AI for logistics in India

Logistics and courier operations in India run on a constant stream of text: e-way bills, delivery exceptions, customer status queries, dispatch notes. unoblox puts every major language model — GPT, Claude, DeepSeek, Qwen, Llama and more — behind one OpenAI-compatible endpoint billed in rupees, so a logistics team can add drafting, summarisation and multilingual support to its stack without opening a foreign-currency account.

The document and message load logistics teams handle

A mid-sized logistics operation generates exception notes when a delivery is delayed, customer queries about shipment status, dispatch summaries for warehouse handoffs, and driver or vendor communication — often in more than one language. Language models are a natural fit for drafting replies, summarising exception notes for a supervisor, and turning a warehouse log into a readable daily summary, with a human confirming anything customer-facing.

Text extraction versus scanned documents

If the source is already text — a typed manifest, an emailed exception note, a system-generated e-way bill — any general-purpose model can summarise or extract fields from it directly. If the source is a photo or scan, such as a handwritten delivery challan, that needs a vision-capable model; on unoblox, qwen/qwen3.8-27b is the open-weight model with vision input, so route image-based extraction there rather than a text-only model.

Picking a model by task and volume

Model	₹ / 1M input	₹ / 1M output	Typical use
qwen/qwen3-1.7b	0 (free)	0 (free)	Free pilot for a status-update bot
deepseek-ai/deepseek-v4-flash	9.07	18.14	High-volume status message drafting
meta-llama/llama-4-scout-17b-16e-instruct	10.08	30.24	Fast exception-note summarisation
qwen/qwen3.8-27b	16.32	48.96	Vision — reading scanned or photographed documents

Other models, including the rest of the OpenAI and Anthropic lines, are on unoblox too — see live rates on /models before choosing.

A quick integration example

curl https://api.unoblox.ai/v1/chat/completions \
  -H "Authorization: Bearer ub-gw-xxxxxxxxxxxxxxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-4-scout-17b-16e-instruct",
    "messages": [{"role": "user", "content": "Summarise this delivery exception note for a supervisor."}]
  }'

The same key and base URL work for every model in the catalogue, so a pipeline can start on a cheap model and move specific steps to a stronger one without re-integrating.

Being direct about data and control

unoblox does not optimise routes, manage fleets or replace a transport-management system — it is a language API that your own systems call for drafting, summarising and translating text. And except for the free, unoblox-hosted qwen/qwen3-1.7b model, the other models here run on their original providers' infrastructure rather than inside India, so the India-specific benefit is rupee billing, a GST invoice and one endpoint — not data residency.

Frequently asked questions

Can this read a photo of a delivery challan or GST document? Only with a vision-capable model such as qwen/qwen3.8-27b, which accepts image input. Text-only models need the content already as text.

Does this replace our route-optimisation or TMS software? No. It's a language API for drafting and summarising text; routing and fleet decisions stay with your existing systems.

Can it draft delivery updates in regional languages? Yes, using a general multilingual model — review translations before sending to customers, especially for place names and timing details.

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

Is there a free tier to test before paying? Yes, qwen/qwen3-1.7b is free to call for prototyping.

How does GST invoicing work across multiple depots or entities? Usage across every model is combined into one monthly GST invoice, with input tax credit claimable under normal conditions.

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