already in the NOC.
Random-sample QA misses nine out of ten calls. A model that scores every call for tone, compliance, and resolution flags the ones that need a supervisor review — without requiring a team to listen to thousands of recordings.
Metrics shown dotted are being finalised in our case-study metrics pack — published only once verified.
Elisa
Inbound volume was dominated by a small set of recurring query types. Banao deployed a voice model for tier-1 deflection and an agent-assist layer wired into the live CRM and billing BSS, so agents handling escalated calls arrive at the conversation with context already loaded.
Banao runs a ~300-person engineering company on its own AI products. InterviewGod screens every engineering hire; Vikaas handles our own demand generation. A system that has to survive our internal operation is already tested on a real business before it touches your call centre.
That is a different standard than a model trained on a benchmark dataset. We know what it costs when the AI gives the wrong answer to a subscriber at 11pm — because we have had to live with that feedback loop ourselves.
Not every call centre benefits from automation, and we will say so before you spend:
We do not quote call-centre automation from a slide deck. We listen to your actual call recordings and read your BSS data first.
We analyse a sample of your call transcripts, map your top-10 intent categories, and test whether a deflection model is feasible on your real data. You get a deflection estimate, a build scope, and ROI maths — yours to keep. If you proceed, the Sprint fee is credited against the build.
Train the voice model on your transcripts and intents, build the agent-assist overlay wired to your CRM and BSS, and build the post-call summarization pipeline. Integration into your IVR and ticketing system is part of the deliverable.
Live deployment with supervisor dashboards, agent feedback loops, and quality-scoring at full volume. The model retrains on new call data so deflection rates hold as your product mix shifts.
Does call-centre AI replace agents or work alongside them?
Both, depending on the call type. Tier-1 automation — outage status, billing summary, plan changes — resolves without an agent. Agent-assist works alongside the human for every escalated call, so agents handle the complexity and the AI handles the data retrieval. Banao designs the split based on your actual call mix, not a generic rule.
How does the voice model handle regional accents and multiple languages?
We train and fine-tune on your own call recordings, which already contain the accent and language distribution your centre handles. Where specific languages need dedicated models, we build per-language. The Discovery Sprint identifies which languages account for meaningful volume before we invest in each.
What does tier-1 deflection realistically achieve?
For a typical telecom centre where outage status, billing clarification, and plan queries make up 40–60% of inbound volume, a well-tuned model can deflect a meaningful share without agent transfer. The Discovery Sprint gives you an estimate grounded in your own transcript data, not an industry average.
How does it integrate with our existing IVR, CRM, and BSS?
Integration is part of the build deliverable. Banao connects to standard telecom IVR platforms, CRM systems, and BSS via API. Where your systems have unusual interfaces or older integration protocols, the Discovery Sprint surfaces this so integration effort is in the quote, not a change order.
How long before something is live in production?
A typical path is a 2-week Discovery Sprint, a 6–8 week build, and a 2–3 week rollout with QA and supervisor sign-off. Banao's ~300-engineer bench means the build can begin within days of Sprint completion — not after a months-long contracting and staffing cycle.
In 45 minutes we can tell you which query types are automatable on your actual transcript data, what deflection is realistic, and what the build would cost.
Book a 45-min scoping call
