TrainAI's hybrid AI workflows delivered real-world chat data at scale
Building authentic multilingual AI training datasets requires more than just technology. It demands cultural nuance, linguistic precision and the kind of expertise that only comes from understanding how people actually communicate.
40%cost savings vs. traditional manual data collection
3language variations managed
Key benefits
Reduced multilingual dataset costs while improving linguistic and cultural accuracy
Scaled synthetic data without losing real-world nuance
Combined AI generation with human expertise to optimize time and cost
Produced authentic conversations with code-mixing, slang, and local tone
Built a reusable pipeline for future multilingual projects
A global AI technology organization set out to build a large-scale, multilingual chat dataset that reflects how people genuinely message each other in India.
The dataset would power the client’s existing conversation summarization feature, which worked well in English but needed to also understand how people in India actually message each other across both Hindi and specifically Indian English.
From the outset, the organization faced an immediate dilemma: Should it invest in authenticity or efficiency? The traditional project approach would mean recruiting hundreds of real users, coordinating across time zones and managing complex group dynamics to capture natural conversations.
There would be potential privacy concerns with this approach. Each participant would need to fully understand how their data would be used, and they’d need to give explicit permission for the team to use it.
Additionally, there would be the risk of inauthenticity. At least some conversations would need to be “staged” to cover specific domains and topics. Staged conversations tend to be much less natural and realistic.
The other alternative was to use human writers to script entire exchanges from scratch. This would come with challenges as well. It risked creating stilted, inauthentic dialogue that wouldn't represent real-world communication patterns. TrainAI by RWS was brought in to find a third path.
Generating natural-sounding synthetic data
The organization needed to create 1,000+ structured conversations spanning 1:1 chats, group messages and community announcements. Each conversation needed to contain realistic timestamps, long message threads, natural code-mixing between Indian English and Hindi (in both Romanized and Devanagari scripts), slang and typos.
Most importantly, the conversations had to include the kind of genuine communication that happens on messaging platforms.
All of this had to be accomplished at scale, while meeting strict structural and linguistic requirements.
Challenges
Create authentic synthetic conversations that genuinely reflect how people message in India
Generate conversations across three language systems (English, Hindi Romanized and Hindi Devanagari) with natural code-mixing
Meet granular structural specifications, including conversation types, message lengths, message types and topic coverage
Ensure linguistic and cultural accuracy without sacrificing efficiency or cost-effectiveness
A hybrid synthetic data generation pipeline powered by large language models (LLMs), combined with human expert linguistic review
Configurable data generation system mapping detailed specifications into precise generation parameters for consistent outputs
In-house language moderators and cultural experts providing deep knowledge of Indian communication norms and authentic code-mixing patterns
An iterative refinement process where native speakers evaluated cultural appropriateness, tone and authenticity before final delivery
Results
Delivered 1,075 multilingual conversations
Achieved 40% cost savings
Created a repeatable and scalable data generation pipeline
Produced a project-ready AI training dataset
Exceeded client expectation
The case for synthetic data
The client initially thought human data collection was the only viable route.
TrainAI suggested combining synthetic data generation with human expert linguistic review. The idea was to use LLMs to generate the foundational conversations, then apply cultural and linguistic expertise to ensure they contain authentically Indian messaging patterns.
The client was skeptical. Generating synthetic chat data that sounded real would require not just technical ability but also deep cultural knowledge. They had already experimented with a leading open LLM but found the conversations felt flat and artificial.
TrainAI assembled a dedicated team, combining prompt engineering expertise with in-house language moderators who deeply understood Indian communication norms. They conducted internal testing of multiple language models, evaluating which could best capture authentic tone, linguistic variety and cultural nuance when paired with human review.
The client got on a call with the TrainAI team, heard the methodology explained in detail and ultimately agreed to try the hybrid approach proposed.
20% one-to-one chats
50% special message types
10 to 100 messages per exchange
Building the synthetic data generation pipeline
Once the client signed off, TrainAI got to work designing a configurable synthetic data generation pipeline. This wasn't simply a matter of prompting an LLM and collecting outputs. The team created a system that mapped the organization's detailed specifications into precise synthetic data generation parameters.
The requirements were granular:
20% one-to-one chats
50% special message types
10 to 100 messages per exchange
Language distribution mattered equally. English appeared throughout, but conversations needed to naturally weave between English dialects as commonly spoken in India, Hindi Romanized (phonetic) and Hindi Devanagari (native script). The code-mixing had to feel organic, not translated.
The organization also needed approximate distributions of other natural elements in the content. For example, it needed approximately 5% of the language to include typos, as well as target levels of slang, acronyms and mentions of specific subjects.
TrainAI's team built the data generation pipeline with these distributions baked in. They defined what percentage of conversations should contain typos, which topics each conversation should address (from weekend plans to fitness routines to community events) and how the special message types should be distributed across the dataset.
The result was a synthetic data generation system that produced conversations matching the client's specifications while maintaining the flexibility to adjust distributions based on feedback.
The human layer that changed everything
This is when cultural expertise became indispensable. After the synthetic data generation phase, every conversation went through linguistic review by TrainAI's language moderators.
These were linguists and cultural experts who could evaluate whether the code-mixing felt authentic, whether the slang was contemporary and appropriate, and whether the tone matched how people actually communicate in different contexts.
The moderators had considerable influence over the final output. They suggested adjusting the mix of conversation types to feel more realistic.
Most conversations passed with targeted edits to tone, slang or code-mixing, and only a small minority needed to be completely rewritten. Moderators refined how code-mixing appeared throughout some of the dialogues, ensuring Hindi phrases didn't feel tacked on but genuinely integrated.
The client's own team initially found this abundance of cultural guidance overwhelming. TrainAI's language moderators explained that authentic Indian conversations needed more flexibility than rigid templates allowed.
With the client's permission, they adjusted distributions and refined tone based on their deep knowledge of how Indians actually communicate across these three language systems.
TrainAI's team proved that synthetic data and human expertise aren't opponents. They're complementary. When combined strategically, they deliver what many AI teams are seeking.
Delivering at scale and speed
The hybrid approach paid off dramatically, delivering 1,075 fully compliant conversations, authentic code-mixing and culturally grounded dialogue.
The conversations even included realistic timestamps. If an initial message said, “I’ll be there in an hour,” the next message could say, “I’m here,” but it would require a timestamp of about an hour later.
The cost savings were substantial. The initial estimate for using actual human contributors would have cost tens of thousands of dollars. The actual cost for the approach involving synthetic data plus linguistic review was about 40% less expensive.
But cost wasn't the real victory. The client received a dataset where conversations felt genuinely Indian.
The language flowed naturally. The code-mixing wasn't forced. The slang felt contemporary. The jokes made sense. And because the dataset was synthetic but rigorously reviewed by cultural experts, it was repeatable, scalable and adaptable for future needs.
On the strength of this project, the client has continued to engage TrainAI for follow‑on work. The organization is even planning to roll the same hybrid approach into additional languages, tuned to each market’s communication norms.
Key learnings
AI-powered synthetic data generation plus human expertise beats either alone
Cultural knowledge drives authenticity in synthetic data
Iterative refinement improves realism far beyond the initial generation.
The broader implication of this story is that anyone with solid prompting skills can build a data generation pipeline. But creating datasets that reflect genuine human communication patterns requires linguistic and cultural expertise.
TrainAI's team proved that synthetic data and human expertise aren't opponents. They're complementary. When combined strategically, they deliver what many AI teams are seeking: authentic, scalable, culturally grounded datasets built faster and at a better cost than traditional approaches.
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