Funding
Funding not disclosed
Founders
Product
Problem
Unmanned aerial platforms used in aerospace and defence often lack integrated, real-time perception and decision-making capabilities, limiting their effectiveness in surveillance, navigation, and mission-critical operations. Existing payload systems are typically siloed, vendor‑specific, and do not meet the stringent reliability and precision requirements of military applications.
Solution
PSYC delivers AI‑first autonomous and payload systems that embed advanced computer‑vision perception and decision‑support directly into unmanned platforms. Its modular payload architectures provide real‑time data processing and autonomous task management, enabling drones to conduct surveillance, navigation, and situational awareness without constant human oversight. The control software is designed for both fully autonomous and semi‑autonomous modes, adhering to military‑grade precision and reliability standards. PSYC’s solutions are built for universal integration, allowing seamless deployment across a wide range of open‑source and commercial drone ecosystems. Continuous R&D ensures the algorithms stay optimized for the evolving demands of aerospace and defence missions.
Target Audience
Primary customers are defence agencies, aerospace contractors, and industrial operators that require high‑performance, AI‑enabled payloads for unmanned aerial vehicles and related platforms.
Features
- AI‑driven perception stack that fuses computer‑vision inputs for real‑time object detection, tracking, and terrain mapping
- Autonomous decision‑support module that prioritizes mission objectives and adapts flight paths on‑the‑fly
- Modular payload framework compatible with multiple drone vendors and open‑source platforms
- Semi‑autonomous control software offering operator‑in‑the‑loop overrides and safety monitoring
- Military‑grade reliability engineering, including hardened hardware interfaces and fault‑tolerant software architecture
- Plug‑and‑play integration APIs for rapid deployment into existing unmanned systems