Preparing reliable data
for production AI
Data preparation consumes the majority of AI time and budget in most organisations yet it’s treated as an afterthought. This white paper details how My Data Machine helps companies build reliable, scalable, and production-ready AI data pipelines across collection, cleaning, enrichment, annotation, and RLHF.
A practical guide for teams ready to stop firefighting their data and start industrialising it.
Operating across Retail, Security, and Satellite Imagery, My Data Machine combines a Franco-Indian team of data scientists and engineers with Human-in-the-Loop workflows delivering the annotated datasets and feedback loops that production AI actually requires.
The context
Most AI projects stall not because of model limitations, but because of fragile data foundations — inconsistent labels, ungoverned pipelines, no versioning, and annotation workflows that don’t scale. Without a structured data preparation partner, internal teams spend the majority of their time on repetitive tasks instead of shipping models.
The challenge
Organisations needed a specialist partner capable of handling the full data lifecycle — from targeted collection and systematic cleaning, to expert annotation and RLHF feedback loops — while maintaining traceability, quality control, and GDPR compliance across demanding sectors like healthcare, security, and satellite imagery.
