A success story in multilingual writing, cultural nuance, and human-in-the-loop training
Summary
One of the top AI labs in the world wanted their language model to go beyond translation and truly write in Indian languages — with fluency, tone, and cultural accuracy. We made that possible across Hindi, Tamil, Bengali, and more.
Highlights
Trained in over 7 major Indian languages
Native-written examples for formal, informal, instructional, and creative writing
Human-in-the-loop corrections with structured feedback loops
AI learned to draft emails, stories, replies, and summaries — in natural, context-aware ways
Impact
The model now writes clearly and confidently in Indian languages. Whether it’s a formal notice in Hindi or a customer support reply in Tamil, it sounds like a real person wrote it.
Giving AI a Voice in the World’s Most Spoken Languages
A multilingual speech training project across four continents
Summary
Voice AI needs to sound real, not robotic. We helped one of the biggest names in AI build natural-sounding speech models in Spanish, Mandarin, French, Arabic, and Portuguese — tuned for tone, rhythm, clarity, and authenticity.
Highlights
Created voice datasets with phonetically rich and culturally accurate scripts
Selected diverse voice talent across regions, accents, genders, and tones
Studio-quality recordings in controlled environments
Native linguists validated tone, pronunciation, and emotion at every stage
Impact
The model can now deliver customer messages in Arabic, read bedtime stories in Latin American Spanish, and speak Mandarin with native-like tonal control — building trust through voice.
When Code Speaks in Accents
How we helped a model learn to code like developers around the world
Summary
We trained a frontier AI model to understand how developers really code — in their own languages, logic, and teaching styles. From Spain to Korea, from Switzerland to Japan, we helped it think like a local dev.
Highlights
Technical prompts written in native languages, full of real-world phrasing
Native-speaking coders reviewed every AI output for clarity and correctness
Introduced realistic bugs and misunderstandings common in each market
Combined code + math explanations tailored to regional teaching logic
Impact
The model can now walk through a Python function in Japanese, explain async/await in Korean with clarity, or debug C++ in Spanish with culturally familiar structure. This is what cognitive localization looks like.
