AI expert on the National Defense Authorization Act

Objectives

The project set out to:

1. create a modular AI ensemble that could parse NDAA text accurately to identify critical mandates and budget data
2. reduce the time needed to retrieve and interpret NDAA information from weeks to hours
3. equip defense agencies with the ability to quickly locate mandates specific to their missions and incorporate them into operational plans

Methodology

Using advanced natural language processing (NLP), Talbot West created the virtual project manager on the Cognitive Hive AI (CHAI) framework. The system operates through modular components, each handling specialized tasks such as text summarization, mandate extraction, and context-based interpretation. This design allows the AI to adapt its processing to each agency’s language and unique requirements.

The AI reads the full NDAA text and automatically organizes it into accessible segments for rapid retrieval. By standardizing language, it bridges terminology differences across agencies, enabling relevant information retrieval using each agency’s own lexicon. Additionally, the AI is configured to be explainable, meaning each data retrieval or recommendation is traceable back to the specific text and context within the NDAA.

Results

The virtual project manager achieved three main outcomes:

1. From weeks of manual analysis to near-instant answers: By fully automating the parsing, analysis, and cross-referencing of the NDAA, the AI reduced the time to identify and interpret relevant authorities and mandates from weeks to minutes. This allowed agencies to swiftly access, validate, and act on crucial legislative information without relying on extended manual research.
2. Context-driven mandate interpretation: The AI’s context-aware capabilities enabled it to interpret mandates with specificity to each agency’s operational needs. Beyond simple data retrieval, the system mapped connections between related sections, detected implicit requirements, and resolved ambiguities, providing agencies with a precise, actionable understanding of their obligations.
3. Deployment across critical programs: The AI was successfully deployed across multiple high-stakes defense programs, including a submarine program that required synchronized compliance with mandates scattered throughout the NDAA. In this case, the AI identified and linked directives around personnel requirements, inter-agency collaboration, and reporting, which were essential for aligning the program with updated legislative standards and ensuring operational readiness.

Submarine program impact

One defense agency overseeing a submarine program used the AI to identify and connect three critical mandates embedded in different sections of the NDAA:

1. Employee requirements: The NDAA required that the agency maintain a specified employee count in a certain state until a specific date.
2. Expanded authority: Another section of the NDAA, many pages away from the first section, authorized the agency to collaborate with a new partner, allowing access to additional resources for submarine program development.
3. Reporting obligations: A third section, completely removed from the other two sections, added a new requirement to report to Congress on certain items not covered in the previous NDAA.

Finding and correlating these mandates manually would have been time-consuming and highly complex. The AI’s cross-referencing capabilities identified each mandate, connected them in context, and flagged them as relevant so the agency could align its program requirements with legislative updates. All mandates were met without delay, maintaining program compliance and operational readiness.

Challenges and solutions

1. Ambiguous language: Each agency within the DoD uses specialized terminology, which made standardized interpretation difficult. We embedded a linguistic pre-processing module to standardize terms so the AI could translate across specific agency language and accurately identify relevant mandates.
2. Dense, complex text: The NDAA’s structure and language posed challenges for even sophisticated NLP. To address this, Talbot West integrated agentic AI capabilities, providing the AI with context-awareness that allowed it to connect related mandates and interpret them accurately, even when they were dispersed throughout the document.

Lessons learned

This project demonstrated that modularity and explainability are critical in government AI applications. By using a modular system, Talbot West enabled the AI to interpret complex mandates flexibly and in alignment with each agency’s needs. Human-in-the-loop mechanisms provided quality control so staff could review AI outputs for accuracy while gaining familiarity with the system’s recommendations. This project served as a proof of concept, indicating one of the many ways AI can benefit the defense industry. 

Future implications

This AI-based approach to handling complex legislative documents like the NDAA could serve as a model for other government agencies that handle extensive regulatory or compliance documentation. With further development, similar systems could support other departments in meeting rapidly changing mandates. This implementation highlights one of many ways that AI can drive government efficiency.