‍The rise of big data analytics and machine learning is transforming businesses, but it comes with significant technical debt due to the increasing computing cost, time, and energy consumption. To address this, Bodo aims to improve analytics performance by 1,000x, decrease learning curves to 1/10, and reduce aggregate operational expenses costs to 1/10 using existing programming techniques and hardware. With its Series A funding, Bodo is committed to democratizing access to inexpensive, near-real-time large-scale analytics, enabling new data-centric revenue opportunities, faster competitive responses, and radically more efficient overall data operations. By eliminating weeks-long machine learning development-to-production cycles and scaling models on the same day they are developed without code rewrite, Bodo makes it possible for teams to get business insight from billions of customer entries daily instead of monthly or quarterly reports. The company's vision is to develop computing solutions that do not compromise on cost, speed, scale, or simplicity, and has achieved this by enabling supercomputing-style MPI parallel performance in native Python. Bodo's solution addresses the hidden technical debt by providing an unprecedented extreme-performance parallel compute platform for data analytics and ML that can showcase linear scaling beyond Terabytes of data and 10,000’s of cores with exceptional efficiency.`‍