AI Services
AI Data Annotation & Labelling
Clean labelled datasets and review workflows for teams training, tuning, evaluating, or operating AI systems.
What's Getting in the Way
AI systems are only as useful as the examples used to train, tune, and evaluate them. Many teams have raw data, conversation logs, documents, images, or model outputs, but no consistent labelling scheme. Without clear labels and review rules, model performance is hard to measure and harder to improve.
- Raw data exists, but it is messy, inconsistent, duplicated, or not ready for AI work
- Teams disagree on what labels mean, so annotations become unreliable
- Model failures repeat because there is no dataset of examples to evaluate against
- Manual review happens in spreadsheets with no quality control or clear process
What You Get
Annotation Schema
We define labels, categories, examples, edge cases, acceptance rules, and reviewer instructions.
Dataset Preparation
We clean, sample, deduplicate, and structure the raw data so it can be labelled consistently.
Labelling Workflow
We set up the review process, tooling, assignment flow, and quality checks for annotation work.
Quality Review
We audit label consistency, reviewer agreement, ambiguous cases, and error patterns.
Exportable Dataset
You receive a clean labelled dataset formatted for evaluation, fine-tuning, analytics, or model development.
The Process
Define the Goal
We clarify whether the dataset is for training, evaluation, classification, extraction, ranking, safety, or quality review.
Design the Labels
We create the label schema and annotation guide with examples for normal, edge, and ambiguous cases.
Prepare and Label
We structure the data, run the annotation workflow, and apply quality checks as labels are created.
Review and Deliver
We review consistency, resolve unclear cases, and deliver the dataset with documentation.
Pricing
$3K-$8K
AI Data Annotation & Labelling
Timeline: 2-4 weeks
Final price depends on scope. We confirm exact pricing after a free scoping call.
Plan your labelling workflowQuestions About This Service
What kinds of data can you label?
Text, conversations, support tickets, documents, model outputs, product data, research material, and structured records.
Do you provide annotators?
We can design and manage the annotation process. Depending on scope, we can work with your team or help set up a review workflow around external annotators.
Can this be used for fine-tuning?
Yes, if fine-tuning is the right path. We can format examples for training, preference data, classification, extraction, or evaluation.
How do you handle quality?
We define label rules, run sample reviews, check agreement, flag ambiguous cases, and document decisions.
Can you label model outputs?
Yes. Output labelling is useful for grading quality, detecting hallucinations, ranking responses, and building eval sets.
Often Combined With
Let's build it.
Tell us your context. We'll scope it in one call.