ClawX
We are building an MVP for a client need: an edge-based, self-optimizing supply chain control system that continuously monitors operations, makes real-time decisions, and recommends or triggers actions.
Core trick
Runs a local vision model on camera feeds to detect disruptions, then ranks corrective actions by simulating each in a digital twin.
Description
We are building an MVP for a client need: an edge-based, self-optimizing supply chain control system that continuously monitors operations, makes real-time decisions, and recommends or triggers actions. The system demonstrates a complete closed-loop pipeline:
Vision → Reasoning → Orchestration → Prediction → Action
Key capabilities:
- Detects disruptions in real time using vision AI
- Understands system behavior over time using AI
- Simulates multiple corrective actions using a digital twin
- Recommends the optimal action with measurable impact
- Operates as a fully local, always-on decision loop
- Controls processes through robotic execution