Solving language blind spots with culturally fluent AI

The mission: Understanding voices beyond language with AI

A client was on the path of developing an AI model that truly understands local language diversity in audible conversations. As the ideal goal, the AI was set to capture critical context and nuance across multilingual audio files, from pitch and tone to pronunciation and local expressions—starting from English, Malay, Mandarin, and Cantonese. To achieve this, the client turned to us to build a team of 90 Malaysian language experts with deep cultural fluency to help the model reach 95% performance accuracy.

The challenge: Capturing nuance demands more than fluency

Traditional approaches to language data often stop at surface-level fluency, but training AI to truly understand local nuance demands talent who can intuitively grasp slang, tone, pitch, context, and social subtleties. Without them, AI models risk sounding tone-deaf, misinterpreting intent, or reinforcing bias especially in sensitive or multilingual environments. But, scouting these culturally fluent experts at scale can be a challenge as they are rare, and their qualifications don’t show up on a typical CV.

Key obstacles:

• Difficulty sourcing native speakers who could authentically express local slang, idioms, tonal shifts and written expressions
• Cantonese presented a unique challenge due to its distinct spoken and written forms
• Standard hiring methods failed to surface candidates with deep cultural context and expressive range
• Evaluating nuance in voice, such as intonation, rhythm, emotion was difficult to screen with traditional assessments

The goal:

Recruit culturally fluent language specialists to train AI models with language data quality that goes beyond the surface.

The solution: High-touch recruitment for the right voices

We activated a high-touch recruitment strategy, shifting to external outreach and targeted networking. Cantonese assessments were redesigned to address both spoken fluency and writing competency.

Our approach:

1. Expanding beyond traditional channels with targeted outreach
2. Developing custom assessments to capture conversational nuance, not just language correctness especially for Cantonese tonal and written complexity
3. Prioritizing expressive accuracy over generic fluency, enabling the model to learn from real, local voice patterns
4. Creating a fast-turnaround pipeline for evaluation, onboarding, and feedback to match the project’s scale and urgency

The results: Precise hires for linguistic depth

• Engaged 92 over 90 language experts to curate datasets and train the language model
• Strengthened talent resources with 700+ candidates waitlisted
• Achieved an average time-to-hire of 14 days

The client now benefits from a robust pipeline of language experts who bring both vocal authenticity and written fluency to the table. Down the pipeline, this team can be instrumental in delivering conversational AI that is culturally attuned to audiences across regions.