Nola Ai

Nola AI is an artificial intelligence technology company founded in 2023 and headquartered in New Orleans. The company's co-founders are Mitchell Klein (Chief of Operations), Correy Kowall (Chief Scientist), and Damon Kirin. The team's work is guided by Chairman of the Board, Scott Kauffman. Chief Scientist Correy Kowall's background includes over three decades of designing neural networks and early collaborations with AI pioneers.

The company develops advanced AI research and development tools aimed at improving machine cognition and scalable intelligence. Nola AI's core offering is its proprietary 'Atomic Framework', a hybrid architecture that integrates epistemic reasoning and Theory of Mind principles to create AI models that are faster and more cost-effective. This framework is designed to reduce common AI issues like hallucinations by enabling a deeper contextual understanding. The company’s introductory product, 'Atomic Speed', is an optimization technology that significantly cuts down AI model training time and compute costs for enterprises, startups, and academic labs. It functions by altering the learning process itself, reducing the number of training steps by 2-4x and cutting per-step compute time by over 50% without degrading model quality. 'Atomic Speed' is optimizer-agnostic and can be implemented in existing machine learning pipelines without requiring changes to model architecture or code.

Nola AI's business model centers on democratizing AI by making sophisticated tools accessible to organizations of all sizes. In June 2025, the company opened a private beta for 'Atomic Speed' to allow select users early access. In July 2025, Nola AI secured $1 million in a seed funding round, with participation from Louisiana-based Boot64 Ventures, to accelerate research and development and scale operations.

Keywords: AI optimization, machine learning efficiency, model training, compute cost reduction, epistemic reasoning, Theory of Mind AI, neural network optimization, Atomic Speed, Atomic Framework, enterprise AI, scalable AI, AI development tools, deep learning, algorithmic innovation, AI hallucination reduction, model convergence, GPU cost savings, AI research, contextual AI, cognitive AI