AI Datasets & Annotation

Problems this solves

• You need labeled data to train or evaluate a model, and none exists yet.
• Annotations are inconsistent because the labeling guidelines are vague.
• You can't tell whether a model is improving because there's no reliable test set.
• You're unsure where training data came from, or whether you're allowed to use it.

The dataset is the specification

A model can only learn what its data shows it. Unclear labels, unrepresentative samples or a test set that overlaps with the training data produce models that look good in development and disappoint in use. Most of the quality of an AI system is decided before any training starts — in how the dataset is designed, labeled and checked.

How we build datasets

Design first. We agree what the model needs to learn, which examples represent real use, how labels are defined, and how much data the task realistically needs.

Guidelines that remove guesswork. Annotation guidelines with clear definitions, worked examples and decisions on edge cases. We pilot them on a small batch and revise them before labeling at scale — that is where most inconsistency is prevented.

Quality you can measure. Review rounds, spot checks and inter-annotator agreement show how consistent the labels are, rather than assuming they are.

Documentation and versioning. Every dataset ships with a datasheet: where the data came from, how it was labeled, known limitations and the intended use. Versions are tracked so a model can always be traced back to the exact data it was trained on.

Types of data we prepare

• Text and document annotation — classification, named entities, relations, and structured fields extracted from documents.
• Human-feedback data for language models — ranking alternative responses, rating answers against criteria, and rewriting answers into better ones. This is the kind of data used in RLHF and preference-based fine-tuning.
• Evaluation and test sets — carefully reviewed "golden" examples used to measure model quality and compare models fairly.
• Collected datasets — gathered from public sources and APIs, cleaned and structured. See also Data Cleaning & Preparation.
• Synthetic data — generated examples to fill gaps, always labeled as synthetic and validated against real data.

Delivered in the format your tools expect, such as JSONL, CSV, Parquet or Hugging Face datasets.

Provenance and licensing

We record the source and license of every collected item, and we don't collect data you aren't permitted to use. Personal data is minimized or removed before annotation wherever possible. For medical and health-related text, see Medical Data Processing.

The right scale

We are a small, hands-on team. That suits dataset design, guidelines, expert and specialist annotation, evaluation sets and quality control. For very large annotation volumes, we design the guidelines and quality process and can work alongside your annotation team or platform.

Need the model trained on the result? See AI Training: Models & Teams.

What's included

• Dataset design: schema, label taxonomy and sampling plan
• Annotation guidelines with worked examples and edge cases
• Text and document annotation: classification, entities, relations
• Human-feedback data: response ranking, rating and rewriting
• Evaluation and test sets for measuring model quality
• Quality control: review rounds and inter-annotator agreement
• Collection from public sources and APIs, with licensing checks
• Synthetic data generation, clearly labeled and validated
• Train, validation and test splits without leakage
• Dataset documentation (datasheet) and versioning

How it works

1. Agree what the model must learn and how success will be measured.
2. Write labeling guidelines and pilot them on a small batch.
3. Annotate with review rounds and agreement checks.
4. Deliver versioned, documented data in the format you need.