A Real-Time ISR Platform Turns Open-Source Data Into Actionable Intelligence for a DoD Analytic Center

Planning Against Adversaries Without Reading What Is Already Public

Left unsolved, the gap has a directional cost. The center’s role is to collect, analyze, and disseminate intelligence supporting military operations, mission planning, and decision making, and the fastest-moving part of the evidence base for that work is open. Without a way to ingest and interpret Publicly Accessible Information as Big Data, assessments of threats and of regional security cooperation environments are built on the fraction of the record that happens to be readable by hand.

Timing is the other cost. Near real time Intelligence, Surveillance and Reconnaissance is what makes rapid response to emerging threats possible, and it is what turns situational awareness into operational effectiveness. Analysis that arrives after the fact still describes the civil environment accurately; it just describes it too late to detect, observe, analyze, and assess in time to act. For work aimed at bringing stability to partnered nations while exploiting weaknesses in competitors and adversaries, late is close to unused.

Big Data Volume, Many Languages, and a Spatial Question Underneath

Three constraints shaped the build at once. The first is volume: the models had to gather and organize information sourced from hundreds of thousands of news articles, files, documents, and both public and private databases, which rules out any approach that depends on human triage as the first filter. The second is language: usable output required sentiment analysis, language translation, and frequency analysis together, because a stability index for various countries cannot be calculated from English-only, unweighted text.

The third constraint is that the answers are geographic and relational at the same time. The center needed to see spatial relationships and entity relationships in the same picture, which is why the system pairs graph analytics with geospatial analytics rather than choosing between them. Layered over all three was the requirement that the analysis stay anchored: with a corpus this large, retrieved evidence has to remain tied to the most pertinent source material so the output does not drift into unsupported synthesis.

A Builder That Could Put Machine Learning, Graph, and Geospatial in One System

The requirements defined an unusual vendor profile. The work needed machine learning and Natural Language Processing depth to build and train the collection and modeling layer; generative AI and Large Language Model experience to read a large textual corpus and simulate potential threat scenarios; database engineering across both document and graph stores; and genuine Esri geospatial capability, because the outputs are maps, link charts, and interactive narratives rather than reports. Very few teams carry all four, and splitting them across vendors would have put the integration risk on the center.

The center engaged i3solutions, and the engagement record shows the profile held: over 20 machine learning models built and trained for the system, and the first ever deployment of ArcGIS Knowledge within the Department of Defense (DoD).

i3solutions developed an international security assessment and analytics prototype for Intelligence, Surveillance and Reconnaissance: one system that bridges the gap between the overwhelming volume of Publicly Accessible Information and actionable intelligence, providing near real time ISR to detect, observe, analyze, and assess the civil environment.

The prototype moves Publicly Accessible Information from raw volume to decision support in one pass. Over 20 machine learning models, built and trained by i3solutions, collect and collate information from hundreds of thousands of sources, and the Natural Language Processing layer performs sentiment analysis, language translation, and frequency analysis to calculate the stability index of various countries. Generative AI and Large Language Models work above that layer: generative AI simulates potential threat scenarios, providing the center with foresight into future challenges, while LLMs extract meaning from the textual corpus, and Retrieval Augmented Generation keeps the resulting analysis anchored in the most pertinent source material. Storage is split by shape rather than forced into one engine. MongoDB holds the overall data catalogue and tabular formats such as CSV in flexible, schema-less documents; Neo4j manages the graph-based data. Esri ArcGIS Knowledge then integrates graph and spatial analytics to support data discovery, collaborative investigations, link analysis, and information sharing, with ArcGIS Pro producing dynamic maps and link chart analysis and ArcGIS StoryMaps carrying the country-level stability narrative.

Near-Real-Time Intelligence From Open Sources

More than 20 machine learning models now gather and organize open-source intelligence for detection and analysis that wasn’t possible before.

The center’s assessment capability changes shape first. By enhancing assessment capabilities, the system gives it a clearer understanding of the dynamics, capabilities, and intentions of rival Great Powers, so it can anticipate potential challenges, capitalize on emerging opportunities, and shape its policies and responses. The comparative instrument behind that is concrete: a stability index per country, calculated by NLP sentiment analysis, language translation, and frequency analysis over the ingested corpus. Artificial intelligence and machine learning algorithms process and analyze complex data sets rapidly, which is how analysts identify patterns, correlations, and emerging trends that traditional methods leave buried, and generative AI simulates potential threat scenarios so a scenario can be read before it happens.

Delivery changed as much as analysis. i3solutions’ intervention led to significant advancements in geospatial analysis using Esri’s tools, and the specific artifacts are the point: ArcGIS Pro produces the dynamic maps and the in-depth link chart analysis, ArcGIS StoryMaps carries the country-level stability indicators as interactive narratives, and ArcGIS Knowledge holds the graph and spatial layers together, so spatial relationships and patterns are visible and geographical insight applies directly to resource allocation and risk assessment. The system aggregates and synthesizes data from many sources through advanced LLM use, transforming raw textual data into actionable intelligence and surfacing insights and trends that were previously hidden, which improves both operational efficiency and agility. It also provides real-time monitoring of key developments and events related to Great Power Competition, so a shift in the open record reaches the center as a geospatial display, a timeline, or a network graph while there is still time to act on it.

Publicly Accessible Information Turned into Near Real Time ISR, in One System

The engagement established that the open record can be made into an analytic asset rather than a backlog. Over 20 machine learning models, built and trained by i3solutions, gather and organize information from hundreds of thousands of news articles, files, documents, and both public and private databases, and Natural Language Processing turns that material into sentiment, translation, and frequency analysis behind country stability indexes.

Generative AI, Large Language Models, and Retrieval Augmented Generation read the corpus and keep the analysis anchored to its most pertinent sources. MongoDB and Neo4j hold the catalogue and the graph, and Esri ArcGIS Knowledge, ArcGIS Pro, and ArcGIS StoryMaps put the result in front of analysts as geospatial displays, timelines, network graphs, question and answer search, and baseball cards. Along the way the engagement produced the first ever deployment of ArcGIS Knowledge within the Department of Defense.