Warfare has evolved from a contest of firepower to a contest of computational throughput. Modern sensor networks—satellites, drones, signals intelligence—generate petabytes of data every hour. This volume has surpassed the human capacity to process, creating a paradox: commanders are drowning in data yet starving for insight.
The Latency Trap: In a hypersonic era, the OODA loop (Observe, Orient, Decide, Act) must execute in seconds. Traditional analysis pipelines, which rely on manual interpretation of video feeds and signal intercepts, introduce fatal latency. By the time intelligence reaches the edge, the tactical reality has already shifted.
The Sovereignty Dilemma: As AI becomes critical, nations face a strategic choice. Relying on public cloud infrastructure or foreign-controlled models introduces unacceptable supply chain risks and data residency vulnerabilities. True national security requires indigenous AI capabilities that operate entirely within a sovereign, air-gapped perimeter.
Legacy Inertia: Defense enterprises are burdened by decades of legacy code (Ada, COBOL) and fragmented IT systems. These "black boxes" cannot easily integrate modern machine learning, creating a widening gap between commercial innovation and military capability.
When analysts must manually review thousands of hours of drone footage, critical threats are missed. The inability to scale attention results in strategic blindness despite ubiquitous surveillance.
Commanders at the strategic level lack real-time visibility into the tactical edge. Orders are issued based on stale information, leading to misaligned objectives and wasted resources.
Static cyber defenses cannot keep pace with AI-driven attacks. Without autonomous patching and adaptive threat hunting, critical infrastructure remains exposed to zero-day exploits.
These challenges require a fundamental architectural shift. We must move from human-in-the-loop systems to human-on-the-loop architectures, where AI handles the high-velocity data processing and humans focus on high-consequence decision making.
From Data to Decision: SVECTOR deploys multimodal AI agents that ingest video, audio, text, and signal data simultaneously. These agents do not just "see" objects; they understand context, intent, and patterns of life. This allows for automated cross-cueing, where a signal intercept can automatically trigger a satellite tasking without human intervention.
Reasoning with Integrity: In high-stakes environments, "black box" AI is unacceptable. Our systems utilize Chain-of-Thought (CoT) reasoning to provide verifiable audit trails. Every recommendation made by the AI—whether a course of action or a target identification—comes with a step-by-step logical explanation that commanders can validate.
The Edge Cloud Continuum: We bridge the gap between centralized command centers and the tactical edge. Our lightweight inference models run on SWaP-C (Size, Weight, Power, and Cost) constrained hardware, allowing drones and vehicles to process data locally and transmit only critical insights, preserving bandwidth in contested environments.
Synthesize data from disparate sensors into a single Common Operating Picture (COP). Detect anomalies across the electromagnetic spectrum and visual domains simultaneously to identify camouflaged or low-signature threats.
Move from scheduled maintenance to condition-based maintenance. AI analyzes telemetry from engines and airframes to predict component failures before they occur, optimizing fleet availability and supply chain positioning.
Deploy autonomous agents that patrol network perimeters, identifying and neutralizing intrusions at machine speed. Automatically generate and deploy patches for vulnerabilities in legacy software systems.
Simulate millions of potential conflict scenarios to stress-test operational plans. Use reinforcement learning to identify novel strategies and counter-strategies that human planners might overlook.
Intelligence agencies possess vast archives of historical video and signal data that remain largely unexploited. SVECTOR's AI can ingest decades of archival footage to establish "patterns of life" baselines. By understanding what "normal" looked like five years ago, the system can detect subtle deviations in the present that indicate emerging threats.
This capability transforms passive archives into active intelligence assets. Analysts can query the database with natural language—"Show me all instances of vehicle type X arriving at facility Y between 2020 and 2023"—turning weeks of manual search into seconds of compute.
Modernizing legacy defense systems is a national security imperative. Our specialized coding models, deployed air-gapped, assist developers in refactoring mission-critical Ada and C++ codebases. The AI understands the strict safety-critical standards (like MISRA) required for weapons systems and avionics.
This accelerates the software development lifecycle (SDLC) within classified environments. Developers can generate unit tests, document legacy functions, and identify buffer overflows automatically, ensuring that modernization does not introduce new vulnerabilities.
The electromagnetic spectrum is a contested domain. Traditional EW systems rely on pre-programmed libraries of known threats. SVECTOR's cognitive EW agents use machine learning to characterize unknown radar and signal waveforms in real-time.
The system can dynamically synthesize jamming profiles to counter novel threats on the fly. This adaptability is essential for surviving in anti-access/area-denial (A2/AD) environments where adversaries constantly evolve their signal signatures.
The future of combat is not unmanned, but manned-unmanned teaming (MUM-T). Our AI acts as a digital wingman, managing the cognitive load for pilots and vehicle crews. It monitors system health, navigates waypoints, and prioritizes targets, allowing the human operator to focus on tactical execution.
Natural language interfaces allow operators to command swarms of autonomous systems verbally. "Scout the ridge line and report activity" is translated by the AI into specific flight paths and sensor tasks for multiple drones.
Total Data Sovereignty: SVECTOR provides a complete, self-contained AI ecosystem that runs entirely on your hardware. From model training to inference, no data ever touches the public internet. This is not a hybrid cloud; it is a fortress.
Hardware Agnostic Deployment: Our stack is optimized to run on diverse compute substrates, from high-performance datacenter GPUs to ruggedized edge processors. We bring the intelligence to the data, wherever it resides.
We deliver model weights to your secure facility physically if necessary. Once installed, the system operates in total isolation. There are no "call homes," no telemetry beacons, and no external dependencies.
Designed for TS/SCI environments. Granular role-based access control (RBAC) ensures that information is compartmentalized. The AI respects data classification levels, ensuring that insights are only shared with authorized personnel.
The system learns from your data in your environment. As your analysts correct the AI or provide new examples, the models update locally. Your capability improves daily without ever sharing that learning with the outside world.
All model components are vetted and signed. We provide a Software Bill of Materials (SBOM) for every layer of the stack, ensuring that no malicious code or compromised dependencies enter your secure perimeter.
