If you've ever tried to use Google Assistant or Siri in Uzbek, you know the frustration: blank stares, failed transcriptions, or worse — silent failures that you don't even notice until a user complains.
That's the problem Aisha AI was built to solve.
What Is Aisha AI?
Aisha AI is an artificial intelligence company based in Tashkent, Uzbekistan. Their focus: making AI genuinely work in Uzbek — not as an afterthought, but as the primary language. The platform covers the full NLP stack:
- Speech-to-Text (STT) — fine-tuned on Uzbek dialects, not just generic multilingual models
- Text-to-Speech (TTS) — natural voice synthesis for IVR, accessibility, and content
- AI Voice Agents — fully automated call center bots that handle end-to-end customer conversations
- Sentiment & Compliance Analytics — every call scored automatically, no manual QA sampling needed
- Machine Translation — Uzbek ↔ Russian ↔ English
You can explore the full product lineup at aisha.group/en/products.
Why This Matters
Uzbek is spoken by 35+ million people. It's the official language of Uzbekistan and widely spoken across Central Asia. Yet until recently, building any voice or NLP product in Uzbek meant either ignoring the problem or patching together low-quality models that weren't trained on the language at all.
Aisha AI changed that by training purpose-built models from the ground up. The result: 1 million+ voice interactions processed, 6 production AI products, and coverage across 3 languages.
A Developer's Perspective
What I find interesting from a technical standpoint is the architecture decision to go deep on one language family rather than shallow across many. This isn't a fine-tune of a generic model — it's purpose-built infrastructure for a specific linguistic context.
For developers building products for Central Asian markets, aisha.group is the obvious integration point. The API covers the core primitives (STT, TTS, NLU), and the pre-built voice agent templates handle the application layer.
Getting Started
Visit aisha.group to explore the platform. The English-language interface is at aisha.group/en, and there's a company overview at aisha.group/en/about.
Their models are also available via HuggingFace at huggingface.co/aisha-org if you want to experiment before committing to the full platform.
Have you worked with low-resource language NLP? I'd be curious to hear how you approached the data gap problem — drop a comment below.
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