Preference ranking
Raters compare responses and rank them, producing the pairs your reward model learns from.
RLHF services for LLMs and LVMs
Refine generative models with reinforcement learning from human feedback, using expertly curated human judgments that improve safety, alignment and response quality.
- Preference ranking · error annotation
- Reviewer calibration & agreement scoring
- Domain-expert raters
preference_pairs_r3.jsonlreview
How long should I boil an egg for a runny yolk?
Response A
Boil it for about 12 minutes, then cool it in cold water.
Error: 12 minutes gives a hard-boiled yolk
Response B preferred
About 6 minutes in boiling water, then move it to ice water for a minute to stop the cooking.
Rubric · A vs B
- Helpfulness
- Factual accuracy
- Safety
- Tone
illustrative examplesample data
RLHF tasks we run
Human judgment in the training loop
RLHF involves judging the quality of model outputs, not just labeling data. It's subjective work that needs trained people who understand nuance, ethics and domain context.
Error annotation
Factual, logical and safety errors flagged and categorized in model outputs.
Content generation
High-quality prompts and reference responses written to fine-tune behavior.
Attribute feedback
Outputs scored on helpfulness, accuracy, tone and harmlessness against your rubric.
Output evaluation
Human-led review that assesses model accuracy, safety and usefulness.
Data correction
Low-quality or harmful outputs rewritten to show the model what good looks like.
Calibrated raters
Preference data you can trust
Raters are calibrated on gold examples before they start, and disagreements are reviewed by leads, so your reward model learns consistent preferences.
- Gold-set calibration before production
- Inter-rater agreement tracked per batch
- Written rationales on hard comparisons
Agreement with gold set · illustrative
threshold 85%
- R-0794%production
- R-1291%production
- R-0388%production
- R-2172%recalibrate
- R-1590%production
Lead review: R-21 rated verbose answers higher than the rubric allows. Added two gold examples on concision and re-tested before returning to production.
Use cases
Where teams put this data to work
Reward modeling
Pairwise and ranked preferences to train reward models for RLHF and DPO.
Safety & red teaming
Adversarial prompts and harm labels to make models refuse what they should.
Model evaluation
Side-by-side human evaluations to compare checkpoints before release.
Hallucination checks
Error annotation that flags unsupported or wrong claims in outputs.
How we work
From pilot to production
Every RLHF project starts with a rubric and reviewer calibration, then runs with inter-rater agreement scoring and continuous feedback loops, managed by domain experts.
- Step 01
Consult
We review your data, edge cases and success criteria, then write annotation guidelines with you.
- Step 02
Annotate
A trained team labels a pilot batch, following the agreed guidelines to the letter.
- Step 03
Calibrate
You review the pilot. We fold your feedback into the guidelines before scaling up.
- Step 04
Multi-layer QA
Every batch passes peer review, expert QA and automated checks before it leaves us.
- Step 05
Deliver
Labels ship in your format on your schedule, ready to drop into training.
Quality pipeline
Five checkpoints between raw data and your model
98%+
Accuracy guarantee
- 01AnnotatorLabels against your guidelines
- 02Peer reviewA second annotator reviews the work
- 03Expert QASenior reviewer samples and corrects
- 04Automated checksRules catch missing or invalid labels
- 05Client sign-offYou approve before final delivery
Results
Quality you can measure
- Accuracy guarantee
- 98%+Accuracy guarantee
- Returning clients
- 96%Returning clients
- Cost reduction
- 2.5xCost reduction
- Projects completed
- 200+Projects completed
“Our fashion AI model required pixel-level segmentation across 72 garment categories, and Intellisane AI handled it flawlessly. They quickly adapted to our complex annotation guidelines and delivered consistent, high-quality labels at scale. Their domain focus, speed, and attention to visual detail were exactly what we needed.”
Valerio Colamatteo
Sr. AI Scientist & Team Lead, MindEarth
Related
Alignment data for assistants, copilots, content tools and multimodal models.
FAQ
Questions, answered
Something else on your mind? Email sales@intellisane.ai.
Get started
Let's scope your project
Tell us about your data. We'll come back with a plan and a free pilot batch so you can see the quality first.