Riverside Research Accelerates the Intelligence Cycle with RavenEye, an Agentic AI Framework
Author(s): Matthew May
Dynamic operating environments present challenges and stressors to analysts, but Riverside Research’s RavenEye Agentic AI framework allows users to respond proactively when mission critical situations change.
RavenEye provides a mission-focused interface that fuses relevant data and surfaces decision-ready insights. Agentic workflows continuously triage new signals and follow up on emerging threats in near-real time while keeping the human decisionmaker in control of operational actions.
RavenEye can help surface indicators of civil unrest, foreign influence, and rapidly evolving narratives by integrating authorized government and commercial sources while preserving provenance and auditability. RavenEye can then scale threat projections and map responses commensurate to the threat level using rapid, scalable deployment integrating with multiple domain environments and platforms.
As RavenEye AI Agents compiles and prioritizes data, it can generate potential follow-on tasks where the human decisionmaker approves, adjusts, or rejects recommended actions. Empowered with data, the user can decide how to act on the information presented, with suggested action plans at the ready.
“Near-peer competition is moving at machine speed. Multi-INT sensors and autonomous systems generate a rapidly changing picture with more data and cascading second-order effects than humans can track manually,” said Matt May, Director, Cognitive Intelligence Solutions at Riverside Research. “RavenEye reduces cognitive load through automated triage and synthesis, delivering defensible recommendations with end-to-end traceability.”
RavenEye Agentic AI framework fills the need of compiling, prioritizing, and synthetizing exponential amounts of information from a variety of sources. With RavenEye, intelligence data becomes actionable in real time.
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Featured Riverside Research Author(s)
Matthew May
Matthew May is the Director of Cognitive Intelligence Solutions at Riverside Research, leading advanced research and development in Artificial Intelligence, Multi-INT fusion, and autonomous systems for national security applications. He oversees initiatives spanning Agentic AI applied to the Intelligence Cycle, Modular Autonomous Payloads (MAP), and Dynamic Intelligence Orchestration (DIO), driving innovation across multi-domain data environments for DoD and Intelligence Community customers.
With a background that bridges defense innovation and applied technology, Matthew has led programs supporting multiple intelligence agencies—delivering prototypes that integrate AI agents, sensor data, and real-time situational awareness to accelerate decision-making at the tactical edge.
Prior to Riverside Research, Matthew founded and led multiple technology startups focused on open-source intelligence and analytical automation, including Applied Technology Solutions (ATS), whose software has been leveraged by government and non-profit organizations to combat human trafficking and transnational threats.
His work focuses on fusing human and machine intelligence to advance the future of autonomous decision support and Intelligence-as-a-Service.
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The above listed authors are current or former employees of Riverside Research. Authors affiliated with other institutions are listed on the full paper. It is the responsibility of the author to list material disclosures in each paper, where applicable – they are not listed here. This academic papers directory is published in accordance with federal guidance to make public and available academic research funded by the federal government.