RAI Swarms provides a runtime operating system for Physical AI that keeps autonomous robots functional beyond initial demos. The platform offers persistent map memory, multi‑vendor coordination, automated supervision and recovery, and workflow drift correction, while abstracting integration with WMS, MES, and other enterprise systems to enable reliable, multi‑site deployments for robotics integrators and manufacturers.
Funding
Funding not disclosed
Founders
Product
Problem
Robotics pilots often succeed in initial demos but fail to scale beyond the first site because they lack a persistent runtime layer that can handle memory, coordination, supervision, and recovery across changing environments. This results in high engineer “babysitting” effort, performance drift, and integration debt that prevent deployments from reaching a second site.
Solution
RAI Swarms delivers a dedicated runtime operating system for Physical AI that bridges the gap between pilot and production. The platform continuously maintains up-to-date maps, mediates vendor conflicts, and enforces consistent workflows to prevent trust collapse. It provides built‑in supervision and automated recovery, reducing the need for specialist on‑site intervention. By abstracting integration points with WMS, MES, and other enterprise systems, the runtime eliminates repetitive integration effort at each new floor. Continuous performance monitoring and drift correction keep robots operating near pilot efficiency over time, enabling scalable, multi‑site deployments.
Target Audience
Primary customers are robotics system integrators, industrial automation teams, and enterprise manufacturers that need reliable, scalable deployments of autonomous mobile robots across multiple facilities.
Features
- Persistent memory layer that updates operational maps in real time to avoid static‑map decay
- Coordination engine that resolves multi‑vendor conflicts and synchronizes mixed‑fleet actions
- Supervision and automated recovery modules that detect pauses and restore operation without manual intervention
- Workflow drift detection with automatic correction to maintain performance within pilot specifications
- Integration abstraction layer for WMS, MES, scheduling, and identity systems, reducing per‑site integration effort
- Deployment feedback loop that captures runtime metrics and feeds them back to engineers for continuous improvement