Preference Data,
Built for Reward Models
We produce pairwise comparisons and written rationales with measured inter-rater agreement — broad coverage where volume matters, domain specialists where correctness does.
- B-114deliveredPairwise compare99.1% QA
- B-115deliveredRationale write-up98.6% QA
- B-116in qaAgreement check74%
- B-117runningAdjudication38%
Where AI teams commission specialist raters & domain experts for preference data that moves the needle.
Generalists to Domain Experts
Raters for scale. SMEs for code, medical, legal, finance, and more
Any Annotation Tool
Use off-the-shelf, open-source, or your custom annotation tooling
Every Task Format
Pairwise ranking, best-of-N, multi-criteria scoring, failure tagging, and rewrites
One Team Running Your Preference Labeling End to End
One programme, run end to end, delivering consistent preference data for RLHF, DPO, reward modeling, and LLM alignment.
End-to-End Delivery for Preference Raters
Generalist raters for scale, domain experts for code, medical, legal, and finance, and QA leads for calibration. Find the right people for any preference task.
Work Happens in Your Tooling
Raters work inside your annotation platform, evaluation stack, or internal preference UI. You control access and permissions. Your data never leaves your systems.
Communication and Project Hub
Built-in chat, instruction editor, and everything you need to coordinate preference labeling across distributed teams. Share guidelines, resolve disagreements, and run calibration sessions in one place.
Secure Global Payments and Transparent Pricing
Pay our delivery team in any country from a single dashboard. You set the rates, we add a small fixed fee on top. No hidden costs, no chasing invoices.
Deliver Preference Raters for Every Task Format
We get a shortlist of qualified raters ready to start labeling. What we deliver:
How ReinforcedX Works for RLHF &
Preference Data
We agree the spec, run delivery inside your tools, and report quality on every batch.
Send Us the Spec
Describe your task format, rubric, and domain requirements. Receive proposals from raters with relevant experience in preference labeling, evaluation, or your target domain.
We Work In Your Tools
We agree the spec, then run delivery inside your annotation platform, evaluation stack, or internal preference UI.
Communicate and Pay in One Place
Share rubrics and guidelines, message your team, and handle global payments from a single dashboard.
Post Your RLHF Job
Now
Send us a sample batch and we will come back with a spec and a quote.
Large Project? We
Can Help.
Get a dedicated team managed end-to-end for complex preference labeling projects.
Deliver for Any Labeling Platform
Deliver preference raters on ReinforcedX, then invite them to your preferred annotation environment.
FAQs About RLHF & Preference Data
Common questions about delivery our delivery team on ReinforcedX for RLHF projects.
What types of preference tasks can raters handle?
How do you assure preference labeling?
Can I use my own custom annotation tool?
How quickly can you scale volume up?
What about multilingual preference data?
Join the #1 Platform for AI Delivery
Three ways to work with us, from a single batch to a standing programme.
Scope Your Project
Send us the spec and we scope the work, agree the quality bar, and start delivering. We run inside the tools you already use, so output lands where your team works.
Done-for-You
We staff, run, and QA the whole programme inside your tools. End-to-end operations for large or complex projects.
Join as an delivery lead
Keep a standing delivery pipeline running against your roadmap, with quality reported every week.
Working with us
How soon can rlhf preference data work start?
Typically within a week or two of a scope being agreed. The first delivery is deliberately a small batch so you can check the output against your expectations before volume ramps.
What do you need from our team?
One process owner who knows the workflow, one engineer with access to the systems involved, and a weekly 45-minute review. No standing committee, and no requirement for an ML specialist on your side.
Who owns the output and the data?
You do. Datasets, labels, weights, evaluation suites and runbooks are yours and are handed over at the end. Your data trains your models only, with zero-retention provider settings by default.
Can you scale volume up quickly if we need it?
Yes, and the quality bar holds because the rubric and gold set are already agreed by that point. Ramping is a staffing question, not a re-scoping one, so it usually takes days rather than a new engagement.
We already have a vendor for this. Why switch?
Often you should not. The cases where teams move to us are when they cannot get a quality number out of their current vendor, or when the work is delivered as an opaque batch with no trace of how disagreements were resolved.
What happens after the engagement ends?
We stay on-call for 30 days at no extra cost, then move to an optional support retainer. Most teams also keep a quarterly evaluation review with us to catch drift early.