Training data, feedback, and evaluations from vetted experts, checked at every layer
Your model is only as good as the data it learns from. ElenchLabs is an applied research lab translating real-world expertise into training data for frontier foundation models. Our datasets capture the reasoning, decisions, and methods behind expert work, and every delivery is checked against clear, task-specific standards.
Generic or poorly labeled examples miss the tasks, constraints, and edge cases your model needs to handle.
Expert-written examples, demonstrations, and labels for the tasks your model needs to learn.
Explore a sampleVague scoring criteria produce inconsistent ratings and preference labels, so the model gets conflicting feedback.
Expert ratings, response comparisons, and scoring rubrics that turn judgment into consistent feedback.
Explore a sampleBroad benchmarks and overall scores don't show which behaviors fail in your workflows, or why.
Tests built for your workflows, with expert scoring that reveals failures and measures progress.
Explore a sample4,182 vetted domain experts in our network
Our experts have worked and studied atA resume is where we start, not what we rely on. Before an expert joins your project, we collect evidence that they have the skills and have done the work, and that evidence stays with them.
Our own network of domain experts, each of whom applied and was interviewed before their first project. They do the reasoning work: authoring, judging, and adversarial testing. Where a project also needs scaled validation across a large volume of items, we run that as a second tier through partner teams, under the same rubric and the same quality layers, and it never substitutes for expert judgment.
Mostly what they have told us and what they have done: conversations with our AI interviewer about work they have actually carried out, and work completed for us that was accepted. The resume gives the first conversation its context. Each source is kept separately, so we always know whether a skill is claimed, described, or demonstrated.
It starts with an initial voice conversation of about 20 minutes, with the expert's resume as context rather than a script. It is open-ended: the interviewer asks for clarification and more detail on the work they have done, and their answers are kept in their own words. Experts can take as many further interviews as they like, and each one adds context to their record.
Before production, experts complete representative samples of your task on your rubric. Accepted work is added to their record as demonstrated skill, which is the strongest evidence we hold.
Experts calibrate on shared examples so everyone agrees on what good looks like. Accepted work is monitored, disagreements are reviewed, and contributors are coached or replaced when quality slips.
Yes. It is a plain list of what each expert has shown they can do and where that evidence came from, so you can read it yourself rather than trust a number.