Inverse problems arise when the quantity of interest cannot be measured directly, but must be estimated from indirect, noisy, incomplete, or degraded data. In scientific imaging, this includes tomographic reconstruction for medical imaging and materials science, denoising, deblurring, and inpainting. Although these applications look different, they share a common mathematical structure: recover an unknown image or volume by balancing agreement with the measured data against prior knowledge. This makes optimisation a central tool for solving inverse problems, but also creates a software challenge. How do we translate mathematical formulations into working code? How do we test different regularisers, constraints, and solvers? How do we compare optimisation strategies fairly and make experiments reproducible across datasets and applications? This talk presents the Core Imaging Library (CIL), an open-source Python framework for optimisation-based workflows in inverse problems. CIL provides composable tools for combining data readers, operators, objective functions and algorithms into transparent computational pipelines. The talk will show how CIL helps users prototype new methods, compare algorithms, and build reproducible workflows for applications in medical imaging, materials science, and other data intensive imaging modalities.