Built on Verified Expertise. Designed for Enterprises.
LH2 Data Labs supplies frontier AI labs and enterprises with proprietary, expert-sourced training data across the full model lifecycle
How we source
Expert network, proprietary data, and quality infrastructure already in place
LH2 Data Labs has already solved for
Data that doesn't exist in any public corpus
Verified domain experts across every discipline
Multi-layer quality validation at scale
Full lifecycle pipeline coverage
The scaling laws for public data have run their course.
Common Crawl is in every base model. So is GitHub, arXiv, and every curated open-source corpus worth including. The marginal value of another pass over the public web is approaching zero — models trained predominantly on it are converging on the same capability ceiling, and synthetic data pipelines are hitting model collapse faster than anyone publicly admits.
The unlock is proprietary data with genuine distribution novelty. Not paraphrased. Not model-generated. Human-derived, domain-grounded, and structurally unlike anything in the pre-training mix. That's the only supply problem we're set up to solve.
What we've built.
On one side: a sourcing network of verified domain experts — researchers, engineers, specialists — across disciplines where depth of judgment is the signal, not just surface-level annotation consistency. People who can generate, evaluate, and rank outputs in domains where most annotators are simply unqualified to operate.
On the other side: access to proprietary data assets — knowledge artifacts, operational traces, structured expert output that don't exist in any public corpus. The kind of data that shifts your model's behavior in the target domain rather than reinforcing what it already knows.
Most data vendors have one of these. Building both, at the quality bar frontier work requires, is the harder problem and the one we've focused on.
Where we operate in the pipeline.
Post-training is where we spend most of our time — SFT, preference data for RLHF, RL environment construction, domain-specific evals that surface real failure modes rather than confirm expected performance. We also supply pre-training data where the requirement is genuine distributional novelty rather than scale.
Across all of it, the work starts with problem definition: understanding where your model's capability curve flattens, what the target task distribution actually looks like, and what data will move your internal evals rather than just your public benchmark numbers.
