The control layer for multi-agent systems
We enable autonomous coordination between robot fleets through distributed intelligence. By eliminating centralized control requirements, we deliver scalable multi-agent systems that operate reliably in complex environments.
The Control Challenge in Multi-Agent Systems
Scalability Bottlenecks
Traditional multi-agent systems rely on centralized decision-making that creates computational bottlenecks. Every new agent, task, or environment requires extensive reconfiguration of the central control system, limiting growth from dozens to hundreds of robots across applications.
Single Points of Failure
Centralized architectures create critical failure points that can paralyze entire multi-agent fleets. Network disruptions, server failures, or communication breakdowns instantly disable hundreds of agents, making systems vulnerable and unreliable across all deployment environments.
Implementation Barriers
Implementing effective multi-agent coordination requires specialized expertise in distributed systems, extensive customization, and lengthy deployment cycles. Most organizations either avoid multi-agent deployments entirely or settle for inefficient centralized architectures.
Our Approach
Multi-Agent Reinforcement Learning
Our agents learn coordinated behavior through simulation-based training before deployment. This approach allows each robot to make intelligent local decisions while naturally coordinating with others in the fleet, eliminating the need for constant communication with a central controller.
Agent-Agnostic Control Abstraction
Our technology works with any robot platform through standardized interfaces. Whether you're running warehouse robots, manufacturing systems, or service robots in grid-based environments, our control layer integrates seamlessly without requiring changes to your existing hardware or software infrastructure.
Scalable Distributed Control
Adding more robots to your fleet doesn't slow down the system. Our distributed architecture ensures that performance scales efficiently as you grow from dozens to hundreds of agents, with each robot contributing to overall intelligence rather than creating bottlenecks.
Navigation Performance
38% Less Travel
Reduced energy costs and wear
75% Higher MTBF
Lower maintenance overhead
52% Fewer Turns
Smoother operations, less wear
74% Higher Throughput
Direct ROI on existing hardware
* measured against traditional centralized fleet management systems in 3PL warehouse environments