SBIR/STTR Research Award

Edlore AI/ML Maintenance On-Prem Platform

Award identifier a938a481b89451d1321f7c5113d7d0afa3044a66a727179c25bae2f0a3f2e6caAwarding agency Department of Defense · Air Force

Award amount

$1,749,000

Program and phase

SBIR · Phase II – Direct to Phase II

Award year

2025

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Award details

Awarding agency
Department of Defense
Branch
Air Force
Recipient company
EDLORE INC.
Proposal award date
2024-10-10
Contract end date
2026-10-09
UEI
EEZFAUUEFEK3
Solicitation number
24.2
Topic code
AF242-D013

Research abstract

The proposed project aims to develop an advanced AI/ML-driven on-premise maintenance platform specifically designed to operate without internet connectivity, addressing the critical need for reliable, efficient maintenance solutions in environments with limited network access. The platform will significantly enhance the capabilities of maintenance personnel by providing real-time access to comprehensive, AI-driven maintenance procedures and 3D visualizations, improving both the accuracy and efficiency of maintenance operations.This AI/ML integration will utilize advanced machine learning models and natural language processing (NLP) to analyze and interpret procedural data from Technical Data (T.D.s). These AI/ML capabilities will enable the platform to provide precise, context-aware maintenance guidance and predictive maintenance insights. The on-premise functionality is designed to ensure uninterrupted operation in low-connectivity environments, making it ideal for Department of Defense (DoD) depots and logistics centers where network reliability is often an issue.The platform will feature an intuitive user interface optimized for use on tablets, allowing maintenance personnel to create, modify, and visualize maintenance procedures directly in the field. This interface will incorporate advanced 3D visualization tools to help users better understand and execute complex maintenance tasks. The platform will also support the integration of maintenance procedures from multiple manufacturers, enabling a unified system for managing diverse equipment and ensuring comprehensive coverage of all maintenance needs.Extensive testing will be conducted to ensure the platform's reliability and effectiveness in real-world scenarios. The project includes rigorous testing phases to validate the AI/ML models and the overall system performance. Comprehensive training and documentation will be provided to ensure smooth adoption and effective use by maintenance personnel. This training will include detailed user manuals, hands-on training sessions, webinars, and an online knowledge base for ongoing support.The anticipated result is a fully functional, AI/ML-driven maintenance platform that operates on-premise, providing DoD personnel with the tools they need to perform maintenance tasks more efficiently and accurately. This platform will not only improve current maintenance operations but also lay the groundwork for future advancements in maintenance technology. The successful implementation of this project will demonstrate the feasibility and benefits of integrating AI/ML technologies into on-premise maintenance systems, paving the way for broader adoption across the DoD and other sectors with similar needs.The development and deployment of this platform will leverage Edlore’s extensive experience and expertise in AI, ML, and digital maintenance solutions, ensuring a high-quality, robust solution tailored to the specific requirements of DoD maintenance operations.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

5 SBIR/STTR records share this UEI (2019–2025). Reported award amounts total $3,101,692. Reported amounts are not federal obligations.

Phases: Phase I, Phase II – Direct to Phase II

Agencies: Department of Defense

Agency intelligence

Department of Defense

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