Case Study: Leading Conglomerate Company achieves rapid, high-quality multilingual Conversational AI training in 40 languages in under 30 weeks with Shaip

Training data to build multi-lingual Conversational AI

Leading Conglomerate Company needed high‑quality, multilingual training data to build Conversational AI but faced challenges sourcing, annotating, and transcribing large volumes of diverse audio across vernaculars. They engaged Shaip to provide audio sourcing, annotation and transcription services tailored to strict accuracy, demographic diversity, and delivery requirements for both 8 kHz and 16 kHz data.

Shaip sourced, annotated, and transcribed over 20,600+ hours of conversational audio across 40 languages and dialects with 3,000+ linguists, delivered transcripts in JSON within 30 weeks (2x–5x faster than competitors), and met the client’s accuracy targets (≥95% word accuracy, ≥90% tag accuracy). The work enabled the Leading Conglomerate Company to train multilingual conversational agents that understand intent across domains, support thousands of use cases, and scale into new geographies quickly.