AI outsourcing
Labelling millions of images with the accuracy a model can trust
A UK computer-vision startup could not label training data fast or accurately enough. An outsourced annotation team scaled volume while lifting accuracy.
Results at a glance
- +8x
- Weekly labelling volume
- +10 pts
- Label accuracy at audit
- +42%
- Engineer time on modelling
- -51%
- Cost per labelled image
40k to 320k images
88% to 98%
The challenge
The startup's model was ready to train but its data pipeline was not. Founders and engineers were annotating images themselves between builds, which was slow, inconsistent and pulling scarce talent away from model work.
Label quality varied by annotator, and the resulting noise was capping model performance. The team needed to label millions of frames on a deadline set by an investor milestone, with quality high enough that the model could actually learn from it.
Pain points
- Engineers annotating data by hand instead of building and training models
- Inconsistent labels introducing noise that limited model accuracy
- No repeatable quality-control process to catch and correct bad annotations
- A hard investor deadline that in-house capacity could not meet
Our approach
- Scaled a trained annotation team against a written labelling guide with worked edge cases
- Ran layered quality control with consensus checks and a senior review tier on sampled work
- Set up a feedback loop so ambiguous cases refined the guide rather than repeating as errors
- Data handled under UK GDPR with a Data Processing Agreement and secure segregated storage of image sets
Results at a glance
The results
- Weekly labelling volume rose from roughly 40,000 images to over 320,000 without loss of quality.
- Label accuracy at audit climbed from 88 percent to 98 percent, lifting downstream model performance.
- The startup hit its investor milestone and kept its engineers on model work throughout.
Our labels were the bottleneck, not the model. A proper annotation team with real quality control let us train on data we could finally trust.
Common questions
Annotators work to a written labelling guide with layered quality control, consensus checks and senior review on sampled work, and a feedback loop that refines the guide as edge cases appear.
These are representative engagements. Client identities are anonymised and the metrics are illustrative of the type of results this work delivers, not audited figures for a named client.
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Six-hour response. UK GDPR compliant. Named UK accountability.