Horizontal LLM Training Data for Foundation Model

Client: A leading North American AI research lab building a multilingual foundation model
Industry: Artificial Intelligence / Foundation Models Services Used: Horizontal LLM Data, Data Curation, Multilingual NLP

Challenge The client was training a next-generation foundation model intended to match frontier LLM performance across 40+ languages. Existing open-source datasets were heavily English-biased, missing the linguistic diversity and cultural context required for genuine multilingual capability. The client needed a massive, ethically sourced, human-reviewed corpus spanning high-resource and low-resource languages, delivered on a compressed 5-month timeline to hit a model training window.

Traditional data vendors could not scale across the required language mix, and synthetic data alternatives introduced quality and bias risks the research team could not accept for a foundation model release.

Lifewood Solution Lifewood deployed a distributed sourcing and curation operation across 12 delivery centers in Africa, Southeast Asia, and Latin America, leveraging native-speaker teams for each target language. The engagement included ethical sourcing protocols, multi-stage human review, toxicity and bias filtering, and continuous quality sampling aligned to the client's evaluation rubric. Data was delivered in rolling weekly batches to feed ongoing training runs rather than as a single endpoint delivery.

Results

• 2.1 billion tokens delivered across 42 languages

• 97.3% quality acceptance rate on client-side evaluation

• 5-month timeline met with zero milestone slippage

• 18 low-resource languages added to the client's training mix for the first time

• 40% reduction in downstream toxicity benchmarks vs. the client's previous model

Representative Testimonial "Lifewood delivered multilingual data at a scale and quality ceiling we hadn't seen from any other vendor. The low-resource language coverage alone reshaped what our model could do." — VP of AI Research, client organization

Tags: LLM Training Data, Multilingual AI, Foundation Models, Data Curation, Low-Resource Languages