AI for the languages no one else builds for.
We design models for underserved languages, sovereign systems with the data, evaluations, and infrastructure that big providers don't build. From Mongolian to Pashto to Swahili, we ship production AI in your language.
Built where
data is scarce.
Every step of the low resource pipeline, from data sourcing in regions with no Common Crawl coverage to evaluation when there are no public benchmarks. We've done it before.
Field Data Collection
Local linguists, native speakers, and regional partners, we source corpora that don't exist online yet.
Cross Lingual Transfer
Bootstrapping from related high resource languages, same family, similar grammar, to compress the data requirement.
Custom Tokenization
Multilingual tokenizers tuned for the script and morphology, Cyrillic, Arabic, Devanagari, Mongol bichig.
Eval Without Benchmarks
We build native speaker evals, covering reasoning, fluency, and cultural fit, when no public benchmark exists.
Sovereign Hosting
In country deployment so weights, training data, and inference logs never cross the border.
Production Voice & Text
Both written and spoken language, STT, TTS, and conversational models tuned to dialect and register.
Jobs frontier models were never built to do.
The big labs optimize for the languages of their revenue. Everyone else inherits models that fragment their script, fumble their grammar, and charge them more for the privilege. These are the six situations where building for the language properly is the whole difference.
National Language Models
The flagship work: a model that treats your language as the first language, not translation target number forty. We build national models end to end, tokenizer designed for the script, corpus constructed with native linguists, evaluation written in the language rather than translated from English, through our custom LLM training practice, and deployed on sovereign infrastructure where the weights, the data, and the capability remain a national asset rather than a licensed dependency.
Serving Customers in the Language They Actually Speak
The commercial case hiding in plain sight: your customers message you in Urdu, Swahili, or Khmer, and your AI answers in stiff, error-prone output that reads like a bad translation, because it is one. We build sales and support conversational AI that is genuinely fluent in your market's language and register, including the code-switched reality of how people actually type, and in WhatsApp-first markets that fluency is the difference between a channel that converts and one that embarrasses the brand.
Education in the Mother Tongue
The learning science is unambiguous: children learn dramatically better in their first language, yet nearly all education AI is built for English. We power EdTech platforms with tutors, grading, and content generation that work in the language of instruction, aligned to national curricula, so the AI tutoring gains the research keeps measuring are not reserved for the languages the labs prioritized.
Documents in Local Scripts and Mixed Languages
Invoices in Urdu with English line items. Contracts in Arabic with Latin-script names. Government archives in scripts standard OCR gives up on. Our document AI pipelines handle multilingual and mixed-script paperwork natively, which for trade-heavy and multilingual-market businesses converts the document pile from an English-only automation into an actual one.
Voice for Languages the Speech APIs Ignore
Commercial speech recognition collapses fast outside its top languages: accents flagged as noise, dialects transcribed as gibberish, and text to speech that no native speaker would call their language. Our speech AI practice builds STT and TTS tuned to your dialect and register, which matters doubly in markets where voice is the primary interface because literacy, script complexity, or habit make typing the exception.
Making the Economics Work in Your Language
The least known problem on this page: commercial models do not just perform worse in low-resource languages, they charge more, because English-centric tokenizers shred other languages into far more tokens per sentence. Fixing that is architectural, custom tokenization and adapted local models, and it converts the ongoing cost curve of every AI system you run in your language. The details are in the second section below, because the numbers deserve their own answer.
The Double Jeopardy, Measured.
Peer-reviewed research put numbers on what speakers of most of the world's languages experience daily: roughly 1.5 billion people face API costs 4 to 6 times higher than English speakers for the same task, purely from tokenizer fragmentation, while receiving measurably worse output. Higher price, lower quality, at once. Every capability on this page exists to break that trade, and it is the most fixable inequity in modern AI, because the fix is engineering, not policy.
The Corpus Is Built, Not Downloaded.
For most languages we work in, there is no dataset to download, and that is the beginning of the method, not the end of the conversation. Cross-lingual transfer from related languages compresses the data requirement, field collection with native linguists builds what the internet never recorded, OCR unlocks physical archives, broadcast transcription captures the spoken register, and validated synthetic generation fills the remaining gaps. Twenty-plus languages shipped means twenty-plus corpora that did not exist before the project started.
Where the corpus doesn't yet exist.
Every step assumes you can't just download a dataset. We build the data, the eval, and the model, in that order.
Linguistic Discovery
Native linguists map dialects, registers, scripts, and the corpus gaps you'll need to fill before training.
Corpus Construction
Field collection, OCR of physical archives, broadcast transcription, and synthetic data generation where needed.
Train & Cross Test
Cross lingual transfer from related languages, then continued pretraining and domain adaptation on the target.
Sovereign Launch
In country deployment, native speaker evals, and ongoing tuning as new data comes in from production.
The tokenizer tax, the timeline, and the business case beyond governments.
Low-resource language AI has a reputation as a philanthropic luxury. The numbers say otherwise: it is an underpriced market with a measurable cost problem that engineering can fix. Here are those numbers.
Because pricing is per token, and tokenizers trained mostly on English shred other languages into fragments. The same sentence that costs one unit in English can cost two to six in a low-resource language, and research estimates 1.5 billion people face 4 to 6 times higher effective API costs as a result, while also getting slower generation and worse quality, since the model reasons over fragmented pieces of words. The fix is structural: a tokenizer designed for your script and morphology, and a model adapted to use it. Clients are routinely surprised that the language project pays for part of itself in token savings alone, before quality gains are even counted.
Three honest tiers. Adapting a strong open model to your language, custom tokenizer, continued pretraining on a constructed corpus, and native evaluation, typically runs $60,000 to $250,000 depending on how much corpus must be built from the field up, and corpus construction, not compute, is usually the dominant line. National-scale programs with from-scratch training, voice, and sovereign deployment run into seven figures and are genuinely national infrastructure. And at the entry level, a focused application in your language, a support assistant, a document pipeline, built on our adapted models, starts far lower, because the language foundation amortizes across everything built on it.
A language adaptation program typically runs 4 to 8 months end to end, and the calendar is dominated by the phase nobody can compress: corpus construction. Linguistic discovery takes weeks, field collection and archive digitization take months, and training is comparatively fast once the data exists. Application projects on top of an already adapted language move at normal software speed, weeks not months. The planning implication is real: if your language matters to your two-year roadmap, the corpus work should start now, because it is the long pole and it cannot be bought at the end.
By building the benchmark, which is a deliverable of every engagement, not a workaround. Public leaderboards do not cover most languages, and translated benchmarks measure translation artifacts, not fluency. We construct native-speaker evaluation suites covering reasoning, fluency, cultural appropriateness, and your actual use cases, scored by native evaluators with inter-rater checks, and the model is judged against that suite before, during, and after training. The eval outlives the project: it becomes your permanent quality gate for every future model, ours or anyone's, which means you will never again have to take a vendor's word for how good their model is in your language.
The government case is real, but the commercial one is quietly stronger and mostly unclaimed. Companies serving multilingual markets are competing with AI that malfunctions for a large share of their customers, in markets where a genuinely fluent assistant, document pipeline, or voice interface is an immediate differentiator precisely because competitors cannot buy one off the shelf. Add the tokenizer economics, running your volume through an adapted local model instead of a mispriced API, and the ROI stops depending on goodwill. The honest filter: if your customers are comfortably served in a top-30 language, you do not need this page. If a meaningful share of them are not, you are underserving them with tools that overcharge you for it.
You do, and for this work the corpus is the crown jewel. Field-collected data, digitized archives, and validated evaluation suites are assets that did not exist before and cannot be re-scraped by a competitor, which for a nation makes them cultural infrastructure and for a company a durable moat. Everything, corpus, tokenizer, weights, evals, and pipelines, lives in your repositories and your jurisdiction, and where data was gathered with linguist partners and communities, the collection agreements are written so your ownership is clean and the contributors were treated properly, because a national language asset built on murky consent is a liability wearing a flag.
Send us three sentences in your language and watch what a frontier tokenizer does to them. That fragmentation is what you are paying for on every API call today, and fixing it is where every engagement here begins.
Questions about
Multilingual & Low Resource AI
For top 30 languages, often yes. For Mongolian, Pashto, Khmer, Hausa, and most of the world's languages, frontier models hallucinate, lose grammar, or refuse, and they aren't sovereign. We build for those gaps.
Mongolian (national scale voice), plus production work across Pashto, Urdu, Arabic dialects, Swahili, and several South Asian and Central Asian languages.
That's the norm for low resource work. We assemble corpora through partnerships with broadcasters, universities, and government archives, often digitizing physical materials and using cross lingual transfer to bootstrap.
Wherever sovereignty requires, usually in country, on infrastructure you own. See our data sovereignty offering for the full architecture.
Both are first class. Real conversations switch languages mid sentence and use dialect that diverges from the standard form. Our models are trained and evaluated on those cases explicitly.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. Tell us your language and use case, we'll tell you what's possible and how we'd build it.
info@croncore.com