Every few weeks a request lands in my inbox that begins, more or less: "We're expanding into Central Asia — can you handle the Turkmen, Kazakh, and Uzbek versions?" The phrasing is innocent, but it carries an assumption that costs agencies real money and credibility: that these three are interchangeable pieces of one regional puzzle, with comparable tooling, comparable turnaround, and comparable risk.
They are not. After fifteen years working in and around Turkmen, I'd argue that the single most useful thing a PM can internalize is that "Central Asia" is a geography, not a language tier. Treat it as one and you will misprice the job, misjudge the schedule, and — most damaging — apply the wrong quality assumptions to the language that can least afford them.
The resource gap is not a rounding error
Kazakh and Uzbek have had a genuinely good few years. Both have benefited from national-scale language-technology efforts — purpose-built large language models, growing parallel corpora, and active academic and state investment in NLP. When you run Kazakh or Uzbek through a modern engine, you're standing on a foundation that has been deliberately built up.
Turkmen is a different story, and it's important to be honest about it. It remains one of the lowest-resourced of the major Turkic languages for localization purposes. There's no flagship national LLM doing for Turkmen what comparable projects have done for its neighbors. General-purpose machine translation handles it unevenly — fine for the gist of a paragraph, unreliable the moment you need register, terminology consistency, or anything legally or technically load-bearing. The training data simply isn't there at the volume that produces fluent, idiomatic output.
What this means in practice is that the post-editing-from-MT workflow many agencies now treat as default behaves completely differently across the three. For Uzbek, light post-editing can be a reasonable starting point on the right content. For Turkmen, machine output often needs so much intervention that calling it "editing" is a polite fiction — you're frequently re-translating while fighting the residue of a wrong first draft. The cleaner, faster, and often cheaper path is human translation from the source. I say that as someone with no romantic attachment to doing things by hand; it's just what the data quality forces.
Script and convention are not shared either
The other place the "one region" assumption breaks is orthography. The instinct to handle script questions once, regionally, is exactly backwards.
Kazakhstan has spent years working through a transition from Cyrillic to a Latin alphabet, with the official forms revised more than once along the way. Uzbek has lived in a long, messy coexistence of Latin and Cyrillic, where the "correct" script genuinely depends on your audience, the platform, and sometimes the age of the reader. Turkmen, meanwhile, settled on its own Latin alphabet back in the 1990s — but it is its own alphabet, with letters and diacritics that don't map onto the Turkish or Azerbaijani Latin sets people reach for by reflex, and certainly not onto whatever the Kazakh standard happens to be this year.
The operational consequences are concrete. Font coverage, character encoding, sorting order, input methods, and even your QA regex for "valid characters" differ per language. A localization engineer who configures one Central Asian pipeline and clones it for the others will ship mojibake or silently dropped diacritics — and because few people on the team read these languages, nobody catches it until a client in-country does. I've been brought in to clean up exactly this kind of damage, and it's always more expensive after the fact than a five-minute conversation at kickoff would have been.
How to scope it like it's three projects
The fix isn't complicated; it's just discipline. A few habits that save everyone grief:
- Split the line items. Price, schedule, and resource Turkmen separately. The sourcing pool of qualified Turkmen linguists is genuinely small, and availability — not rate alone — drives the timeline. Build that into the plan rather than discovering it on day three.
- Question the MT default per language. Ask your vendor honestly where machine translation helps and where it hurts on this content for this language. Don't let a single regional MT policy override that. For Turkmen technical, legal, or marketing copy, human-first is usually the right call, and a good vendor will tell you so.
- Lock orthography decisions early. For each language, confirm the target script and the specific alphabet standard before a single string is translated, and verify your tooling renders the full character set. This is a kickoff question, not a delivery-day surprise.
- Build and reuse termbases, because the engines won't. Where MT is weak, a maintained glossary and translation memory carry disproportionate weight. For a low-resource language, your TM is the institutional knowledge; treat it as an asset, not an afterthought.
- Plan for in-country review on Turkmen specifically. With thinner tooling and fewer reviewers, a second qualified pair of eyes catches what no automated check will.
None of this is exotic. It's the same rigor you'd apply to any language pair — the point is simply to apply it three times instead of once. The agencies that win repeat business in this region are the ones that understand the differences before the client has to explain them. Turkmen rewards attention precisely because so few people give it any. That's the whole opportunity.