Challenge
AI projects in the pharma and life sciences sector often stall before they start — not because of poor algorithms, but because of poor data.
Models trained on inconsistent, noisy, or incomplete labels fail to generalize, especially when dealing with complex text like clinical trial documents, adverse event reports, or medical literature.
Our Solution
We built an end-to-end data labelling framework designed specifically for regulated industries where data quality, transparency, and auditability are non-negotiable.
Features
- Created detailed annotation guidelines with examples and edge-case handling (e.g., abbreviations, dosage ranges, negations).
- Used pre-trained NER models to generate auto-suggestions that annotators could accept or correct
- Deployed active learning loops to prioritize samples where model confidence was lowest — focusing human effort where it mattered most
Benefits
- Reusable Gold Standard: Centralized, version-controlled dataset for continuous AI development
- Accelerated Model Training: Clean and consistent labels reduce training iterations
- Improved Data Governance: Transparent workflows and role-based validation ensure accountability
- Lower Operational Cost: AI-assisted labelling cuts manual effort while improving accuracy
- Regulatory Confidence: Every label traceable to its source, reviewer and change history
Tech Stack
Label Studio
