RLHF & Model-Training Data Services
The human feedback layer behind aligned AI models
Corpshore AI delivers RLHF and LLM training-data services — human preference data, SFT demonstrations, model evaluation, red-teaming and safety alignment — produced at scale by expert annotators.
Great models are trained on great human judgement. We supply the comparison data, demonstrations and evaluations that teach large language models what a helpful, honest and harmless response looks like. It sits alongside our data annotation practice and the wider Corpshore AI stack, so you can source labelling and alignment data from one trusted partner.
Aligning models to human preferences
RLHF — reinforcement learning from human feedback — is how modern language models are tuned to be helpful, honest and safe after pre-training.
The idea is simple but the execution is hard: humans demonstrate good responses and rank competing model outputs, a reward model learns those preferences, and the policy is optimised against it. The quality of that human signal — its consistency, coverage and domain expertise — sets the ceiling on how well a model aligns. Corpshore builds and manages the expert workforce, tooling and quality systems that produce reliable alignment data, from initial supervised fine-tuning demonstrations through preference comparisons, evaluation and adversarial testing.
Our RLHF & model-training data services
The full human-feedback stack for LLMs and multimodal models.
Preference & comparison data
Pairwise and ranked human preferences over model responses to train reward models for RLHF and DPO.
SFT demonstrations
High-quality demonstration responses that show a model the ideal answer for supervised fine-tuning.
Prompt & response writing
Curated prompts and expert-written responses spanning tasks, tones, difficulty and edge cases.
Model evaluation & rating
Human rating of helpfulness, accuracy, safety and instruction-following against your rubrics.
Red-teaming & safety
Adversarial prompting to surface harmful, biased or policy-violating behaviour before release.
Domain-expert data
Specialist annotators in code, law, medicine, finance, STEM and more for expert-grade signal.
Multilingual data
Preference, demonstration and evaluation data across 30+ languages and locales.
Multimodal data
Instruction, preference and evaluation data for image, audio and document-grounded models.
Rubric & guideline design
We help design the annotation guidelines and rubrics that make your human signal consistent.
How model-alignment data is produced
A repeatable, auditable pipeline turns your policy and rubrics into reliable training signal.
Guidelines & calibration
We co-design rubrics and calibrate annotators until judgements are consistent and defensible.
Demonstrations & prompts
Experts write SFT demonstrations and build prompt sets that cover your target distribution.
Preference collection
Annotators compare and rank model outputs to create the reward signal for RLHF or DPO.
Evaluation & red-teaming
Human raters score model versions and probe for unsafe or off-policy behaviour.
QA & adjudication
Multi-review, gold sets and inter-annotator agreement keep quality measurable and high.
Delivery & iteration
Structured datasets delivered in your format, with feedback loops to refine each round.
Judgement from people who know the domain
Alignment data is only as good as the humans behind it. We recruit, train and calibrate specialists — not anonymous crowds — and manage them under one accountable programme.
- Vetted domain experts across code, STEM, law, medicine and finance
- Trained and calibrated against your specific guidelines
- Multilingual coverage across 30+ languages
- Managed teams with QA leads and adjudicators
Measurable quality, protected data
We treat quality as a metric and security as a baseline, with the controls enterprise AI teams require.
Data for every kind of model
LLMs & chat assistants
Alignment, helpfulness and safety data for conversational language models.
Code models
Expert preferences and evaluation for code generation, review and agentic coding.
Multimodal models
Instruction and preference data for vision, audio and document understanding.
RAG & search
Relevance, groundedness and citation-quality judgements for retrieval systems.
Safety & policy
Red-teaming and policy-compliance data to harden models before launch.
Agents & tool use
Evaluation and preference data for multi-step, tool-using AI agents.
Why AI teams partner with Corpshore
Expert judgement, measurable quality and secure delivery — the foundations of training data you can trust.
RLHF & training data, answered
What is RLHF and why does it matter?
What types of training data do you provide?
Who produces the data - crowdworkers or experts?
How do you ensure quality and consistency?
How do you handle data security and confidentiality?
Ready to align your model with expert human feedback?
Tell us your model, domains and guidelines. We’ll design an RLHF and evaluation data programme — and prove it with a paid pilot.
Start with a pilot