The technology gap in data science is a significant issue, with over 85% of data science projects failing. Existing technologies do not empower most data scientists to develop applications at scale, requiring them to learn and apply parallel programming for compute clusters. Bodo aims to solve this problem by providing an auto-parallelization platform that allows data scientists to focus on solving the business problem instead of rewriting their Python code in various languages and parallel libraries. This platform provides automatic-parallelization and High-Performance Computing (HPC) capabilities, democratizing HPC and allowing data scientists to work at scale. Bodo's solution offering includes simplicity, productivity, performance, scalability, quality, aggregate cost, and adaptability, providing unprecedented productivity, performance, and scalability with lower infrastructure costs. The platform is designed for Pythonic workloads and has plans to be ported and optimized on GPUs and FPGAs, following the path of least resistance towards simplicity, performance, and cost-effectiveness.