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Axonis Launches Federated AI Architecture to Integrate AI Directly With Data

Red Hat

Axonis’ Federated AI Architecture Unveiled

Business Honor
11 December, 2025

Startup announces commercial launch of enterprise AI platform, focused on secure production for DoD and Intelligence applications.

Axonis is an AI Infrastructure Platform that allows companies to utilize Artificial Intelligence on their distributed, sensitive and real-time Production Data. As Axonis comes out of stealth mode today with an architecture that allows for the use of AI on any type of distributed data, it announces Todd Barr as the new CEO. Todd will help Axonis commercialize its architecture to support the enterprise market through the development of its architecture to support Defense Department hardened Services for enterprise use. Todd will work alongside both co-founders, David Bauer and Chris Yonclas, experts in Distributed Computing and AI and Machine Learning programming techniques for the Defence, D.A.R.P.A., and numerous agencies in US Government cloud, intelligence and Digital Transformation initiatives.

Axonis enhances and safeguards cloud storage and data lake investments by offering a secondary option to allow organizations to use unprocessed, real-time data directly upon ingestion, while still adhering to their existing centralized data strategy. "Companies are realizing very quickly that the biggest challenge the industry faces with regards to AI is not developing AI algorithms; it's actually deploying those algorithms to a production level. The technology is being addressed through architectural design by Axonis, and Todd has the skills and experience to provide a viable market solution at large volume."

Current lacks of scalability in bringing an AI model from a proof of concept into a production environment continue to exist for businesses today. A lot of the most highly valuable information being provided to organizations (including data from transactions, customers, logs, sensors, images, etc.) is unmovable to a central storage. In addition, the current state of the market with regulatory constraints, as well as cost, latency, and operational risk, has resulted in making traditional data centralization strategies unreliable; hence, the current impasse for businesses in executing a full-scale deployment of AI models into their workflows.