Intelligence, surveillance, and reconnaissance

Explainable AI for ISR operations

Seekr is a sovereign, expert-trained, edge-deployable AI layer that turns any ISR platform into an active analytical node, equipping decision makers and operators with advanced abilities to observe, orient, decide and act at the speed of mission requirements, in any contested environment.

Request a demo

Sovereign AI reduces cognitive load for decision makers and operators who have no room for error

Warfighters are increasingly challenged to accelerate operational tempo ahead of adversaries, but cannot process multi-sensor data at mission speed while simultaneously reducing risk.

Degraded environments & conditions

Darkness, foliage, glare and weather conditions require solutions beyond electro-optics (EO), but attempting to reason across radar, SAR, RF and thermal signatures adds to cognitive load and impedes decision-making.

Data overload and analyst fatigue

Lengthy missions generate large volumes of data, requiring operators and decision-makers to continuously monitor and scan, leading to fatigue-driven mistakes, missed detection, and incorrect classification of threats.

Remote and denied connectivity

Terrain and the ‘Tyranny of Distance’ add time to decision-making, effectively impeding or prohibiting communications between operators and leaders, while increasing mission risk.

Concealment and evasion

Adversaries understand that it’s difficult to synthesize data into meaning across Radar, RF and EO signatures and exploit these gaps to conceal operations and evade detection.

Explainable AI for ISR operations

Trusted, sovereign AI. In any environment, in every domain.

Seekr extract meaning from all ISR data with AI Reasoning, from the sensor edge to the ops center.

Onboard edge and ground exploitation

One full stack from the sensor edge to the ops center: edge tip-cue and real-time fusion, plus all-source PED to provide a single, fused COP.

Sovereign AI compute

On-platform and ground-based compute increases autonomy, providing full mission capability in denied or degraded environments, with no data leakage.

Purpose-built, mission models

Expert-trained, sensor and task-specific models deliver higher accuracy and reduced hallucinations, meeting SWaP requirements for the ISR mission, not a generic framework.

AI governance

Auditable, explainable decisions with the full AI decision chain logged; US Systems Autonomy, EU AI Act and GDPR-aligned, with guardrails engineered in from the ground up.

ISR capabilities for defense and security

Persistent target tracking and ID

Multi-modal ATR. Tracks held across handoffs and coverage gaps.

Digital fingerprinting and attribution

Evidence-grade identity through spoofing and reflagging.

Pattern-of-life and predictive threat

Behavioral baselines flag anomalies before activity manifests.

Shadow fleet and sanctions tracking

Identify AIS gaps, ship-to-ship transfers, and anomalous signatures.

Autonomous navigation and self-tasking

Navigation decisioning and onboard re-tasking for unmanned platforms.

FAQs

What is an ISR AI augmentation layer?

An ISR AI augmentation layer is software that adds detection, classification, and reasoning capability to a collection platform without replacing its sensors or airframe. Seekr’s layer runs both on the platform and in the operations center, so exploitation happens at the point of collection rather than after downlink. The same capability set runs on airborne, surface, and subsurface platforms, manned and unmanned, and configures to the sensor mix on each.

Can AI-enabled ISR operate in denied or degraded (DDIL) environments?

Yes. Seekr runs the full analytic stack onboard, so detection, fusion, triage, and cross-cueing continue with no satellite or ground link. When a link is available, the platform sends synthesized intelligence and its supporting evidence rather than raw sensor data, which keeps the mission running over intermittent or narrow bandwidth. Mission profiles and target signatures are pushed forward to the platform before and during the sortie where connectivity allows.

How does AI identify a target when one sensor is not sufficient?

Identification comes from correlation across sensors. Seekr agents reason over radar, EO/IR, SAR, hyperspectral, LiDAR, WAMI, RF, AIS, and multilingual audio, then combine those signatures with reporting and archive data to reach a single identification. A UHF emission paired with an AIS track can fit a commercial trawler or a naval combatant; acoustic profile, SAR hull shape, and prior reporting resolve which. Sensors cross-cue in place, with no link in the loop.

How can an analyst verify an AI-generated conclusion?

Each Seekr output carries an evidence package that attributes the conclusion to its inputs: the specific sentences in reporting, the regions of the SAR or optical frame, the video frames and timestamps. An analyst can check those inputs before acting. Attribution also localizes faults, separating a model problem from a data problem from a context problem, and shows which source produced a conclusion if that source was spoofed or poisoned. In agentic workflows, where each step reasons on the output of the last, it shows whether a bad input is being carried forward.

Who controls the data and models in a sovereign deployment?

The customer does. Seekr deploys on customer-owned or in-region infrastructure. Training and model refinement execute inside the customer-controlled environment, with no cross-contamination of data, models, or customers, and the customer retains ownership of data, models, and agents. The architecture is Kubernetes-based and portable across datacenters, clouds, and models, so a deployment is not tied to a single vendor. Governance controls and decision logging are part of the system design.