BACKGROUND. Quick-Fire provides an end-to-end capability that streamlines the lifecycle of exercise, experimentation, and operational observations—from their initial receipt to the delivery of finalized lessons by COEs and FMPs. This dashboard synthesizes high-impact inputs—including Quick-Fire observations, comprehensive Ecosystem metrics, doctrinal source material, and the entirety of CALL’s publication archive
LOCATION: The Center for Army Lessons Learned (CALL) transitioned Quick-Fire datasets from a SharePoint-based environment into the Army Vantage platform. This migration has enabled a suite of advanced capabilities that mirror and, in some cases, exceed those found in leading commercial data ecosystems.
https://go.army.mil/quick-fire
CURRENT CAPABILITIES (As of 3QTR/FY25). The system currently leverages Generative Artificial Intelligence (Gen AI) to aggregate diverse datasets including observational inputs, doctrinal references, and external documents delivering contextual responses to natural language queries. Advanced features include video search, citation-level document linking, and AI-powered summarization modeled after platforms such as YouTube chaptering and Zoom meeting recaps. Manual upload of field data and supporting documentation is supported, although mobile functionality remains temporarily offline pending restoration. Operational awareness is significantly enhanced through customizable dashboards that deliver real-time updates, outperforming traditional platforms like Power BI. Integrated data visualization tools graphically represent observational data and metadata across warfighting functions and DOTMLPF-P domains. AIP Logic Tools further extend Gen AI capabilities by enabling focused logic applications, including publication queries and automated generation of Executive Summaries and After-Action Reports. The system also integrates with external platforms such as the Mission Command Center of Excellence (MCCoE) and First Army Common Operational Picture (COP), with ongoing expansion of data and media sources to ensure sustained relevance and operational utility.
PHASE II (2QTR/FY26). Future capability development is centered on enhancing the Army’s operational agility and analytical dominance across all echelons. The initial focus is the integration of CTC Data Pillar assets including OC/T reports, AARs, and video media—into a unified data environment. This integration will enable RTUs to refine SOPs during home station training and empower WfF to more effectively close width against opposing forces. Delivering actionable information at the point of need is essential to increasing readiness. To support this, a mobile application is under development to extend analytical capabilities to handheld devices, ensuring accessibility for commanders and senior leaders operating in dynamic, contested environments. Machine learning will be employed to accelerate data analysis, compress decision timelines, and enable predictive analytics. These enhancements will identify operational patterns and recommend solutions, drawing from proven commercial models such as recommendation engines, fraud detection systems, and predictive maintenance frameworks. Concurrently, enterprise data connectivity is being expanded through scalable data lakes and robust APIs, ensuring seamless integration of disparate systems and reinforcing the Army’s digital ecosystem to meet the demands of multi-domain operations and future fight requirements.
PHASE III (4QTR/FY26). Quick-Fire is a critical capability within the broader HAVOC environment being developed by the CAC CDAO. It is designed to drive unity of effort and dismantle longstanding information silos across the force. As the lessons learned engine of HAVOC, Quick-Fire enables the structured capture, analysis, and dissemination of operational insights, ensuring that experiential knowledge is preserved and actively informs future decision-making. Quick-Fire operates within a system-of-systems architecture that includes complementary capabilities in data integration, visualization, machine learning, and mobile accessibility. Together, these systems facilitate the bi-directional flow of information across echelons and domains, allowing the latest observations, doctrinal updates, and analytical outputs to be surfaced and shared in real time. The HAVOC construct represents a deliberate shift toward a digitally unified operational ecosystem, where data-driven insights are continuously exchanged, refined, and applied to enhance readiness, adaptability, and strategic coherence across the Army enterprise. Quick-Fire is a key enabler within this ecosystem, transforming lessons learned into actionable insights that inform operations, doctrine, and strategic planning. By breaking down information silos and enabling real-time data flow, Quick-Fire enhances decision-making and institutional learning, supporting a more agile, adaptive, and strategically coherent Army.
CONCLUSION. The migration of Quick-Fire datasets into Army Vantage marks a pivotal milestone in the Army’s data modernization journey, unlocking a suite of capabilities that elevate both operational insight and strategic agility. This transformation enhances current functionality through AI-driven analytics, real-time dashboards, and integrated data visualization, while laying the groundwork for future innovations in mobile access, machine learning, and enterprise-wide data connectivity. As a cornerstone of the HAVOC environment led by CAC CDAO, Quick-Fire exemplifies the Army’s commitment to breaking down information barriers and achieving unity of effort across a system-of-systems architecture. By enabling the seamless exchange of lessons learned and operational data, this initiative ensures that the most relevant, actionable knowledge is continuously available to inform decisions, drive adaptation, and strengthen the Army’s collective readiness.
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