Collection is compounding, creating an opportunity to dramatically expand analyst reach. You must move beyond predefined target decks, BE facilities, and one image at a time analysis to exploit broader areas, more frequent collects, and a growing sensor base.
More sensors. More frequent collection. More imagery arriving from across the battlespace. Proliferating drones and next generation uncrewed ISR systems make scalable exploitation a requirement, not an optimization.
Analytic teams are often structured around RFIs, KIQs, and PIRs tied to known locations and established targets. That model works well for persistent monitoring, but those locations represent only a fraction of the broader AOR. Peer adversaries are increasingly adept at masking TTPs during competition, rapidly adapting during conflict, and managing the RF and other Multi-INT signatures we have traditionally relied upon. As those cues become harder to detect or deliberately obscured, dynamic operations demand the ability to look beyond predefined sites, staging areas, and facility boundaries. The most consequential activity may be occurring outside the places already under watch.
Sources: [1] VADM Whitworth, 2025 GEOINT Symposium keynote, NGA; [2] NGA prepares to use Generative AI for mission support
Extract Decision Advantage From Every Pixel.
RAIC enables broader search, persistent awareness, and rapid imagery triage across diverse mission threads, helping analysts move faster from collection to understanding.
"Find" means discovery, not just confirming threats against known locations and signatures. RAIC uses like object search and clustering to reduce location bias and help analysts discover unknown objects while searching for known ones.
How it worksBaseline a site, aggregate change over months, and surface anomalous activity using the secondary objects no detector was ever built for. Extend the analysis to support forensic review, identify emerging indicators, and reveal the logistics, staging, and support chains behind operational activity.
How it worksTriage scenes at scale to decide where expensive ATR should run, turning an underused model catalog into targeted, affordable coverage.
How it worksHyper-scaled broad area search lets an analyst interrogate an entire theater from as little as one example, then validate results in like-object clusters rather than one frame at a time.
Move from semantic clusters to geographic contextWhen SIGINT detects a potential threat object, GEOINT is often tasked to confirm at the center point of the uncertainty ellipsoid. That can create a false sense of precision: a candidate object at the center may appear to validate the attribution even though the true source could exist anywhere within the broader area. Peer adversaries are RF-smart, and as they manage, mask, or manipulate signatures, center-point cueing becomes increasingly unreliable.
RAIC treats the full ellipsoid as the search space rather than a single point. Teams can collect and search the area holistically, evaluate candidate objects statistically, refine attribution as evidence accumulates, and maintain awareness of viable candidates through continuous monitoring and traditional collection.
The same indexed representation supports every phase, so custody does not break when the mission moves between them.
Expanded AOR search exposes adversary activity operating outside known garrison and staging locations.
Search results carry visual timeline and location context, so an analyst confirms the target rather than inferring it.
Shallow model monitoring holds custody; broad area search reacquires the object when its appearance changes.
Search and clustering support combat assessment and change detection immediately after action.
The primary object is often only part of the picture. Movement, staging, support activity, surrounding change, and other secondary indicators can reveal intent before the object of interest changes state.
Traditional ATR programs continue to add models, but the growing model library is already difficult to task, manage, and deduplicate and still represents only a fraction of the objects analysts need across different AORs, sensors, and conditions.
Conventional computer vision expands by adding specialized models, each with its own inference workload and compute cost. RAIC shifts broad search, clustering, categorization, and monitoring into a reusable embedding space, allowing teams to reserve GPU-intensive models for the tasks where their precision and automation provide the greatest advantage.
Camouflage, concealment, markings, and other visual changes can degrade detectors trained on a fixed appearance. RAIC uses a feature-rich embedding representation that captures deeper visual characteristics beyond raw pixel similarity, making detection and search less rigid as objects change and adversary TTPs evolve.
RAIC enables teams to rapidly stand up embedding based filters and lightweight detectors as collection requirements evolve. New objects, indicators, and areas of interest can be operationalized quickly, refined as more data arrives, and retired when no longer needed without committing to a full labeling, training, and deployment cycle.
RAIC ships as containerized services designed to integrate with existing DevSecOps pipelines and deploy across commercial cloud, on premises, classified, and fully air gapped environments. A hub and spoke deployment model supports data isolation across enclaves and mission partners while maintaining a consistent application baseline, allowing teams to preserve existing security boundaries, release processes, and authorization controls.
RAIC has an active Certification to Field for a Top Secret environment. That authorization, along with the supporting security documentation and deployment history, can be reused as a starting point for your environment, giving security teams a proven foundation rather than beginning the accreditation process from scratch.
RAIC maintains hardened and continuously scanned container images aligned with Iron Bank requirements. Standardized artifacts, vulnerability management, and repeatable container builds help reduce friction during security assessment and accreditation.
RAIC brings experienced security and platform support to work with AOs, ISSOs, ISSMs, and engineering teams throughout deployment. We support security documentation, control implementation, technical findings, remediation, and ongoing authorization requirements as the environment evolves.
Starting from a hand-drawn sketch, RAIC found its first positive match in less than two minutes. It located the 150-pixel object within 18 trillion pixels of imagery, detected it 13 separate times, tracked it toward its launch site, and identified dark aircraft operating nearby.
Temporary and illicit airstrips can appear quickly, operate briefly, and vary significantly in visual signature, limiting the effectiveness of fixed detection approaches. RAIC teams used broad area imagery search to identify more than 50 previously unknown airstrips across 2020 and 2021.
Fast moving airborne objects can produce distinct band separated signatures in multispectral satellite imagery as the sensor captures the scene over time. RAIC exploited this motion induced parallax to identify Blue Angels aircraft in 3 meter imagery near NAS Pensacola, demonstrating detection based on sensor phenomenology rather than a conventional airframe signature.
Using an existing vessel category as the starting point, RAIC reacquired the Russian naval formation in open source Sentinel 2 imagery over the Mediterranean during 3 to 8 May. Visual similarity across vessel length, appearance, and wake enabled the formation to be associated across separate collections and changes in relative position to be identified.
The IC and key stakeholders within Trident Spectre should make every effort to invest and continue to evaluate this specific tech, as it has the potential to make amplifying effects on the battlefield right now if used correctly.NSW G8 SRT SIGINT and OPS/INTEL Analyst — RAIC rated highest capability at Trident Spectre ’23 (88/100), bringing no models and no data, only a laptop
Bring representative imagery, an existing workflow, or a defined problem set. We will explore where RAIC can extend exploitation, surface new indicators, and unlock additional capability from the collection and infrastructure already in place.
