Everyone wants to use AI but making it useful is harder than it looks. At ARLIS, we focus on Technical Language Processing (TLP) because it delivers lasting, mission-relevant capabilities when grounded in the realities of human work, policy constraints, and domain complexity.
Natural Language Processing (NLP) models, such as Large Language Models (LLMs) promise a lot, but without deep understanding of technical domains, they often fall short. TLP goes beyond general NLP - it’s about applying AI to specific, highly structured problems like analyzing maintenance logs, scanning classified documents for declassification purposes, or dissecting complex system reports - where language has meaning only insiders understand.
Off-the-shelf tools cannot always deliver that. You need models built and tuned with domain-specific context in mind. The value is not just in automation, it’s in amplifying human expertise and extracting insights from data that was previously unusable, often buried in analog records, handwritten logs, or legacy systems.
In certain domains, such as declassification, despite advances in AI, policy dictates that we are not at a point where machines can make the final decisions. Declassification requires a balance between managing transparency, by releasing previously classified information to the public, with risk, by protecting information that needs to remain classified for national security. To ensure a proper balance is struck, the outcome of these algorithmically-derived decisions must be verified by humans before final release.
But that’s not a bottleneck: it’s an opportunity.
The best outcomes come when humans and machines trust each other. If they don’t, they cannibalize each other’s functions and effectiveness where a human may override an algorithm incorrectly or vice versa because they are not performing the right tasks. TLP is designed to optimize that collaboration. It frees analysts from tedious work (like scanning thousands of pages for specific terms) so they can focus on judgment, insight, and action. AI can assist in letting people do what they do best.
ARLIS researchers understand the intersection of technology, policy, and mission. Our role is to translate between cutting-edge tools and real-world problems. We’re not here to build the final product for mass use, but to solve the hard, foundational problems that make good tools possible.
Doing this right requires knowledge of the policy landscape, the workflows analysts live in, and the technical details of how AI works. ARLIS has extensive experience working at the intersection of these areas, making us uniquely positioned to guide responsible, effective use of language technologies across government missions.
We’ve helped agencies modernize government records management by developing AI tools that streamline the review, tagging, and declassification of federal records - making critical information more accessible and secure. With a 71 million document backlog for declassification and the growing digital tsunami, these tools are needed now more than ever before. Offices involved in declassification review need every tool available to efficiently complete their missions. We've also explored how to extract insights from traditional maintenance records, advancing sustainment of DoD platforms and supporting declassification modernization.
We are able to help ensure tools can move from research to operational use and solve the foundational gaps that keep AI from scaling.
In short, ARLIS helps mission owners get the right tool for their problem, not just the most advanced one. That means finding value in existing data, applying AI thoughtfully, and always keeping the user at the center.