Data labeling
LLM evaluation and RLHF data from trained human reviewers
Response ranking, rubric-based scoring, fact-checking, red-teaming and supervised fine-tuning data, written and judged by reviewers who are calibrated against your criteria, not left to guess.
- Pilot phase
- Two or more review layers
- You own every label
Preference data is only useful if people judge consistently.
Human feedback is subjective by nature. The hard part isn’t finding people who can read a model response; it’s getting a team to apply the same standard of helpfulness, accuracy and safety across hundreds of thousands of judgements.
We document ranking criteria with worked examples for borderline responses, hold regular calibration sessions with your research team and route ambiguous safety cases to senior reviewers instead of deciding them in isolation.
Used for
- RLHF and DPO preference datasets
- Model benchmarking and regression testing
- Safety evaluation before launch
- Fine-tuning data for domain assistants
- Multilingual evaluation for African markets
Industries: Generative AI & LLMs
What our LLM evaluation & rlhf covers
Combine any of these in one workflow and one guideline.
Preference ranking
Side-by-side and multi-response ranking with written rationales.
Rubric scoring
Scores for helpfulness, correctness, tone and instruction-following.
Red-teaming
Adversarial prompts that probe safety, bias and policy gaps.
SFT data
Prompt-and-answer pairs written to your style and quality bar.
Fact-checking
Claim-level verification of model outputs with sources.
African-language evaluation
Evaluation and red-teaming in Swahili and other languages we can staff.
Formats and tools
Delivery formats
- JSONL
- Parquet
- CSV
- Custom schema
Tools we work in
- Your evaluation platform
- Label Studio
- Custom web tools
Pricing
LLM evaluation and RLHF from $12.00 per annotator hour, and from $10.00 at volume. Per-task pricing is available for stable workflows.
How we keep it accurate
Every project runs on the same four-check QA process. See how our QA works.
Calibration sessions
Regular sessions with your researchers keep our judgements aligned with yours as criteria evolve.
Agreement tracking
Reviewer agreement is measured on overlapping items and reported per batch.
Reviewer safeguards
Exposure limits, rotation and support for reviewers working on harmful content.
How a project runs
Scope and guidelines
We review a sample, agree classes, edge cases and acceptance criteria, and sign your NDA.
Pilot phase
Pilot phase helps you judge quality, speed and communication.
Certified team
Annotators pass a gold-set test before they are cleared for production.
Production and reporting
Batches with QA reports, in your format, on an agreed schedule.
LLM evaluation & RLHF questions
Who are your LLM reviewers?
Reviewers with excellent written English and, where needed, Swahili, who have a degree or equivalent experience in writing, teaching or research, and pass a paid writing and evaluation sample.
Can you write supervised fine-tuning data?
Yes. Reviewers write prompts and reference answers to your style guide, with editorial review before delivery.
How do you keep preference judgements consistent?
Written criteria with borderline examples, calibration with your team, overlap-based agreement tracking and escalation rules for ambiguous cases.
Do you do safety red-teaming?
Yes, with clear scope, policy guidance and wellbeing safeguards for the reviewers involved.
Send a sample. See the labeling quality first.
Share a few files and a line about your goals. A project lead replies within one business day with an approach, a timeline and a price.