Domain-expert evaluation

Experts who can actually grade the hard questions.

Pedagogically trained STEM experts design and grade capability and accuracy evaluations, and build curriculum-aligned benchmarks in math, physics, chemistry and biology up to undergraduate level. The same subject-matter bench behind content for the world's largest education publishers.

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GPQAExpert65%Non-expert34%The human bar, made measurable
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Meet Graveiens AI

A quick look at how Graveiens AI partners with teams to deliver human data for AI models.

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What it is

You cannot grade a model reasoning with a generalist crowd

Domain-expert evaluation is the grading of a model outputs by qualified subject-matter experts against a rigorous rubric, rather than by a generalist crowd or an automated judge. For STEM it means specialists scoring responses in math, physics, chemistry and biology, and building curriculum-aligned benchmarks with ground-truth answers. This is general capability and accuracy grading, deliberately scoped away from dangerous-capability and weaponization work.

Checking whether a final answer is right is not the same as knowing whether the reasoning holds. On a multi-step physics problem, only someone who understands the method can tell a lucky guess from sound work, or spot the step where the logic failed. That judgement is what this service supplies.

See the broad LLM evaluation hub
1Scope2Pilot3Produce4QA5Approve
How it maps to Graveiens AI

SME-designed evaluations and benchmarks, graded to a rubric

STEM SME reviewers

Pedagogically trained specialists, not a generalist crowd, matched to the subject and level you need.

Curriculum-aligned benchmark construction

New evaluation sets built to a defined syllabus and difficulty, with expert-written ground-truth answers.

Rubric and reference answers

Expert-written gold answers and grading guidelines, run through our four-stage QA workflow and backed by ISO 9001:2017.

The Expert Grading Depth Ladder

Five rungs, so you buy the rigor the task demands

Not every evaluation needs the same depth. We scope each engagement to a rung.

Answer check

Is the final answer correct against ground truth.

Method check

Is the working and method valid, not just the result.

Reasoning-trace review

Where exactly in the chain the model reasoned well or failed.

Rubric grading with rationale

Scored against a detailed rubric with written justification per item.

Calibrated multi-expert grading

Multiple experts grade with inter-rater calibration for the highest-stakes items.

Who it is for

For teams building and measuring model capability

Labs building benchmarks

Teams that need subject-matter experts to design and grade capability evaluations.

Evaluation startups

Teams that need an expert bench they can scale into without hiring one.

EdTech enterprises

Teams fine-tuning or evaluating models on curriculum-aligned academic tasks.

How it works

From subject and level to graded, expert-verified results

Scope

We agree on subjects, level, rubric, depth rung and volume, and match SMEs to your domain.

Design or ingest

We build a curriculum-aligned benchmark with ground-truth answers, or grade against your existing set.

Expert grading

STEM SMEs score responses against the rubric, documenting reasoning errors and rationale.

QA and deliver

Every result passes the four-stage review before you receive the graded dataset and any new items.

How the approaches compare

LLM-as-a-judge, generalist crowd, and domain-expert grading

LLM-as-a-judge

Best for cheap, fast, large-scale scoring. Instant and scalable, but inherits model blind spots and is not a trustworthy ground truth on hard reasoning.

Generalist crowd

Best for simple, subjective tasks. Fast and inexpensive, but cannot reliably judge STEM reasoning and agreement collapses on hard items.

Domain-expert grading (Graveiens AI)

Best for STEM capability and benchmarks. Judges reasoning, not just answers, and is defensible for capability claims.

Common mistakes

What to avoid when grading model capability

  • Grading STEM reasoning with a generalist crowd, so agreement collapses and the score becomes noise.
  • Trusting LLM-as-a-judge for ground truth, when a judge model shares the blind spots of the model under test.
  • Skipping expert review of benchmark items, since even expert-authored benchmarks carry errors.
  • Checking only final answers, so a right answer from wrong reasoning inflates the score.
  • Ignoring level alignment, so the benchmark measures the wrong thing.
FAQ

Questions, answered

What is domain-expert evaluation of AI models?
It is the grading of a model outputs by qualified subject-matter experts against a rigorous rubric, instead of by a generalist crowd or an automated judge. For STEM it means specialists scoring responses and, where needed, building new benchmark items with ground-truth answers. The goal is a defensible measure of capability and accuracy in fields where correctness is objective.
Why can a generalist crowd not grade STEM model outputs?
Judging technical reasoning requires the knowledge being tested. On graduate-level science benchmarks, PhD-level experts score far above skilled non-experts even when the non-experts have unlimited web access. If a non-expert cannot answer the question, they cannot reliably grade a model answer, so their grades add noise rather than signal.
Is this dangerous-capability or weaponization testing?
No. This service measures general capability and accuracy, such as math, science and educational-to-undergraduate reasoning. It is deliberately scoped away from weaponization and dangerous-capability evaluation. Sourcing specialist experts for a client own supervised safety programs is handled separately through our frontier expert network.
How is a curriculum-aligned benchmark built?
We define the subject, level and syllabus, then experts author items with ground-truth answers and a grading rubric, mapped to the cognitive skills the benchmark should test. Items are reviewed for correctness, fairness and difficulty before delivery, because benchmark items can contain errors even when expert-written.
How is this different from your LLM evaluation offering?
Our LLM evaluation hub covers model testing broadly across many task types. This page is the narrow, high-difficulty layer inside it: subject-matter-expert grading of STEM capability and curriculum-aligned benchmark construction.
Who are the experts?
Pedagogically trained subject-matter specialists drawn from the Graveiens AI education-services heritage, where experts built content for tier-one education publishers. Credentials and domain fit are matched to your task during scoping, and reviewer roles are shared on request.
Do you certify that our model is accurate?
No. We grade against the rubric you approve and deliver the evidence, including reasoning-trace notes and any new benchmark items. Whether the model meets your bar, and any public accuracy claim, is your decision. We provide measurement and expert judgement, not certification.
How much does expert evaluation cost and how do we start?
Start with a scoped pilot, either grading a sample of your outputs or building a small benchmark, invoiced only on approved deliverables. Cost depends on subject, level, grading depth and volume, quoted after scoping.

Put real subject-matter experts on your evaluations

Tell us the subject, level and rubric, and we will scope a grading or benchmark pilot you can judge on your own model.

Book a pilot

Related services

LLM evaluation RLHF & alignment data Red-team & safety evaluation Frontier expert network Specialized workforce