Software: AI-driven R&D and engineering
AI as the core driver, combined with modern software engineering discipline.
- AI applications: enterprise knowledge bases, document understanding, support and sales assistance
- LLM deployment: model selection, private deployment, evaluation and cost control
- Engineering discipline: code conventions, CI/CD, performance budgets, accessibility baseline
- Enterprise systems: project, CRM, inventory and productivity systems
- Data visualisation: scattered data turned into decision-ready dashboards
- Intelligent automation: efficiency tools for specific roles
Hardware: embodied intelligence
Deep research into multi-form robot algorithms, combined with AI perception and decision-making.
- Humanoid: bipedal balance, whole-body coordination, HRI
- Quadruped: gait control, rough terrain, payload transport
- Wheeled: navigation, indoor/outdoor mobility, logistics
- Drone + manipulator: flight control, aerial grasping, data capture
- Industrial arm: trajectory planning, vision guidance, fine manipulation
- Perception: multi-sensor fusion and real-time understanding
How they combine
Software defines the hardware; hardware validates the software. AI decides what a robot does and how, while real operating data feeds back into models and engineering. That loop is what "forged in real work" means technically.