AMR/AGV fleet orchestration, goods-to-person robotics, computer-vision receiving, and edge-in-DC inference
Modern logistics runs on Physical AI: autonomous mobile robots, computer-vision receiving lines, AI-directed yard and dock operations. The challenge is not individual automation islands — it is the distributed-agent architecture that coordinates them under real-time constraints, with on-prem inference and no cloud dependency. Auralink's multi-agent resource-arbitration architecture — proven in coordinating distributed EV charging assets under grid-balancing constraints — provides directly transferable primitives for warehouse AMR/AGV fleet orchestration. We work to ISO 3691-4 (driverless industrial trucks) and IEC 62443 OT security for logistics edge deployments.
We design Physical AI architectures for logistics operations — from multi-agent AMR fleet controllers and computer-vision inspection pipelines to autonomous yard management — deployed on sovereign, on-prem infrastructure with no cloud dependency. Our distributed-agent primitives, proven in Auralink's EV charging network coordination, transfer directly to warehouse and port orchestration. Engagement model: Advise (architecture and vendor-neutral tech selection), Build (end-to-end system delivery to ISO 3691-4 and IEC 62443), Train (capability transfer to internal logistics engineering teams).
Based on common industry needs
Twelve weeks to a production-grade multi-agent system that serves as the software and control-plane complement to your cyber-physical stack — fleet intelligence, SCADA-adjacent orchestration, or autonomous operations — with the eval harness, the observability stack, and the SRE handoff your team needs to operate it
Four weeks to a strategy document, a business case, an ROI model, and a 12-month execution plan — scoped for industrial operators where OEE, safety-regime timelines, and physical-system procurement cycles are the real constraints, not just board optics
Twelve weeks to harden an edge or embedded AI pilot stuck before production — on constrained hardware, inside safety envelopes, under latency and reliability requirements the pilot was never designed to meet
The Full-Stack Physical AI layers most relevant to this sector.
A 30-minute call is enough to diagnose whether your AI initiative is stuck for industry-specific reasons — and what to do about it.