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Federal, Defense & Intelligence

Enable intelligence analysts to rapidly analyze new unstructured data sources—including new human intelligence—in multiple languages.


Use Case 1: Enable Intelligence Analysts to Rapidly Analyze New Unstructured Data Sources

For: Intelligence Analysts and Data Scientists

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.

  • Unstructured text is messy. Analysts know what they are looking for -- people, organizations, places, programs, weapon systems, and technologies, etc -- but finding these identities can be challenging, especially when dealing with ambiguity, variability, and content in multiple languages. Agolo offers a next-generation to this problem. Agolo has reduced the 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 data sources and text. Agolo is a self-learning system that accommodates the variety and ambiguity of identity expression, reducing false negatives (missing data) and false positives (incorrect matches).

    Agolo enables both common identity likes people, organizations, and place as well custom entity types. Example customer entities include weapon systems, part numbers, political topics, etc. Agolo enables custom entity type creation to meet unique analytical needs.

Use Case 2: Elevate Cross-Lingual Data Fidelity

For: Analysts, Data Scientists, Architects

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, places, programs and events.

  • Garbage in - garbage out. Agolo solves the problem of data ambiguity and multi-lingual text obscurity by looking at information within its contextual expression and learning from it, just like a human analyst would.

  • By organizing multilingual unstructured text into a high-fidelity data platform, data scientists can manage the risk of introducing irrelevant data into analytical processes. Human-in-the-loop processes allow your analysts to collaboratively annotate results and apply their expertise and know-how.

    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.

Use Case 3: Identify Overlapping and Redundant Funding Requests

For: Federal Agency Budget Reviewers, Financial Analysts, Researchers

Automate researching and identification of redundant programs and 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. Agolo automates identification of redundant programs and initiatives, enabling agencies 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.