Instruction and SFT Datasets
We create high-quality prompt and response pairs for supervised fine-tuning, written and reviewed to your domain and tone.
LLM Annotation Services
LLM Annotation Services
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
Great models are built on great data. Noseberry provides human-in-the-loop annotation that trains, aligns and evaluates large language models, from instruction datasets and preference data to red teaming and benchmark sets, delivered by domain reviewers with strict quality control.
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What is LLM annotation?
LLM annotation is the human labeling of text data used to train, fine-tune, align and evaluate large language models. It includes writing instruction and response pairs, ranking model outputs for reinforcement learning, red teaming for safety, and building evaluation sets that measure quality. Noseberry delivers this annotation with clear guidelines, domain-expert reviewers and multi-pass quality assurance, so your model learns from data it can trust.
Key takeaways
- LLM annotation is the labeled data layer behind fine-tuning, alignment and evaluation.
- It powers instruction tuning, RLHF preference data, red teaming and benchmark sets.
- Quality depends on clear guidelines, expert reviewers and inter-annotator agreement.
- Your data stays secure, with private handling, access controls and audit trails.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
What we deliver
Our LLM annotation services
RLHF and Preference Data
We rank and compare model outputs to build the preference data that aligns models with human judgement.
Red Teaming and Safety Labeling
We probe models for harmful, biased or unsafe outputs and label the failures, so you can harden the model before launch.
Evaluation and Benchmark Sets
We build gold-standard test sets and rubric-based scoring to measure accuracy, helpfulness and safety over time.
RAG Ground Truth
We label question, context and answer sets so you can measure and improve retrieval-augmented systems.
Classification and Entity Labeling
We annotate text for intent, sentiment, entities and categories to support NLP and routing models.
Domain Expert Annotation
We use reviewers with subject knowledge in areas like finance, healthcare and legal, where generic labelers fall short.
Multilingual Annotation
We label across languages so your model performs beyond English.
Quality control
How we ensure annotation quality
Annotation is only as useful as it is accurate, so quality control is the core of the service.
- Clear, versioned annotation guidelines agreed with you upfront
- Trained reviewers, with domain experts where the task demands it
- Multi-pass review and inter-annotator agreement scoring
- Gold sets and spot audits to catch drift
- A feedback loop that refines guidelines as edge cases appear
Who needs LLM annotation
Teams fine-tuning open-source models, building custom or domain-specific LLMs, aligning model behaviour with RLHF, or setting up evaluation to track quality and safety before and after launch.
How it works
From guidelines to labeled data
1
Scope and guidelines
We define the task, labels and quality bar with you.
2
Pilot batch
A small labeled sample to validate guidelines and agreement.
3
Production annotation
Labeling at volume with ongoing QA.
4
Delivery and iteration
Labeled data in your format, with a loop to refine as needed.
Turnaround depends on volume and complexity. A pilot batch can be ready in days, with production annotation scaled to your timeline.
Data security
Your data, handled privately
Your data is handled privately, with encrypted storage, role-based access controls, NDAs with reviewers, and audit trails. We align to standards including GDPR, HIPAA and SOC 2, and can annotate within your environment where required.
GDPRHIPAASOC 2
Why Noseberry
Why choose Noseberry for LLM annotation
Specialist
We sit inside an AI, Cloud and Data firm, so annotators understand how the data will actually be used.
Quality-first
Guidelines, expert review and agreement scoring, not volume at the cost of accuracy.
Vendor-neutral
We support any model or fine-tuning pipeline you use.
Proven at scale
Part of a team with 250+ solutions delivered across 20+ countries.
LLM annotation, answered.
Data labeling is the broad practice of tagging data. LLM annotation is the subset focused on language model needs, such as instruction data, preference ranking, red teaming and evaluation.
Reinforcement learning from human feedback data is a set of human rankings of model outputs. It teaches the model which responses people prefer, which is central to aligning behaviour.
Yes. For fields like finance, healthcare and legal we use reviewers with subject knowledge, because generic labelers miss domain nuance.
Through gold sets, multi-pass review, inter-annotator agreement scoring and spot audits, against guidelines agreed with you.
Yes. Where data cannot leave your environment, we annotate inside it, with access controls and audit trails.
Building or fine-tuning a model?
Book your free 30-minute AI strategy session and we will scope an annotation pilot for your data.
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