Datavault AI (NASDAQ: DVLT), a company specializing in instant data monetization and enterprise digital twins, announced an expanded collaboration with IBM to deliver enterprise-grade artificial intelligence performance at the edge in New York and Philadelphia. The deployment will utilize IBM watsonx AI products running within SanQtum AI's zero-trust, micro edge data center network operated by Available Infrastructure.
The implementation aims to provide cybersecure data storage and compute capabilities, real-time data scoring, tokenization, credentialing, and ultra-low-latency processing across two of the most data-dense metropolitan regions in the United States. This infrastructure will support enterprise AI workloads without requiring dependence on public cloud infrastructure, addressing growing concerns about data sovereignty, latency, and security in AI deployments.
The SanQtum AI platform's architecture represents a significant shift in how enterprises can approach AI implementation. By leveraging edge computing infrastructure in strategic metropolitan areas, businesses can process sensitive data closer to its source while maintaining enterprise-grade security protocols. This approach is particularly relevant for industries requiring real-time data processing, including financial services, healthcare, and logistics operating in these high-density urban centers.
Datavault AI's technology suite includes the Information Data Exchange (IDE), which enables Digital Twins and licensing of name, image, and likeness by securely attaching physical real-world objects to immutable metadata objects. The company's platform serves multiple industries, including HPC software licensing for sports & entertainment, events & venues, biotech, education, fintech, real estate, healthcare, and energy sectors.
The expanded collaboration builds on existing partnerships between the companies and reflects the growing market demand for distributed AI infrastructure. As enterprises increasingly adopt AI technologies, concerns about data privacy, latency, and cloud dependency have prompted exploration of alternative deployment models. The edge computing approach demonstrated in this collaboration could influence how other technology providers structure their AI offerings, particularly for applications requiring immediate data processing or operating under strict regulatory environments.
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