Banglalink Launches Bangladesh's First AI Complaint Management System for Faster, More Accurate Support

Banglalink launches Bangladesh's first AI-based complaint management system in telecom

Banglalink has introduced an AI-based Complaint Management System that detects issues, guides customers through registration, and hands off unresolved cases to live agents. It analyzes handset, SIM, and network status to create a self-diagnosis report, giving customers instant clarity before escalation.

More categories are being added soon. The goal: faster resolutions, smarter routing, and consistent accuracy across touchpoints.

How it works

• Device, SIM, and network checks run automatically in the background.
• Customers receive a clear self-diagnosis with next steps.
• If the issue persists, the system prompts a complaint and transfers context to an agent.
• All actions are transparent, reducing back-and-forth and guesswork.

Why this matters for support teams

• Faster triage: Automated diagnostics cut time-to-diagnosis and reduce handle times.
• Higher first-contact resolution: Accurate classification and context-rich handoffs improve outcomes.
• Better customer trust: Real-time visibility and clear guidance reduce anxiety and repeat contacts.
• Agent focus: Routine checks are automated, freeing agents for edge cases and complex recovery.
• Operational insight: Structured data from diagnostics improves root-cause analysis and backlog prioritization.

What you can replicate now

• Build a diagnostic flow for device/SIM/network checks and plug it into your app, web, and IVR.
• Autofill tickets with diagnostic results and attach logs so agents start with context.
• Define confidence thresholds for when to escalate to a human, with a clear path to opt out.
• Start with a few high-volume categories, measure impact, then expand.
• Integrate with your CRM/ITSM so reason codes, tags, and SLAs are consistent across channels.

Metrics to watch

• First Contact Resolution (FCR)
• Average Handle Time (AHT) and time-to-diagnosis
• Self-service completion and deflection rate
• Reopen rate and repeat contact rate
• Customer Effort Score (CES) and NPS/C-SAT

Quality, training, and controls

• Create a feedback loop so agents flag wrong diagnoses and propose better prompts or paths.
• Review AI suggestions with QA sampling; adjust models and workflows based on error patterns.
• Keep help content and troubleshooting trees current; retire failing branches quickly.
• Protect privacy with clear consent and data minimization; align with complaint handling standards like ISO 10002.

What Banglalink says

Taimur Rahman, chief corporate & regulatory affairs officer at Banglalink, noted that the system diagnoses issues and creates complaints in real time, delivering faster, more accurate, and effortless service. He emphasized their focus on customer obsession and ongoing expansion of AI to improve satisfaction and operational efficiency.

Why this sets a new benchmark

Automating diagnostics while preserving a clean handoff to human agents is a practical model for telecom and high-volume support teams. It balances speed with accountability and gives leaders the data they need to fix issues at the source.

Further reading and learning

• Industry perspective on AI in networks: GSMA: Artificial Intelligence
• Upskill your team on practical AI for support and operations: Complete AI Training - Courses by Job