RLHF Data Services: Human Preference, SFT and Reward-Model Data
Pairwise preference rankings, rated responses, rewritten gold answers and instruction-tuning data, written and judged by vetted native speakers and domain experts. Free pilot batch before you commit.
What you get from an RLHF data partner
RLHF data services supply the human judgements a model is aligned on: which of two responses is better and why, a score against your rubric, a corrected or rewritten answer, and fresh prompt-response pairs for supervised fine-tuning (SFT). The model is only as good as the people making those calls, so the work is about rater selection, rubric design and agreement checks, not just volume.
AI Taggers runs RLHF and SFT projects with raters matched to your domain and language: native speakers for multilingual models (Arabic dialects, Turkish, Hebrew, Persian, Urdu, South-East Asian languages and 100+ others), and credentialed reviewers for medical, legal and financial content. Every batch goes through Australian-led QA with inter-rater agreement tracking before it ships.
You keep control of the guidelines, the rubric and the data. We work in your tooling or ours, deliver in JSONL or your schema, and can run DPO-style chosen/rejected pairs as easily as classic reward-model rankings.
RLHF and alignment data we deliver
Pairwise preference ranking
A/B and multi-way comparisons with written rationales, for reward models and DPO (chosen/rejected pairs).
Rubric scoring
Likert or criteria-based ratings for helpfulness, correctness, harmlessness, tone and instruction-following, with calibration rounds.
SFT / instruction data
Prompt-response pairs written from scratch by subject experts, including multi-turn conversations and tool-use traces.
Response rewriting
Raters correct or rewrite model outputs into gold answers, so you get the fix as well as the verdict.
Multilingual & dialect RLHF
Native-speaker preference data in Gulf, Egyptian, Levantine and Maghrebi Arabic, Turkish, Hebrew and more, judged for fluency and cultural fit, not literal translation.
Expert-domain judgement
Clinicians, lawyers, finance and engineering reviewers for domain-specific preference and factuality data.
How a project runs
- 1
Scope call and rubric
We review your guidelines (or help write them), agree the task types, languages, volume and output schema.
- 2
Free pilot batch
25-50 prompts rated or written by the proposed rater pool, returned in 24-48 hours so you can check quality on your own data.
- 3
Calibration
Disagreements from the pilot become guideline edits and calibration examples before scaling.
- 4
Production with QA
Ongoing batches with gold-set checks, inter-rater agreement reporting and reviewer escalation on low-agreement items.
How RLHF pricing works
RLHF is priced per task (per comparison, per rated response or per written pair) or per rater-hour for open-ended work. We quote after the free pilot, once we know how long a task really takes.
Typical per-unit rates for image, text, audio and video work are on our pricing page.
Best fit for
- Teams fine-tuning or aligning an LLM for a non-English market
- AI products that need domain-expert preference data
- Labs needing a second vendor alongside Scale, Surge or in-house raters
- Startups that want a small, high-quality pilot before a large contract
Frequently asked questions
What is the difference between RLHF data and SFT data?
SFT data is example prompt-response pairs the model learns to imitate. RLHF data is human judgement on the model's own outputs (rankings, scores, rewrites) used to train a reward model or to run preference optimisation such as DPO. Most alignment projects need both.
Can you do RLHF in Arabic dialects and other languages?
Yes. Multilingual preference data is our main specialism. Raters are native speakers of the specific dialect (for example Saudi Najdi, Emirati, Egyptian or Moroccan Darija), so judgements reflect natural usage rather than Modern Standard Arabic or translated English.
How do you control rater quality?
Raters pass a task-specific qualification test, then every project uses gold items, overlap on a sample of tasks, inter-rater agreement tracking and senior reviewer escalation. Raters who drift below the agreed threshold are removed from the project.
Do we have to use your platform?
No. We can work inside your annotation tool or labelling platform, or provide ours. Output is delivered in JSONL or whatever schema your training pipeline expects.
How fast can you start?
A pilot batch is usually returned within 24-48 hours of receiving prompts and guidelines. Production ramp-up depends on language and expertise, typically one to two weeks for scarce specialisms.
Get a free RLHF pilot batch
Send 25-50 prompts (or model outputs) plus your rubric. We return rated, ranked or rewritten data in 24-48 hours so you can judge quality first.
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