Access verified domain specialists, engineered evaluation rubrics, and deterministic grading environments for SFT, RLHF, DPO, and complex reasoning tasks.
Domain experts write demonstrations, rank outputs, and set the bar for quality. Your model learns from people who actually know the field.
Human reviewers catch subtle errors, bias, and unsafe behavior that benchmarks and LLM judges score as fine. Every flagged failure becomes a new training signal.
Route real-world feedback and hard cases back to expert annotators, so each release is measurably better than the last.
Access a vetted network of credentialed professionals, sourced, screened, and managed by our team.
Scale domain expertise across your AI workflows
Monitor deployed agents against expert-defined standards
Validate model performance before it reaches production
Each service answers a different question on the road from a capable model to a dependable one.
How do we show the model what a correct answer looks like?
Practitioners write worked examples in their own field, with the reasoning laid out and the format matched to your production pipeline. Your model learns professional judgment from professionals, not from the average of the internet.
Reviewed example sets, delivered in your schema.
How do we tell the model which of two good answers is better?
Specialists compare outputs side by side against a written scoring guide and record a short rationale for each decision. An unsupported claim or a missing caveat gets flagged rather than averaged away.
Ranked response pairs with documented reasoning.
Where will the model break under real-world pressure?
Lawyers, clinicians, and finance professionals build scenarios from the situations that actually create liability. These include compliance checks (HIPAA, SEC, GDPR) and long conversations designed to wear down safeguards. You get findings before launch, not incident reports after.
A severity-ranked risk log with reproducible test cases.
How do we define "good" in a way everyone can verify?
Experts turn quality standards into explicit criteria, then train reviewers until they reach the same verdict on the same output. Your product, risk, and procurement teams share one yardstick for releases and vendor decisions, and it is updated as the model improves.
A rubric, a reviewer guide, and a calibration report.
We partner with your ML researchers to design deterministic grading rubrics.
We spin up a custom cohort of live-tested domain practitioners within 72 hours.
Experts evaluate model outputs inside our secure, monitored grading environments.
High-signal alignment data is formatted into JSONL or Parquet for immediate training.
Your models can only learn from the quality of the signals they receive. Deploy verified experts to evaluate complex reasoning and produce feedback built for better AI.
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