Telecom · Billing dispute automation
Banao builds billing-dispute automation that classifies incoming disputes, retrieves the relevant usage and payment evidence, and applies resolution decisions — without an agent opening every ticket.
Straightforward cases close automatically. Contested cases reach agents pre-loaded with the full evidence picture, the most similar past decisions, and a recommended outcome. First-contact resolution climbs; average handle time falls.
Dispute automation is not a chatbot on top of your ticketing system. It is classification, evidence retrieval, decision logic, and agent tooling — each piece built for your billing environment.
Every inbound dispute — voice transcript, chat log, or web form — is classified by type, channel, and priority before a human reads it. Roaming overcharge, service-credit claim, and direct-debit error each follow a different evidence path.
The system pulls the subscriber's call detail records, data usage logs, payment history, and prior dispute outcomes in one pass. Agents stop copy-pasting between six screens; the evidence is assembled for them.
Disputes that match established resolution patterns — billing-cycle errors, duplicate charges, promotional-code misapplications — close with a credit applied and a confirmation sent. No agent queues, no lag.
When a case needs judgment, the agent sees a summary of the evidence, the three most similar past decisions, and a recommended outcome with confidence score. They decide; the AI prepares.
Cases that exceed credit thresholds or hit regulatory flags route to the right tier automatically, carrying a complete timeline of every action taken — no re-explanation required.
Agent overrides and supervisor corrections feed back into the classification and recommendation models. The system learns from what your team actually approves, not just from historical data.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Elisa
Elisa's support teams were spending significant agent time re-assembling evidence subscribers had already submitted. Banao built a classification and retrieval layer that pre-loads the evidence picture before the agent picks up the case.
Banao runs a ~300-person engineering operation on its own AI products. Vikaas handles our own demand generation; InterviewGod screens our own engineering hires every week. We are not outside observers describing how AI changes operations — we run it ourselves.
That operating experience shapes how we build dispute automation. We know where classification breaks down, where retrieval gaps appear, and how agent teams actually adopt AI-assisted workflows — because we have lived the same integration cycle internally.
Automation earns its keep at volume and on structured dispute types. We will tell you when the conditions are not right:
We don't quote an automation platform on a description of your problem. We look at your actual dispute data first.
We analyse a sample of your dispute tickets, classify dispute types by volume and resolvability, and map your evidence sources. You get a clear picture of which case types can auto-resolve, what the integration touchpoints are, and what the handle-time reduction is worth. Yours to keep. If you proceed, the Sprint is credited against the build.
Intake classification, evidence retrieval from your billing and CDR systems, auto-resolution logic, agent-assist interface, and escalation routing. Delivered as working software integrated into your support stack.
Live deployment with agent feedback loop, override tracking, and a resolution dashboard for QA and compliance leads. Monthly rule reviews keep the decision logic aligned with current tariffs and promotions.
What types of disputes can the system resolve automatically?
Disputes with deterministic resolution paths — billing-cycle miscalculations, duplicate charges, misapplied promotions, and service-credit claims within a defined threshold. The Discovery Sprint maps your specific dispute taxonomy to what is auto-resolvable before we build anything.
How does it integrate with existing billing and CRM systems?
Banao builds integration adapters for the billing platform, CDR store, and CRM your team already runs. We have worked with incumbent telecom billing stacks including legacy mediation layers. The integration design is agreed in the Discovery Sprint.
What happens when a case needs a human decision?
The system routes it to the right agent tier, pre-loaded with the evidence summary, similar past decisions, and a recommended outcome. The agent decides; the AI reduces the time spent assembling context from minutes to seconds.
How is the model kept accurate as billing rules change?
Resolution rules are maintained as versioned configuration, not hardcoded logic. When a promotion ends or a tariff changes, the relevant rules are updated without a full model retrain. Agent overrides also feed back and are reviewed monthly.
What does the Discovery Sprint deliver if we don't proceed?
A dispute-type taxonomy with volume and resolvability scores, an evidence-source map, a handle-time reduction estimate, and an integration complexity assessment — all documented and yours to keep. Many teams use the taxonomy output to improve manual triage even without building the automation.
In 45 minutes, we can show you which case types are driving your handle time, where auto-resolution is already within reach, and what a realistic reduction in cost-per-dispute looks like.
Book a 45-min scoping call
