Data annotation outsourcing: labelling data for AI

Key takeaways

Data annotation outsourcing hands the labelling of text, images or documents to a trained team so an AI model can learn from them. It suits outsourcing because the work is high volume and rule-based once the labelling guide is written. Quality depends on clear guidelines and checks for agreement between labellers. Personal data needs a processor contract and transfer safeguards.

• Write a labelling guide with examples first.
• Pilot on a small batch before scaling.
• Check agreement between labellers.
• Keep personal data minimised and protected.

What does data annotation involve?

Annotators tag items according to a guide: classifying emails by topic, marking fields on invoices, labelling objects in images or rating whether answers are correct. The labelled data trains or tests an AI model. The work is repetitive and needs concentration, consistency and clear rules for edge cases.

• Which business processes should you outsource?
• AI versus BPO: what to automate and what to staff

When should you outsource annotation?

When volumes run into thousands of items, when your experts' time is too valuable for labelling, or when you need it done quickly. Keep the labelling guide, edge-case decisions and final quality sign-off in-house, because they define what the model learns.

• Outsource: labelling at volume, first-level checks.
• Keep: the guide, edge cases, final quality sign-off.

• Human in the loop: pairing AI tools with an outsourced team
• Outsource, split, fix or keep: four process verdicts

How do you check annotation quality?

Have a sample labelled by two people and measure how often they agree. Low agreement usually means the guide is unclear, not that labellers are careless. Review disagreements, update the guide with examples, and keep a gold set of items with known correct labels to test new annotators.

• Quality sampling: how many outsourced items to check
• Can you measure it? The test every outsourced process needs

What data protection rules apply?

If the data includes personal information, you need a lawful basis, a processor contract and, for offshore teams, a transfer mechanism such as the IDTA. Remove or mask personal details the labellers do not need. Special category data, such as health records, needs extra care and may not be suitable for offshore annotation.

• Processes with sensitive data: can they be outsourced?
• GDPR and offshore outsourcing

What does this look like in practice?

A pattern we see: annotation projects stall because the guide was written before anyone tried labelling. A 200-item pilot exposes the unclear cases early, when they are cheap to fix.

• See client work
• BPO Solutions for UK businesses

Data annotation checklist

Prepare these before scaling.

• Labelling guide with examples.
• Pilot batch of a few hundred items.
• Agreement check between two labellers.
• Gold set for testing.
• Personal data minimised or masked.
• Processor contract and transfer mechanism.

• The process selection score: outsource, split or keep?
• IT processes to outsource in a small business

Next step

Tell us what data you need labelled and roughly how much. We will suggest how to pilot it and what the guide would need to cover.