Data Acquisition & Preparation

AI models learn from data, so the quality and nature of that data shapes everything the model can do. Training data comes from many sources: web scraping, licensed datasets, human annotation, synthetic generation, sensor feeds, and user interactions. Each comes with trade-offs around cost, quality, coverage, and legal risk. But collecting data is only the beginning. Making raw data usable requires significant work - cleaning, filtering, deduplicating, formatting, and labelling - that is often the most time-consuming and expensive part of building an AI system. The choices made here ripple forward in ways that are difficult to undo: biased data produces biased models, gaps in coverage create blind spots, and poor labelling teaches the model the wrong things. Despite this, data preparation rarely gets the attention it deserves. Understanding what goes into the data behind an AI system is one of the most practical things you can do to evaluate its trustworthiness.