MythWorx launched TrainingWorx℠, a modular approach to training large-language models (LLMs), and announced an upcoming invite-only guided preview giving select organizations early access. According to the company, traditional AI forces enterprises to invest millions of dollars in infrastructure or fall behind, and TrainingWorx℠ eliminates this constraint through a modular expert architecture that reduces cost while increasing flexibility, scalability and performance. CEO Jason Williamson said TrainingWorx℠ enables enterprises to train advanced AI models on their own proprietary data without the cost, complexity and risk of traditional training. The company said traditional training updates billions of parameters simultaneously, resulting in massive infrastructure costs, rigid architectures and systems prone to hallucination and knowledge degradation during updates, while TrainingWorx℠ uses a modular system of specialized experts developed sequentially, aligned efficiently and deployed as a single, high-performance system, reducing training costs by more than 90%. TrainingWorx℠ trains one expert module at a time on domain-specific data using as few as 1 to 2 GPUs, with experts coordinated through a lightweight routing system during inference; updates are isolated in individual experts, avoiding “forgetting,” and new domains can be added incrementally rather than via full retraining. Features include full audit and reasoning chains and the ability to run on cloud, on-premises, or at the edge with minimal hardware.