Multimodal, multilingual training data across 22+ Indian languages — annotated, evaluated, and defended batch by batch. Sourced responsibly. Measured per language.
Bias, hallucination and thin multilingual coverage are data problems before they are model problems. Our annotation, evaluation and RLHF work turns raw material into training data you can trust — across text, image, audio and video, in 22+ Indian languages.
Great AI starts with great data.
Ours comes with a paper trail.
Years
Specialists
Words processed
Hours of transcription
Words translated
Speech data collection
LLM-trained words
A model can be fluent in Hindi and still be wrong in it. Register slips — it addresses an elder the way it would address a friend. Code-mixing breaks it: half the sentence is English, written in Devanagari or in Roman script, and both are correct. "Tamil" on a spec sheet is several production realities that share a name and little else.
These failures don't look like failures. They look fluent. Which is why they survive an eval run and reach your users.
Hinglish, Tanglish and Roman-script input treated as first-class data, not as noise to be cleaned out.
Formality, honorifics and who-is-speaking-to-whom specified in the guideline before labelling starts.
Disputes go to a named native-speaker adjudicator. The ruling is written back into the guideline, so it's settled once.
Unicode normalisation, conjunct handling and transliteration variants checked at validation, not assumed.
Accuracy in language work isn't a number you publish. It's a process that has to hold on the four-hundredth batch as well as the first.
Edge cases, register rules and script conventions agreed before labelling starts — not discovered in QA.
Three levels on every pipeline, native speakers at each.
Your benchmark, run continuously through the engagement rather than at the end of it.
Every dialect and register dispute has a named decider and a written ruling that updates the guideline.
Speech, translation and preference work are each scored on their own instrument. One blended number would hide the language that's slipping.
Language data is collected from people. That makes provenance a delivery question, not a compliance afterthought.
DPDPA-aligned consent and provenance records maintained throughout the data lifecycle.
Age, gender, region and dialect balanced to the spec — so the corpus doesn't quietly inherit the bias you hired us to remove.
The people doing this work are on our team and trained on your guideline. It's why the guideline holds from batch one to batch four hundred, and why the person who adjudicated a dialect call in March is still here in November.
ISO 27001 for information security and ISO 9001:2015 for quality.
LLM & language operations
36,000+ hours of transcription, 7,000+ hours of speech collected and 1.1 million+ words of LLM training data built across 22+ Indian languages and into Southeast Asia — and the machine's output corrected before it's fed back.
So a model can work in Kannada, Tamil or Indonesian the way it already works in English.
LLM & language operations
Since 2019 we've been the data partner to the IIT Madras lab building open-source AI for Indian languages — transcription, translation and corpus work across 22 languages.
So a model built in India can answer in the language the question was asked in.
Text, image, audio and video. Transcription, translation and MTPE, ASR benchmarking, and locale-true content work.
Bias identification, hallucination testing, fact-checking, and benchmarking against golden sets you already trust.
Preference ranking, response rating and structured human feedback at production volume.
Consent-managed collection with demographic controls, for the languages where the corpus doesn't exist yet.
Working across technology, healthcare and life sciences, research, financial services, retail, and media.
Send us a language and a golden set.
Pick a language, a task type, and a benchmark you already trust. We'll run a scoped pilot against it and show you the per-language numbers — not a blended one. You'll hear back within two business days.
