FOR HUMAN DATA COMPANIES
[01] The idea
Human data companies and AI labs work rigorously to post-train, fine-tune, and evaluate frontier models. Data Gradient applies that same level of rigor to training the workforce.
Model side
Data Gradient
Mid-training
Foundational courses built from guidelines on domain specific corpora.
Supervised fine-tuning
Guided drills on ideal response generation that improve outcomes
Evals
Scored practice against production rubrics to inform your next training run
RLHF
Reviewer feedback loops on real output with reasoning traces
Continual training
Spaced re-trains to maintain high quality outputs for persistent projects
[02] Two tracks, one record
The same qualification data serves both sides. Annotators earn a verified, portable record of what they can actually do. Teams search that record instead of interviewing their way through a spreadsheet of resumes.
[03] For teams
Built for the people accountable for batch acceptance: technical program managers, strategic project leads, delivery and quality leads.
01 / BUILD
Research become guidelines.
We take frontier research and turn them into guidelines.
02 / GATE
Credentials that matter.
If an annotator doesn't pass threshold, they don't get a certificate.
03 / SEARCH
We have the metadata
We track everyone's ability from task type, language, modality.
04 / GRADE
Rubrics mean something here.
They mean everything. We take rubrics very seriously. Every task is answered in accordance to a clearly outlined rubric.
05 / COMPREHENSIVE
One program, many techniques
We plan to cover every modality known to man... in due time.
06 / PLACE
Partner and receive
If you partner with us on a course, you get vetted candidates instantly.
[04] For annotators
We take state of the art research and create training courses out of them.
01 / LEARN
Courses for real work
We run our courses the way we ran our teams for the biggest frontier labs on the planet.
02 / PRACTICE
Real tasks, not quizzes
You do the actual task in the actual interface. Get it wrong here, where it costs nothing, instead of on a live batch.
03 / IMPROVE
Get actual feedback
You don't just get a score. You get feedback on how you can improve.
04 / QUALIFY
The grade opens the work
Each skill has a threshold. If you clear it, it makes you visible to teams staffing for that skill.
05 / KEEP IT
Your credential follows you
It belongs to you, not to a company. Take it to the next project, the next human data company, the next lab.
06 / STAY CONNECTED
Stay in touch with your network
Even the best projects and clients can sunset their work. Stay in touch with the people who made it memorable.
[05] The proof
Training platforms usually report completions and seat time. Neither predicts whether a batch gets accepted. These do — and they’re the same numbers whether you’re staffing the work or doing it.
Time to qualified
Days from first lesson to first accepted production task. The number that decides whether you can staff a project next week or next month.
First-pass acceptance
Share of work accepted without revision — measured in practice first, so you know the answer before the client does.
Rework rate
The margin killer. Tracked per person, per task type, per error class, and traced back to the lesson that failed to land.
[06] Questions
Is this an LMS?
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It does what an LMS does: courses, enrollment, progress, and analytics. But that's the delivery mechanism, not the point. The product is qualification: practice environments that mirror production workflows, scored against the rubric the work will be graded on. If all you need is compliance training, a general-purpose LMS is cheaper and you should use one.
Do annotators pay?
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How do the sandboxes match our workflow?
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Who owns the qualification data?
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What does a first engagement look like?
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[07] Get started
You choose the research, we build the course, the sandbox, the rubric, and we co-lead the session.