AI for telecom in India

Telecom operators and ISPs in India support subscriber bases large enough that even small per-query costs compound fast, and much of that support is repetitive text: billing questions, plan queries, outage updates. unoblox gives telecom teams one rupee-billed, OpenAI-compatible endpoint for GPT, Claude, DeepSeek, Qwen, Llama and more, so support and ops tooling can be built on rupee economics that scale with usage.

Where telecom and ISP teams use a language API

The practical patterns are high-volume drafting: answering common billing and plan questions in a support chatbot, drafting outage or maintenance notifications, summarising network-operations tickets and logs for an on-call engineer, summarising call-centre transcripts for quality review, and generating boilerplate scripts for OSS/BSS automation tasks. unoblox is the inference layer here — it doesn't touch your billing system, network equipment or OSS/BSS platform directly; your engineers wire its output into those existing systems and keep a person in charge of anything that changes a subscriber's account or bill.

Subscriber-scale economics

At the scale of a national subscriber base, the difference between models is a real cost line, not a rounding error — a fraction of a rupee per reply adds up fast across millions of monthly interactions, so most teams default to a cheap model and escalate only the harder queries.

Model	₹ / 1M input	₹ / 1M output	Typical use
qwen/qwen3-1.7b	0 (free)	0 (free)	Free pilot for a billing FAQ bot
deepseek-ai/deepseek-v4-flash	9.07	18.14	High-volume subscriber support replies
qwen/qwen3-235b-a22b-instruct-2507	9.07	55.44	Multilingual support at scale
moonshotai/kimi-k2.7-code	68.5	342.7	OSS/BSS automation scripting help

Every model's live rate is on /models; figures above are indicative.

Regional languages across a national subscriber base

A telecom operator's subscribers span every major Indian language, and there's no dedicated "telecom language" model to reach for — use a general multilingual model such as qwen/qwen3-235b-a22b-instruct-2507, meta-llama/llama-4-scout-17b-16e-instruct, or openai/gpt-5, and have support leads spot-check translated replies for accuracy before they go into a live chatbot flow.

A sample call

curl https://api.unoblox.ai/v1/chat/completions \
  -H "Authorization: Bearer ub-gw-xxxxxxxxxxxxxxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/deepseek-v4-flash",
    "messages": [{"role": "user", "content": "Draft a short outage notification for affected subscribers."}]
  }'

Frequently asked questions

Can this replace our OSS/BSS or billing systems? No. It's a language API for drafting and summarising text; your existing systems remain the source of truth for billing and network state.

Can it support subscribers in regional Indian languages? Yes, using a general multilingual model — review sample replies before enabling a language in a live chatbot.

Is subscriber data stored in India? Only for the free qwen/qwen3-1.7b model. Other models run on their original providers' infrastructure outside India.

What's the cheapest way to run millions of support replies? Route high-volume, low-complexity replies to deepseek-ai/deepseek-v4-flash or the free qwen/qwen3-1.7b model, and reserve stronger models for harder queries.

Is there a free tier to pilot before rolling out to all subscribers? Yes, qwen/qwen3-1.7b is free to call.

How does GST invoicing work at telecom scale? One monthly GST invoice in rupees regardless of volume, with input tax credit claimable, covering every model used.

Get started in rupees → https://unoblox.ai/sign-in