The "A25D-002: Artificial Intelligence for Interoperability" topic is part of the Small Business Innovation Research (SBIR) program, aiming to utilize Large Language Models (LLMs) and other Artificial Intelligence (AI) approaches to improve system integration and address data interoperability challenges, particularly within tactical environments. The core objective is to enhance the integration of disparate systems and unify data, regardless of the source system, target system, or format. This initiative primarily targets improving the interoperability of various military systems, enabling more efficient data exchange and reducing redundant or outdated systems.

The Department of Defense (DoD) has identified several goals related to this project, including reducing redundant systems, bridging gaps between DoD and mission partner standards, and streamlining data flow. AI methods, especially LLMs, are seen as a key enabler in achieving these goals. The DoD recognizes that in tactical environments, various warfighting systems operate with different databases and components, creating barriers to seamless interoperability. The use of LLMs can potentially bridge these gaps and ensure consistent, standardized data flow across systems. This initiative supports the Army’s AI leadership goals and focuses on optimizing AI applications for improved system performance in military settings.

The SBIR program will be conducted in three phases, each focusing on different levels of research and development. Phase I proposals can receive up to $250,000 for a six-month period to research and document techniques for AI training in software system integration, including API development, data models, and message mapping. Phase II will involve a concept demonstration to show how interoperability can be enhanced between two or more traditional systems, using the proposed AI techniques. In Phase III, dual-use applications are explored, where the technology could be adapted for use in finance, customer support, cybersecurity, and healthcare sectors.

Proposals must focus on the integration of AI with traditional software systems and be measured against specific data interoperability metrics that will be identified in Phase I. The ultimate goal is to improve the efficiency of data flow in tactical military operations, allowing systems to communicate seamlessly despite differences in data format and system architecture. The DoD aims for this project to contribute to its broader strategy of improving data interoperability, reducing system redundancies, and enhancing overall operational efficiency.