We built an intelligent chatbot using Azure OpenAI to handle massive customer inquiries for Asia’s largest telecom group, providing instant, personalized support at scale.
1. The Challenge
Asia’s largest mobile network was fielding tens of millions of billing, recharge, and service requests every month. Pain points were clear:
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Volume overload: even tier-1 contact-centre staffing struggled during peaks.
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Multilingual demand: customers speak a dozen regional languages; English-first bots failed to engage.
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Inconsistent recommendations: upsell and troubleshooting scripts varied by channel, hurting NPS.
The goal was a single conversational interface that could scale elastically, understand context, and surface personalised offers.
2. Our Solution
| Layer | What We Delivered | Business Benefit |
|---|---|---|
| Generative AI core | Azure OpenAI GPT – 4 Turbo, fine-tuned on telecom FAQs, plan catalogues, and troubleshooting flows. | Human-like answers with policy-level accuracy. |
| Retrieval-Augmented Generation (RAG) | Vector search over 200 k knowledge-base articles and real-time plan data. | Always-current responses; no model drift. |
| Multilingual & Voice | On-device speech-to-text/text-to-speech; 11 Indian languages plus English. | Inclusive support across regions and literacy levels. |
| Context Threads & History | Per-topic threads, resumable sessions. | Cleaner dialogs; higher first-contact resolution. |
| Troubleshooting Workflows | Guided diagnostics scripted as decision trees, surfaced by the LLM. | Device and network issues solved without agent escalation. |
3. Implementation Timeline
| Phase | Duration | Key Outputs |
|---|---|---|
| Discovery & Corpus Prep | 4 wks | Intent taxonomy, 2 TB data ingestion on Azure Blob |
| Model Fine-Tuning & RAG | 8 wks | v1 model (BLEU + 18 %), citation pipeline |
| Mobile & Web SDKs | 6 wks | React Native & JS widgets, voice interface |
| Pilot Roll-out | 4 wks | 2 M users, 87 % auto-resolution |
| Nation-wide Launch | 4 wks | Traffic ramp to 19 M chats/month |
Zero downtime for existing customer apps—the chatbot landed as a seamless update via the carrier’s app stores.
4. Impact
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19 million self-service conversations every month: agent queues shrank, and wait times dropped by two-thirds.
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Cost per contact down 55 %, driven by automation and shorter dialogues.
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Upsell conversion up 12 %: the bot suggests context-aware data packs and 5G upgrades at the right moment.
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Higher CSAT: regional-language users reported the largest satisfaction jump (+0.9).
“The assistant understands my question in my language and fixes it faster than calling.”
— Customer feedback, post-launch survey
5. Why It Worked
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Domain-specific fine-tuning: model trained on tariffs, KYC rules, and device manuals, not generic internet text.
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Live knowledge retrieval: plan changes hit the bot within minutes, eliminating outdated advice.
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Voice-first design: speech interface meets hands-free, lower-literacy, and accessibility needs.
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Privacy guardrails: PII masked before LLM calls; logs stored in the telco’s Azure tenancy.
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