RLHF and alignment data

Human preference data, at the scale alignment needs.

Consent-backed, ISO 9001:2017 human preference data that is multilingual and STEM-capable: preference comparisons, ranked-response datasets, safety-refusal and helpfulness labeling, constitution-principle authoring, and edge-case validation. Priced to pilot at near-zero risk, invoiced only on approved work.

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Response ApreferredResponse BHuman preference, captured consistently
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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

Alignment is only as good as the humans behind the labels

RLHF data services supply the human preference judgements that teams use to train reward models and align large language models: ranked response pairs, safety and helpfulness labels, and validated edge cases. Graveiens AI produces this data through trained annotators and subject-matter experts, across 25+ languages and with consent-backed sourcing. We supply the human data; we do not promise specific gains in model performance. What we control, and what we sell, is the quality, consistency and provenance of the judgements that go into your pipeline.

Newer methods such as Direct Preference Optimization use the same preference pairs without a separate reward model, and Constitutional AI uses a written set of principles to generate harmlessness preferences. All of them depend on the underlying human data being clean.

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1Scope2Pilot3Produce4QA5Approve
How it maps to Graveiens AI

The full range of human alignment data

Preference comparisons and ranked responses

Human raters compare and rank model outputs against your guidelines, producing clean reward-model signal.

Safety-refusal and helpfulness labeling

Labels that teach the model when to refuse, when to help, and how to balance the two.

Constitution authoring and edge cases

Expert-written principles and hard-case checks for the prompts where models most often fail, run through our four-stage QA and ISO 9001:2017 workflow.

The Alignment Data Quality Framework

Five signals that separate signal from noise

Volume is easy; signal is hard. We hold every program to five quality signals, and report on them.

Guideline clarity

An unambiguous rubric with worked examples before any labeling starts.

Annotator expertise

The right people on the right prompts, with SMEs on technical and safety-critical items.

Inter-annotator agreement

Measured and calibrated over the campaign, because comparative judgement drifts.

Edge-case coverage

Deliberate inclusion of the hard and adversarial cases, not just the easy middle.

Consent and provenance

Explicit consent and an audit trail on every contribution.

Who it is for

For the teams aligning frontier and enterprise models

AI labs

Teams needing consistent, multilingual, STEM-capable human preference data.

Data platforms

Platforms that use Graveiens AI as specialist or overflow supply on demanding preference and safety work.

Enterprises fine-tuning models

Teams in regulated or expert domains who need judgement, not just clicks.

How it works

A low-risk path from guidelines to production preference data

Scope and guidelines

We align on your rubric, edge cases, quality signals and acceptance criteria first.

Pilot batch

A small labeled sample proves quality and consistency on your real prompts.

Production and QA

Work scales through the four-stage review, with agreement measured across the campaign.

Approve and scale

You approve deliverables and are invoiced only for what you accept.

How the approaches compare

In-house team, crowd-only vendor, and managed expert supply

In-house annotation team

Best for tight control on sensitive data. Full control and deep context, but slow to hire and scale and hard to cover many languages.

Crowd-only vendor

Best for high-volume, simple labeling. Fast and cheap, but weak on expert and safety-critical judgement, with provenance often unclear.

Managed expert supply (Graveiens AI)

Best for multilingual, STEM, safety-critical preference data. SME bench, consent-backed, four-stage QA, invoiced on approved work.

Common mistakes

What to avoid when buying preference data

  • Chasing volume over signal, when noisy labels make a reward model worse, not better.
  • Putting generalists on expert prompts, so technical and safety items lose agreement.
  • Skipping inter-annotator calibration, when comparative judgement drifts across a long campaign.
  • Ignoring consent and provenance, which becomes a compliance liability later.
  • Assuming AI feedback replaces humans everywhere, when it underperforms on expert, cultural and safety-critical judgement.
FAQ

Questions, answered

What are RLHF and alignment data services?
They are the production of human preference judgements used to train reward models and align language models: ranked response pairs, safety and helpfulness labels, principle authoring and validated edge cases. Graveiens AI supplies this data through trained annotators and subject-matter experts, multilingually and with consent-backed sourcing. We control the quality and provenance of the judgements; the training and its outcomes stay with your team.
What is the difference between RLHF and DPO?
RLHF trains a separate reward model from human preferences, then optimizes the policy against it. Direct Preference Optimization skips the separate reward model and optimizes the policy directly on the same preference pairs, often at lower cost. Both need clean human preference data, which is what we produce, and our deliverables work for either method.
Can AI feedback (RLAIF) replace human annotators?
Not fully. RLAIF can match human feedback on some tasks and reduce cost, but it underperforms on domain-expert, cultural and safety-critical judgement. The practical pattern is a mix: AI feedback for scale on easy cases, human experts for the hard, sensitive and multilingual cases. Our service focuses on that human layer.
How much preference data does a reward model need?
It varies by model and objective, and stable reward models often require large volumes of ranked examples. Volume alone is not the answer, though: label quality matters more, because high label noise degrades alignment. We scope volume with you and prioritize the quality signals that make each label count.
Is your preference data consent-backed and multilingual?
Yes. Every contributor is onboarded with explicit consent, and each dataset carries metadata and an audit trail. Labeling runs across 25+ languages with deep Indic coverage plus major Asian and European languages, so your safety and helpfulness signals are not English-only.
Do you promise our model will improve?
No. We supply high-quality, consistent, consent-backed human data and report on the quality signals behind it. Whether and how much your model improves depends on your training choices, which are yours to make. The honest deliverable is the data quality, not an outcome we do not control.
What is constitution-principle authoring?
It is writing the set of principles, or constitution, that guides preference generation in Constitutional AI style methods. Experts draft clear, testable principles and the preferences that express them, so the model has consistent guidance on how to balance helpfulness and harmlessness. Few vendors offer this as a named deliverable; we do.
How do we start and how are we billed?
Start with a scoped pilot batch on your real prompts. You are invoiced only for approved deliverables, so the risk of trying us is low. After the pilot proves quality, we scale through the same four-stage workflow. Pricing depends on task type, languages, expertise level and volume, quoted after scoping.

Get preference data your reward model can trust

Send us a sample task, a preference run or a safety-labeling batch, and pay only for what you approve.

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Related services

LLM fine-tuning Domain-expert evaluation Red-team & safety evaluation Data annotation & labeling Voice & speech data