AI & Data Annotation
The labelled data, guidelines, and evaluation that make AI systems dependable.
Behind every dependable model is carefully prepared data. We help teams design annotation guidelines, label datasets across text, image, audio, and video, and stand up the human-in-the-loop review and quality assurance that keeps model behaviour honest. We treat data annotation as core work — the groundwork that decides whether an AI system can be trusted — not an afterthought bolted onto general consulting.
What you can expect
- Clear, tested annotation guidelines your labellers can apply consistently.
- Datasets prepared and quality-checked for the task you are actually solving.
- Human-in-the-loop review and evaluation that surface model weaknesses early.
What this looks like in practice.
Text annotation
Classification, entity labelling, spans, and relationships for language tasks and NLP training data.
Image annotation
Bounding boxes, segmentation, keypoints, and classification for computer-vision datasets.
Audio annotation
Transcription, speaker and event labelling, and segmentation for speech and sound models.
Video annotation
Frame-level and temporal labelling, object tracking, and event tagging across sequences.
Data classification & entity labelling
Consistent taxonomies and structured labels that make datasets usable for training and evaluation.
Sentiment & intent labelling
Nuanced subjective labels with guidelines that keep judgement calls consistent across annotators.
Dataset preparation
Cleaning, de-duplication, sampling, and formatting so a dataset is ready to train or evaluate against.
Annotation guideline development
Written, example-rich guidelines that turn a labelling task into a repeatable process.
Human-in-the-loop review
Review loops where people check, correct, and improve model or annotator output continuously.
Quality assurance
Sampling, adjudication, and inter-annotator agreement checks to keep label quality measurable.
Model evaluation support
Structured evaluation sets and rubrics to measure model behaviour against what actually matters.
Pilot & ongoing operations
Run a contained pilot, then stand up and refine the workflow for ongoing annotation delivery.
A clear path from start to steady state.
- 01
Discovery
Understand the model, the task, and what a correct label really means for your use case.
- 02
Sample dataset review
Examine a representative sample together to surface ambiguity and edge cases early.
- 03
Annotation guideline design
Write clear, example-driven guidelines that make labelling decisions repeatable.
- 04
Pilot batch
Label a small, contained batch to validate the guidelines and the workflow in practice.
- 05
Quality review
Measure agreement, adjudicate disagreements, and tighten the guidelines where needed.
- 06
Workflow refinement
Adjust tooling, taxonomy, and review steps based on what the pilot revealed.
- 07
Scaled delivery
Move to steady-state labelling with quality checks built into the pipeline.
- 08
Ongoing evaluation
Maintain evaluation sets and review loops so quality holds as data and models change.
Good to know.
Can you start small before we commit?
Yes — a pilot batch is exactly how we recommend starting. It validates the guidelines and workflow on real data before anyone scales.
Do you handle sensitive data?
We design annotation workflows with data handling in mind and agree the appropriate safeguards with you up front. We do not make compliance or certification claims we have not earned.
Which annotation tools do you use?
We choose tooling to fit the data type, task, and your existing pipeline rather than forcing every project through one platform.
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Ready to talk ai & data annotation?
Tell us where you are and what you are trying to achieve. We reply to every enquiry personally.