The challenge
A healthcare organisation had a large corpus of source material, including books, articles, clinical text and patient resources, that needed preparing for generative AI training. Raw documents carried numerical data, URLs, headers and footers, diagram captions, stylistic inconsistencies, first-person narrative, and brand references that all needed normalising before training.
What we did
Developed a preprocessing pipeline that automated the heavy lifting while routing ambiguous content to a human-review queue for quality assurance.
The outcome
Provided a high-quality text corpus prepared for generative-model training, enabled downstream generative tasks with clean, consistent input, and formed the backbone for enterprise-grade NLP modelling with data readiness, governance and consistency built in.
Turned a messy clinical and patient-resource corpus into clean, consistent, governed training data for a generative AI assistant.
The pipeline stripped structural noise (headers, footers, captions) and normalised inconsistent style and voice automatically, escalating only genuinely ambiguous passages to a human-review queue before the corpus was released for training.