Frontier model training is bottlenecked on one resource: expert humans who can label, evaluate, and adversarially probe what the model is doing. Our AI training and model-training services run on a curated pipeline of industry specialists, PhDs, and domain experts who deliver that work at quality and cost levels the marketplaces can't match.
Crowdsourced labeling works when the task is generic and the noise floor is low. For frontier training — domain-specific reasoning, safety-critical tasks, regulated subject matter — you need annotators who actually know the domain. That's the gap we fill.
The people reviewing your model's work are real practitioners: practicing doctors for medical projects, working attorneys for legal ones, security engineers for safety testing. No generalists pretending to be specialists.
Every piece of work gets reviewed more than once. When reviewers disagree, the disagreement is settled and documented, so the data gets cleaner over time, not noisier.
We try to break your model on purpose using a repeatable set of tests, then run the same tests on every new version, so you can watch safety improve over time instead of checking it once.
Without marketplace overhead and middlemen, we deliver expert-quality work at a much lower cost per task. Quality stays up; the bill comes down.
We offer the four kinds of expert human input that modern AI training needs. Most clients use at least three.
Experts compare and rank your model's answers to teach it what a good response looks like, judged by people who genuinely know the subject.
Custom tests for your field, scored by human experts against standards we design together with your team.
Experts deliberately probe your model for weaknesses in the areas that matter most to you, from security holes to harmful or off-limits answers.
Expert-written examples of questions and ideal answers, organized and balanced to teach your model exactly the behavior you want.
Training-data engagements move quickly. Most projects ship a first batch inside three weeks and settle into a steady weekly cadence.
One week to define the task, the standards, and the quality bar. We write the instructions together with you, so there's no gap between what you want and what gets delivered.
A small pilot batch catches unclear instructions before they multiply across thousands of examples. We measure how consistently reviewers agree, then tighten the instructions.
Full-scale delivery with multiple rounds of review. You get weekly batches with quality scores, so you can start using the data as it arrives instead of waiting until the end.
Disagreements get settled and unusual cases get documented and folded into the instructions for the next batch, so quality keeps improving as we go.
Final delivery includes all the finished data, the instructions used to create it, the quality measurements, and a record of how disputes were settled. Most clients keep us on weekly for ongoing testing.
AI training and model training services supply the expert human feedback that frontier models need: RLHF (reinforcement learning from human feedback), evaluation, and red-teaming. Galatine Technologies delivers this through a curated network of PhDs and domain specialists at quality and cost levels marketplaces cannot match.
RLHF, reinforcement learning from human feedback, is a technique for aligning AI models using rankings and corrections from expert human reviewers. Galatine Technologies provides RLHF, evaluation, and adversarial red-teaming for organizations training or fine-tuning frontier models.
Galatine Technologies' AI training work is performed by a curated pipeline of industry specialists, PhDs, and domain experts, rather than untrained crowd labor, which is what makes the feedback frontier-grade.
Tell us about the model you're training, the field it needs to work in, and the standards you're aiming for. We'll come back with a pilot plan inside two business days.