Ukrainian military analysts are trialing a new artificial intelligence tool designed to speed up the process of collecting and sorting online open-source intelligence related to Russian military activity. The system, known as “ODCR Assistant,” is being developed by the Boryviter defense innovation center in Kyiv.
The tool scans a large volume of open-source content—ranging from social media posts by Russian soldiers to defense-focused blogs and public forums—and sorts the data according to NATO’s ODCR framework (Observation, Discussion, Conclusion, Recommendation). The goal is to help analysts identify useful information more efficiently while reducing the time spent reviewing low-priority content.
The ODCR Assistant applies initial filters to incoming data, flagging posts as either relevant or irrelevant. Relevant items are marked green and then further sorted based on novelty and potential value. The system also learns from user feedback, gradually improving its ability to highlight important content, according to Militarnyi.
The AI does not operate independently. Analysts are still required to review results and interpret findings, especially in a military context where nuance and source reliability are critical. The platform remains in a pilot phase and is currently limited to publicly accessible sources.
In parallel with ODCR Assistant, the team is also developing a second tool similar in structure to ChatGPT. This AI model is intended to assist Ukrainian officers in drafting reports and recommendations quickly, offering structured support in decision-making processes during field operations.
Open-source intelligence (OSINT) has played a major role throughout the Russia-Ukraine conflict, as well as other conflicts around the world. The integration of AI into such workflows and automating portions of the collection and analysis process could provide Ukraine’s defense units with faster access to actionable insights, especially as online channels continue to be a significant source of battlefield information.
As these systems mature, the focus remains on combining AI efficiency with human oversight to ensure reliability in high-stakes environments.
