Building the multilingual data LLMs learn from, and correcting what the machine gets wrong.

LLM Language Ops is DesiCrew's language-data practice. It builds, transcribes, translates and corrects the data that trains large language models, across 22+ Indian languages and into Southeast Asia, for AI data companies, research institutes and government programs.

An LLM is only as good in a language as the data behind it, and for most languages that data is thin, missing, or machine-made and wrong. DesiCrew's linguists build what is missing and correct what the machine gets wrong. So a model can work in Kannada, Tamil or Indonesian the way it already works in English.

01 — The Mandate

DesiCrew runs the human work behind multilingual AI, collecting speech, transcribing and translating it, building training corpora, and correcting what the machine produces. The clients range from AI data companies to research institutes to government AI bodies.

• Scale. 36,000+ hours of transcription, 7,000+ hours of speech collected, 900,000+ words translated, and 1.1 million+ words of LLM training data built.
• Coverage. 22+ Indian languages, and increasingly Southeast Asian languages like Indonesian, for clients across India, the US and Singapore.
• Lines of work. Speech data collection and transcription, translation and post-editing, LLM training data, machine-transcription QC and transliteration, prompt creation and response collection, and multimodal image-and-text correction.
• Sized to need. Projects run from short three-month builds to ongoing multi-phase programs, mobilized fast, including ad hoc work where the client's strategy shifts mid-project.

02 — The Challenge

Getting words on the page is the easy part. Getting them right in a language the model was never taught is the hard part.

Most LLMs are trained in English and a handful of high-resource languages. For everything else the data is either missing or machine-made and full of errors. A model trained on bad Hindi transcription learns bad Hindi. A prompt-response pair collected from the wrong speakers teaches the model the wrong thing. Fixing this is human, language-by-language work, and it decides how well the model performs.

• The machine's output needs correcting. Machine transcription and machine translation get a lot wrong. Someone fluent has to correct and tag it before it is fed back, or the model just learns the errors.
• The data has to be collected first. For many languages there is no corpus. Speech has to be collected from real, diverse speakers, transcribed, segmented and delivered in a usable format.
• Who speaks matters as much as what. For a Southeast Asian LLM, the participant list had to include both migrants and non-migrants, because whose voice trains the model shapes what the model understands.
• Every domain is different. Multimodal correction spans images and prompts from every domain, so the team has to research each one before it can judge whether the machine's response is right.

Anatomy of one language task — why a model cannot check itself

One task can be a machine-transcribed Hindi sentence to correct and tag, a Kannada speech file to segment and transcribe into JSON, a Tamil prompt to write and record with the right speaker, or an image caption to fix against its guideline, across 22+ languages and several countries.

A model can produce the output. It cannot tell you the output is wrong. Whether a transcription matches the audio, whether a translation carries the meaning, whether a prompt was answered by the right kind of speaker, these are judgments only a fluent human can make. DesiCrew's linguists make them, and the model trains on what they approve.

03 — The Approach

DesiCrew builds the data that is missing, corrects what the machine gets wrong, and mobilizes the right linguists fast.

• Collect and transcribe. Speech data collection across 10+ Indian languages, transcribed, segmented and delivered in structured formats like JSON.
• Correct the machine. Machine-transcription QC, correction and tagging, and machine-translation post-editing, so cleaned data can be re-fed to the model for accuracy.
• Build LLM training data. Large-scale training-corpus creation, up to 900,000 words per language, framed to the client's guidelines and approved on quality.
• Create and collect prompts. Prompt creation and response collection with diverse, screened participants, for LLMs in Indian and Southeast Asian languages.
• Handle multimodal. Image-and-text caption correction against machine responses across many domains, researched case by case before the call is made.

04 — The Outcome

• Machine output, corrected and re-fed. 150 hours of Hindi transcription corrected and 120 more reviewed for a Seattle AI data company, cleaned and returned so the system could retrain on it — so the model relearns from correct data, not from its own mistakes.
• Corpora built in the languages research needs. Around 200 hours of Kannada and Oriya speech collected and transcribed for an IIT-Madras open-source initiative, segmented and delivered in JSON, won through a competitive tender — so an open-source Indian-language program has real speech data to train on.
• LLM training data, built to guideline. 900,000 words per language built across four South Indian languages for a research lab at IIT-M Research Park, approved on quality, as phase one of the model work — so a research model gets accurate output in languages the big models handle badly.
• New-language LLMs, started from scratch. Prompt-and-response data collected in Tamil and Indonesian for a Singapore government AI body building an LLM across five Southeast Asian languages — so a national AI program gets a foundation in languages the big models skip.
• Machine responses, corrected against the guideline. Around 150 image-and-text tasks corrected across domains, each researched before the call, so the machine's response was judged right or wrong against the prompt and image — so a multimodal model learns to correct itself toward better output.

05 — Why the relationship holds

The work spans AI data companies, research institutes and government AI bodies, across India, the US and Singapore. What holds across all of them is that the language layer is human, and DesiCrew has the linguists to do it, mobilized fast when the work is ad hoc or the strategy shifts mid-project.

• The moat is the range. Speech collection, transcription, translation, LLM corpora, prompt work, machine-output correction and multimodal, across 22+ Indian languages and into Southeast Asia, is not something a single-service vendor can cover. Clients come for one and stay for the rest.

It is also built to be trusted. Delivered to research and tender standards, in structured formats, with quality signed off by the client. For programs that publish or answer to a government, that matters as much as the volume.

About DesiCrew

DesiCrew is an applied-intelligence company — the human and technology layer that makes AI systems and enterprise operations work reliably and grow at scale, refined in production since 2007.