Every workflow below is staffed by language-native workers, validated by AI cross-checks, reviewed by peers, and signed off by an expert QA Lead.
"Preference data that actually understands the user."
Native-speaker preference ranking, response evaluation and constitutional AI feedback across 22+ Indian languages — including code-mixed Hinglish, regional dialects and low-resource scripts.
"The most spoken language online. The most under-trained."
Purpose-built Hindi training data for LLMs — prompts, responses, cultural context, idiomatic usage, and code-mixed Hinglish at scale.
"Automated scores miss the meaning. Humans don't."
Human-in-the-loop quality scoring for Indic machine-translation models. BLEU and COMET are a floor — we provide the ceiling.
"Safety is local. What's fine in SF breaks in Patna."
Adversarial evaluation of LLMs in an Indian context — political, communal, linguistic and cultural pressure tests that global red-team sets systematically miss.
"Accented, noisy, multilingual. Real India, transcribed."
Transcription, speaker diarization, emotion tagging and intent labeling for Indic audio — call-center streams, field recordings, broadcast media and on-device voice.
"Where generalists fail, credentialed experts ship."
Domain-expert annotation and evaluation performed by verified specialists — licensed lawyers, qualified doctors, chartered accountants, CFAs and domain academics.