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Type B — Horizontal LLM Data

Type B —
Horizontal LLM Data

Horizontal LLM data is the broad, domain-general corpus a foundation model trains on — collection, annotation and model testing across every modality rather than one industry. Lifewood builds it across 50+ languages and 40+ delivery centers under a 95%+ accuracy SLA.

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Voice content spans 6 project types and 9 data domains across 23 countries

25,400 valid hours of annotated multilingual speech data for large language model training

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TARGET

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Collection
Target

Capture and transcribe recordings from native speakers from 23 different countries (Netherlands, Spain, Norway, France, Germany, Poland, Russia, Italy, Japan, South Korea, Mexico, UAE, Saudi Arabia, Egypt, etc.). Voice content involves 6 project types and 9 data domains. A total of 25,400 valid hours durations.

23 Countries6 Project Types9 Data Domains25,400 Valid Hours

Horizontal LLM data, answered

What is horizontal LLM data?

Horizontal LLM data is the domain-general corpus a foundation model trains on — broad rather than specialised, covering every modality and many languages instead of one industry deeply. It is what gives a model general competence, and it is bought when the goal is a capable base rather than expertise in a single field.

How does horizontal data differ from vertical data?

Breadth versus depth, and they fail differently. A model trained only on horizontal data is fluent everywhere and authoritative nowhere; one trained only on vertical data is expert in its field and brittle outside it. Most production programmes buy both, with horizontal establishing the base and vertical specialising it.

What does a multimodal dataset include?

Text, image, audio, video and LiDAR — with the difficulty being consistency across them rather than any one modality. Each has its own tooling, annotator skill profile and failure modes, so a single quality standard has to be enforced across all five. Lifewood applies the same 95%+ accuracy SLA and dual-layer review to every modality, across 50+ languages and 40+ delivery centers.

Frequently asked questions

More than most buyers expect, and the constraint is usually quality rather than volume. Public datasets are typically single-pass and ungraded, which is why frontier programmes commission graded corpora instead: prompt-response pairs, RLHF preference rankings and supervised fine-tuning sets, each serving a different training stage.

Prompt-response pairs teach a model to answer; preference rankings teach it which of two answers is better. They are different data with different annotator requirements. A model given broad coverage and no preference data answers every language fluently and none of them well, which is the most common gap in a first training run.

Yes, and it usually has to be commissioned rather than sourced, because little or no usable public corpus exists. Lifewood collects through region-native speakers across Asia-Pacific hubs including Cebu, Malaysia and Bangladesh, which is the only reliable way to reach languages with no standard orthography or commercial recordings.

Evaluation data is built to the same standard as training data and kept separate from it. The point of a held-out set is that the model has not seen it, so contamination between the two is the failure to guard against — which is why supply and testing are scoped together rather than bought from different vendors.

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