Multilingual support bots
Assistants that answer in Chinese, English, Arabic and other languages from one self-hosted model, with retrieval over your help content.
AI platforms
Qwen development.
Qwen is an open-weight model family with strong multilingual coverage and a range of sizes, including coding-focused variants. We use it for self-hosted assistants where several languages matter and where a smaller model needs to punch above its weight.
A short call. Your first build, a fixed quote, and the timeline. No obligation.
What we build with Qwen
Where it fits.
Code assistance
Internal code review and generation helpers running on Qwen coding variants, kept inside your network.
Small-model task workers
Tuned smaller checkpoints that handle tagging, routing and extraction at high volume on modest GPUs.
Vision-language tasks
Reading charts, receipts and screenshots with the vision-capable variants served through an OpenAI-compatible endpoint.
When it is the right choice
Choose Qwen when
- →Your users write in several languages, especially including Asian languages.
- →You want a range of model sizes to match different hardware budgets.
- →You need self-hosting with an OpenAI-compatible interface for easy swapping.
Worth knowing
Honest trade-offs.
- →Licence terms differ between sizes and releases, so we check them for the exact checkpoint.
- →Some deployments have policy requirements about model origin, which we raise early rather than late.
Often paired with
vLLMPyTorchHugging FaceDockerKubernetesPostgreSQL
Related services
Services that use it.
Proof
Work built by the same team.
Closeum
13+ connected modules from calls through commissions
13+connected modules
5role types
32completed transactions
40+leads managed
SIIMS
Orders, production, warehouse, and dispatch on the same records
4operational roles
37backend tests passed
17hosted workflow tests passed
Ledgerline
603 tests passed in a dated financial-control verification
SAR 760Kcash reconciled end to end
603tests passed
14control findings fixed
3finance sources in one ledger
Common questions
Before you start.
How does it compare with other open models?
It depends on your task and languages. We run a small head-to-head test on your own examples and choose on evidence.
Can we call it like the OpenAI API?
Yes. Serving engines such as vLLM expose an OpenAI-compatible endpoint, so application code can switch between hosted and self-hosted models with little change.
Is a fine-tune needed for our language?
Often not. The base models cover many languages well, and we only tune when testing shows a gap in your domain vocabulary or output format.
What do you build with Qwen?
Self-hosted models, Multilingual assistants, Fine tuning, Cost-efficient inference, and related work. We choose Qwen when it is the right fit for the product, the team and the budget.
Can you work with our existing Qwen code?
Yes. We can review what you have, fix what is fragile, and extend it, or build alongside your team.
Keep exploring
More than Qwen.
Where we apply it
The Audit
Get my free AuditKnow what to build first.
Book a short call and show us one workflow. You leave with what we would build first, a fixed quote, and what should wait.