As humans, when we look at a scene our first impressions are sometimes wrong; we need to take a second look, to squint and reassess. Squinting enables us to focus our attention on the subject we are investigating and often clarifies and corrects our initial assumptions. Computer vision algorithms, including those enabled through artificial intelligence and machine learning pipelines, can also be made to squint. As the role of AI/ML algorithms in automation matures, the cost of mistakes will increase dramatically. Consider use cases in the healthcare industry, cancer diagnosis and research and autonomous systems. In this presentation we show how the Squint Insights Studio platform uses explainable AI to add context and reasoning to vision model decisions, enabling the user to build vision pipelines that continuously assess and revise vision model predictions in production environments.