Leveraging AI/ML for Optimizing JAG Data, Decision Making, and Military Justice Outcomes - SBIR Topic DAF26BZ03-NV018
Funding Amount:
Est. $250,000
Deadline to Apply:
July 22nd, 2026
Objective:
The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.
This SBIR Phase I initiative aims to explore and validate AI/ML solutions specifically for enhancing the data management, decision-making, and judicial outcomes within the Air Force District of Washington Judge Advocate (AFDW JA). The primary objective is to prototype advanced AI/ML techniques that can address the unique challenges associated with JAG data lifecycle and operational demands. This includes:
Optimizing JAG Data Management: Employing AI/ML for automated document classification, metadata extraction, and intelligent eDiscovery to streamline evidence review processes.
Enhancing Decision-Making: Utilizing predictive analytics for case forecasting, resource allocation, and bias detection to ensure equitable and efficient legal rulings.
Improving Military Justice Outcomes: Implementing generative AI for rapid advisory responses, anomaly detection in compliance pipelines, and reinforcement learning for workflow improvements.
The aim is to develop capabilities that bolster data integrity, mitigate risks in critical areas such as military justice proceedings, and support the AFDW JA's mission to provide timely, accurate legal counsel. These efforts will align with Department of War ethical AI principles, ensuring secure data handling on IL5-compliant platforms, and adhere to the guidelines set forth in Air Force Doctrine Note 25-1 for responsible AI integration.
ITAR:
The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.
Description:
The legal and judicial data management processes within the Air Force District of Washington Judge Advocate (AFDW JA) are currently labor-intensive and prone to delays due to the reliance on manual reviews and updates. These challenges hinder the timely delivery of accurate and independent counsel required for contingency responses, ceremonial honors, and global operational support. This Phase I Small Business Innovation Research (SBIR) topic aims to address these issues by introducing innovative AI/ML solutions that automate and expedite AFDW JA's workflows, thereby enhancing mission effectiveness and efficiency.
Key areas of focus include:
Military Justice under the UCMJ: Automating the intake and processing of judicial and legal case data through advanced AI techniques such as natural language processing for automatic document classification, metadata extraction, and anomaly detection.
Contracting Oversight: Leveraging predictive modeling techniques to improve data quality, accuracy, and mitigate bias in decision-making processes.
Interagency Civil Support: Utilizing generative AI for rapid query responses and reinforcement learning to optimize workflow efficiencies, along with advanced data visualization tools to provide actionable insights and facilitate stakeholder briefings.
The desired outcome of this project is to transform the unstructured data overload into actionable intelligence, thereby reducing processing delays by 50-70% and significantly enhancing overall legal advisory excellence. The proposed AI/ML solutions must comply with Air Force standards and Department of War ethical AI principles, ensuring secure and explainable outputs that are compatible with existing Air Force data platforms. This will support seamless integration and scalability across the National Capital Region and worldwide missions.
The initial Technology Readiness Level (TRL) at the project start is TRL 3, with the aim to achieve TRL 6 by the end of Phase II. The efforts required to achieve these objectives include:
Phase I: Conducting feasibility studies, developing preliminary AI/ML models, and validating the core functionalities through prototype testing. Minimum deliverables include a detailed feasibility report, initial prototype/wireframe demonstration, and a comprehensive plan for Phase II development.
Phase II: Refining and expanding the AI/ML models based on Phase I results, conducting extensive testing in operational environments, and finalizing the integration with existing AFDW JA systems. Minimum deliverables include a fully functional AI/ML solution, detailed testing and evaluation reports, and documentation for system integration and user training.
By implementing these advanced AI/ML solutions, the AFDW JA will enhance its capability to provide timely and accurate legal counsel, aligning with the strategic objectives of the Air Force District of Washington and ensuring operational readiness and effectiveness.
PHASE I
Phase I aims to explore AI/ML analytics, predictive models, and visualizations for AFDW JA's data handling and decision processes. The focus is on theoretical design validated by simulated data, addressing issues such as data quality and predictive modeling. Deliverables include a technical report, conceptual prototypes, and a transition plan. Success is measured by conceptual innovation and alignment with AFDW JA's operational needs.
PHASE II
In Phase II of this Small Business Innovation Research (SBIR) effort, participants will advance the conceptual framework from Phase I into a functional prototype of the AI/ML solution, conducting rigorous development, integration, and validation testing to demonstrate operational viability for Air Force judicial and legal case management use cases. Building on synthetic or de-identified datasets representative of military justice proceedings, contracting oversight, and operational legal support scenarios, the prototype must exhibit end-to-end performance in automating data ingestion, quality enhancement, and decision support, while quantifying impacts on AFDW JA's mission objectives for improved data quality, accuracy, and timeliness in delivering independent counsel. The prototype shall implement automated ingestion pipelines for large-scale, unstructured judicial and legal data volumes, integrated with machine learning-based data quality issue detection—such as real-time identification of missing values, inconsistencies, or biases—and deploy predictive modeling and imputation techniques (e.g., generative adversarial networks or ensemble-based methods) to resolve gaps and boost data integrity for enhanced decision-making. Participants must also integrate interactive data visualization interfaces to dynamically display pre- and post-imputation analytics, including metrics on completeness, accuracy improvements, and derived insights for judicial process reviews. Testing will occur in simulated operational environments to evaluate scalability, interoperability, and robustness against edge cases, with all elements adhering to Department of War ethical AI principles and Air Force standards for secure, explainable AI, ensuring compatibility with existing data ecosystems. Deliverables include a working prototype, comprehensive validation reports with empirical benchmarks (e.g., imputation accuracy >90%, processing efficiency gains >50%), user feedback from AFDW JA stakeholders, and a detailed Phase III commercialization and transition strategy to support full-scale deployment across the National Capital Region and worldwide missions.
PHASE III DUAL USE APPLICATIONS
In Phase III, the validated AI/ML solution from Phase II will transition to full-scale production, deployment, and commercialization, integrating into AFDW JA's operational systems for real-world application in military justice, contracting, and contingency support, with DoW funding or non-SBIR contracts facilitating rapid fielding. This phase emphasizes scalability, user training, and sustainment to achieve enterprise-wide efficiencies, such as 70%+ reductions in data processing times and enhanced equity in outcomes. Dual-use potential extends to civilian sectors, including federal/state courts and private law firms, where the technology can automate legal data management, predictive case analytics, and compliance reporting, fostering partnerships for broader market adoption in judicial tech and legal AI services.
Who will win?
If you can achieve the objective above better than any other company on the market, you have a very high-likelihood of success and should apply.
Who is eligible to apply?
Any company that meets the following criteria:
For-profit company
U.S.-owned and controlled.
500 or fewer employees (including affiliates)
How Can BW&CO Help?
1) End-to-end support including, strategy, writing of the full proposal, and administrative & compliance support.
2) Proposal strategy and review.
3) Administrative & compliance support.
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