AI-Enabled “Just-in-time” Knowledge Delivery for Naval Maintainers

SBIR topic DON27BZ02-NV021 (U.S. Department of Defense – Navy), Phase I and II. Open to U.S. small businesses. Opens: December 2, 2026. Closes: January 6, 2027. This effort is aimed at addressing the critical operational challenge of maintaining increasingly diverse and technologically complex arrays of systems with generalist knowledge. The solution requested in this SBIR topic is a just-in-time knowledge delivery system for Fleet maintainers that tells them what they need to know, when they need to know it. It would provide detailed procedures and technical knowledge for diagnostics, repair and maintenance procedures on demand, both ashore and at sea, where there will be limited access to remote data stores. The system will employ generative AI to analyze authoritative technical documentation and historical maintenance records, at both the Fleet and individual asset levels, and generate summarized technical guidance initiated by free-text queries or guided diagnostic dialogues. Post-task, the system will prompt the technician for repair feedback, synthesizing their insights to update the database with both general maintenance guidance and individual asset histories. Problems with current knowledge delivery are particularly acute when new systems and technology are introduced to the Fleet, such as with the introduction of unmanned systems (UxS), and when locally developed knowledge needs to be propagated across the larger maintenance community. Operational readiness is currently bottlenecked by an inefficient maintenance paradigm: generalist maintenance technicians constantly face unfamiliar equipment without access to historical repair data, while frequent duty rotations continually erase localized (tribal) knowledge. This over-reliance on a transient pool of specialists causes extended system downtime and degrades operational readiness. These problems are particularly acute when new systems and technology are introduced to the Fleet. While new advances in…