Enable intelligence analysts to rapidly analyze new unstructured data sources—including new human intelligence—in multiple languages.
Agolo captures the subject-matter expertise, institutional knowledge, and folklore of key missions. This enables hundreds of analysts to rapidly discover what they care about.
Agolo Al evaluates inbound unstructured human intelligence to identify and reconcile all references to people, organizations, programs, weapon systems, and technologies. By leveraging natural language processing, advanced name matching, and sophisticated contextual information, the key entities are organized into easily discoverable formats. Agolo has reduced need for manual intelligence processing and has enabled 30% greater capacity per analyst to be invested in higher value analytical activities
Agolo Entity Intelligence applies the collective knowledge of your analytical mission to resolve the identities in your multiple unstructured text. This self-learning system accommodates the variety and ambiguity of identity expression reducing false negatives (missing data) and false positives (incorrect matches). Entitiy types can include people, organizations, and places, but also custom entity types that are relevant to your data sourcesexamples includes weapon systems, part numbers, political topics, etc. Agolo enables custom entity type creation to meet unique analytical needs.
Transform ambiguous references and mentions in multilingual unstructured data to a high-fidelity data schema organized by entities in order to perform high quality analysis on discrete people, locations, and events in your multilingual data
Unstructured text is messy and the old adage of "Garbage in - garbage out" has never been more true than when working with text. By addressing the ambiguity and variety of how entities are expressed, data science analytics can define a high fidelity set of data specific to their topics of interest without introducing noise.
By organizing multilingual unstructured text into a high-fidelity data platform, data scientist can manage the risk of introducing irrelevant data into their processes. Agolo supports multiple languages, including European languages as well as Arabic, Russian, and Mandarin. This enables the platform to analyze a large volume of unstructured text sources and documents at high quality levels.
Automate researching and identification of redundant programs & initiatives, enabling Federal Agencies to minimize waste in budgeting processes
Many federal agencies have information about different programs spread across thousands of different documents. New funding requests often overlap with current programs and technologies. Researching and identifying overlapping programs and technologies is often a time-consuming, error-prone analytical task that often leads to false negatives. Automates identification of redundant programs & initiatives, enabling DoD to minimize waste in budgeting processes.
Agolo's Al technology dives deep into your digital haystacks, using context to accurately identify entities in texts and documents. Its unique 'ghost entity' feature ensures that no new or previously unidentified entity goes unnoticed, effectively reducing false-negatives. Simultaneously, by understanding different representations of the same entity, it cuts down on false-positives. The result? A more comprehensive, streamlined, and efficient eDiscovery process for your investigations. Say goodbye to overlooked data and productivity pitfalls. Say hello to precision with Agolo.