We mark every step in a chain as correct, flawed, or unrecoverable — the process supervision a reward model needs, with independent adjudication wherever reviewers disagree.
Generalist reviewers for throughput, domain experts for math, code, science, legal, finance, and more
Reviewers work inside your annotation platform, custom interface, or internal pipeline
Math, code, logic, multi-hop chains, scientific reasoning, domain-specific problems, and more
One programme, run end to end, delivering step-level correctness labels for training process reward models, improving reasoning reliability, and catching errors before they compound.
Generalist reviewers for throughput, domain SMEs for math, code, science, and specialized fields, and calibration reviewers for consistency. Find the right people for any process supervision project.
Reviewers work inside your annotation platform, custom interface, or internal data pipeline. You control access and permissions. Your data never leaves your systems.
Built-in chat, instruction editor, and everything you need to coordinate verification across distributed teams. Share rubrics, manage calibration sessions, and align on edge cases in one place.
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.
We get a shortlist of qualified reviewers and SMEs ready to start labeling. What we deliver:
Create your account, scope a project, and commission specialists who work inside your existing tools.
Describe your labeling criteria, domain requirements, and volume needs. Receive proposals from reviewers and SMEs with relevant experience in reasoning evaluation, process supervision, or your target field.
We agree the spec, then run delivery inside any annotation platform, custom interface, or internal data pipeline.
Share rubrics and verification guidelines, message your team, and handle global payments from a single dashboard.
Send us your first batch and get reasoning reviewers and domain SMEs who can deliver step-level correctness labels, error identification, and the process supervision data your models need.
The largest network of process supervision specialists, ready to work in any annotation tool or internal workflow.
Deliver reasoning reviewers on ReinforcedX, then invite them to any annotation platform, or your own internal systems.
Quick answers to common questions about process supervision data on ReinforcedX.
Three ways to work with us, from a single batch to a standing programme.
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.
We staff, run, and QA the whole programme inside your tools. End-to-end operations for large or complex projects.
Keep a standing delivery pipeline running against your roadmap, with quality reported every week.
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.
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.
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.
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.
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.
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.
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