Engineering the shift from peak-time pressure to real-time precision, with Azure OpenAI that scales, sells, and saves.
The business bottleneck
For Asia’s largest telecom, customer support had become a numbers game they couldn’t win. Volume spikes hit hard — millions of users logging billing issues, data-pack renewals, and service complaints all at once.
Language added another layer of friction. Customers reached out in more than a dozen regional languages, but the company’s English-first chatbots simply couldn’t keep up.
On top of that, guidance varied from one channel to another. The business needed a single conversational interface that could scale on demand, understand natural language in every market it served, and deliver accurate, policy-aligned answers.
Why “generic chatbots” fail at telecom scale
- LLMs drift without live plan/tariff data
- English-only models miss regional nuance and intent
- “One-shot” answers don’t handle multi-turn troubleshooting
- Without privacy guardrails, PII becomes a non-starter
So we built for accuracy, freshness, and language from day one.
How we delivered automation in telecom
Our team started where it mattered: the data. We fine-tuned Azure OpenAI GPT-4 Turbo on the telco’s own knowledge — tariffs, KYC policies, device manuals, and care flows... so every answer would sound human but stay policy-correct.
The assistant understands my question in my language and fixes it faster than calling.
A retrieval pipeline kept it sharp with live plan data and 200k knowledge articles, ensuring every answer stayed current.
We gave it a voice in 11 languages, added context memory so it could hold a real conversation, and built in privacy guardrails from day one.
The result: a fast, trusted assistant that speaks every customer’s language and never forgets the rules.
Technologies we used
Generative & Retrieval Layer
Application Layer
Data & Infrastructure
What really moved the needle
We learned that great models start with your own domain, not the internet. Feeding GPT with real telecom knowledge made accuracy real, not lucky. Let's not forget that keeping the knowledge live and connected was just as vital.
Also, we built trust in from the start: citing data sources, enforcing policy checks, and masking PII before every call.
And we kept our eyes on what moves the business: deflection rate, p95 latency, NPS, and conversion — not vanity metrics.
