Human-in-the-Loop Data Labelling for AI Training
Designed and built a data labelling solution for a London-based AI company, producing the high-quality, human-validated training data that machine-learning models depend on. The system orchestrates the full labelling workflow (routing, capture, validation, and dataset assembly) and replaces ad-hoc spreadsheet work with a repeatable pipeline that produces model-ready training sets with measurable quality.
Tracked
Quality
Scalable
Pipeline
Prod
Grade
Feedback Loop
Built-in
Background
The client is a London-based AI company building ML systems whose performance is bottlenecked by the quality of their training data. Their existing labelling work ran on ad-hoc spreadsheets and informal review. That was fine at small scale, but increasingly the limiting factor on model quality as they grew. They needed a production-grade workflow that could turn raw data into evaluable training sets, not just labelled rows.
Challenge
High-quality training data is not just labelled data. It is labelled data with measurable quality. That requires routing the right items to the right reviewers, capturing labels in a structured form that downstream training can actually consume, validating quality through inter-reviewer agreement and structured spot checks, and assembling everything into clean datasets with held-out evaluation splits. Doing this manually does not scale; doing it without a feedback loop wastes every correction a reviewer makes.
Approach
Routed raw data items to human reviewers with task batching and quality-aware assignment
Captured structured labels in a schema designed for downstream model training and evaluation
Validated quality through inter-reviewer agreement and structured spot checks
Assembled corrected output into datasets suitable for model training and held-out evaluation
Built the same human-in-the-loop feedback loop we use elsewhere, with reviewer corrections feeding back to improve the next round of model output
Deliverables
Labelling workflow orchestration covering routing, batching, and reviewer assignment
Structured-label capture aligned to the downstream training schema
Quality validation layer using inter-reviewer agreement and spot checks
Model-ready dataset assembly with held-out evaluation splits
Reviewer-feedback retraining loop turning corrections into measurable model improvement
Results
Inter-reviewer agreement and structured spot checks make training-data quality observable rather than assumed
Raw data turns into model-ready training sets through the same automated workflow every time
Replaced ad-hoc spreadsheet labelling with a production-grade workflow built for an AI-native client
Reviewer corrections feed back into the next training round, the loop that turns data into improvement
Impact
A repeatable pipeline that turns raw data into model-ready training sets with measurable quality, rather than ad-hoc spreadsheet work. This gives the client the data-quality foundation their model performance ultimately rests on.